librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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RichardErkhov/Digest0703_-_test_llm-gguf	RichardErkhov	"2025-04-05T01:18:43Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-04-05T00:05:47Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) test_llm - GGUF - Model creator: https://huggingface.co/Digest0703/ - Original model: https://huggingface.co/Digest0703/test_llm/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [test_llm.Q2_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q2_K.gguf) \| Q2_K \| 1.27GB \| \| [test_llm.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ3_XS.gguf) \| IQ3_XS \| 1.38GB \| \| [test_llm.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ3_S.gguf) \| IQ3_S \| 1.44GB \| \| [test_llm.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K_S.gguf) \| Q3_K_S \| 1.44GB \| \| [test_llm.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ3_M.gguf) \| IQ3_M \| 1.49GB \| \| [test_llm.Q3_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K.gguf) \| Q3_K \| 1.57GB \| \| [test_llm.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K_M.gguf) \| Q3_K_M \| 1.57GB \| \| [test_llm.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K_L.gguf) \| Q3_K_L \| 1.69GB \| \| [test_llm.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ4_XS.gguf) \| IQ4_XS \| 1.71GB \| \| [test_llm.Q4_0.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_0.gguf) \| Q4_0 \| 1.79GB \| \| [test_llm.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ4_NL.gguf) \| IQ4_NL \| 1.79GB \| \| [test_llm.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_K_S.gguf) \| Q4_K_S \| 1.8GB \| \| [test_llm.Q4_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_K.gguf) \| Q4_K \| 1.88GB \| \| [test_llm.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_K_M.gguf) \| Q4_K_M \| 1.88GB \| \| [test_llm.Q4_1.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_1.gguf) \| Q4_1 \| 1.95GB \| \| [test_llm.Q5_0.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_0.gguf) \| Q5_0 \| 2.11GB \| \| [test_llm.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_K_S.gguf) \| Q5_K_S \| 2.11GB \| \| [test_llm.Q5_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_K.gguf) \| Q5_K \| 2.16GB \| \| [test_llm.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_K_M.gguf) \| Q5_K_M \| 2.16GB \| \| [test_llm.Q5_1.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_1.gguf) \| Q5_1 \| 2.28GB \| \| [test_llm.Q6_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q6_K.gguf) \| Q6_K \| 2.46GB \| \| [test_llm.Q8_0.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q8_0.gguf) \| Q8_0 \| 3.19GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
pfunk/CartPole-v1-CP_DQPN_x5-seed3	pfunk	"2023-03-20T02:59:44Z"	0	0	cleanrl	[ "cleanrl", "tensorboard", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-03-20T02:59:41Z"	--- tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQPN_freq Agent Playing CartPole-v1 This is a trained model of a DQPN_freq agent playing CartPole-v1. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[CP_DQPN_x5]" python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/dqpn_freq.py curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/poetry.lock poetry install --all-extras python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 100 --seed 3 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq.py', 'batch_size': 256, 'buffer_size': 300000, 'capture_video': True, 'cuda': True, 'end_e': 0.1, 'env_id': 'CartPole-v1', 'exp_name': 'CP_DQPN_x5', 'exploration_fraction': 0.2, 'gamma': 1.0, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 1000, 'policy_network_frequency': 100, 'policy_tau': 1.0, 'save_model': True, 'seed': 3, 'start_e': 1.0, 'target_network_frequency': 20, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 500000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
andradejunior/ppo-LunarLander-v2	andradejunior	"2022-12-20T02:34:03Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2022-12-20T02:33:35Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 244.51 +/- 39.99 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mirella-guenther/distil-whisper-distil-large-v3-torgo-2-epochs	mirella-guenther	"2024-06-05T00:23:47Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-06-05T00:23:43Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Kondara/gemma-3-12b-it-Q4_K_M-GGUF	Kondara	"2025-03-13T04:05:18Z"	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:google/gemma-3-12b-it", "base_model:quantized:google/gemma-3-12b-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	"2025-03-13T04:04:41Z"	--- base_model: google/gemma-3-12b-it library_name: transformers license: gemma pipeline_tag: image-text-to-text tags: - llama-cpp - gguf-my-repo extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # Kondara/gemma-3-12b-it-Q4_K_M-GGUF This model was converted to GGUF format from [`google/gemma-3-12b-it`](https://huggingface.co/google/gemma-3-12b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/google/gemma-3-12b-it) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -c 2048 ```
prushton/dreambooth-myra-3000	prushton	"2023-12-04T01:22:16Z"	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2023-12-03T21:41:37Z"	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of myra tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - prushton/dreambooth-myra-3000 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of myra using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
TheBloke/MelloGPT-AWQ	TheBloke	"2023-12-16T15:02:11Z"	11	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:nbertagnolli/counsel-chat", "base_model:steve-cse/MelloGPT", "base_model:quantized:steve-cse/MelloGPT", "license:mit", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	"2023-12-16T14:44:18Z"	--- base_model: steve-cse/MelloGPT datasets: - nbertagnolli/counsel-chat inference: false license: mit model_creator: Steve Boby George model_name: MelloGPT model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # MelloGPT - AWQ - Model creator: [Steve Boby George](https://huggingface.co/steve-cse) - Original model: [MelloGPT](https://huggingface.co/steve-cse/MelloGPT) <!-- description start --> ## Description This repo contains AWQ model files for [Steve Boby George's MelloGPT](https://huggingface.co/steve-cse/MelloGPT). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MelloGPT-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MelloGPT-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MelloGPT-GGUF) * [Steve Boby George's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/steve-cse/MelloGPT) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/MelloGPT-AWQ/tree/main) \| 4 \| 128 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.15 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/MelloGPT-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `MelloGPT-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/MelloGPT-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''{prompt} ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/MelloGPT-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/MelloGPT-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/MelloGPT-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Steve Boby George's MelloGPT A fine tuned version of Mistral-7B-v0.1 on Counsel Chat dataset for mental health conversations. In an era where mental health support is of paramount importance, A large language model fine-tuned on mental health counseling conversations stands as a pioneering solution. This approach aims to elevate natural language understanding and generation within the realm of mental health support. Leveraging a diverse dataset of anonymized counseling sessions, the model has been trained to recognize and respond to a wide range of mental health concerns, including anxiety, depression, stress, and more. The fine-tuning process incorporates ethical considerations, privacy concerns, and sensitivity to the nuances of mental health conversations. The resulting model will demonstrate an intricate understanding of mental health issues and provide empathetic and supportive responses, offering a valuable tool for individuals seeking guidance, mental health professionals, and the broader healthcare community.
stablediffusionapi/godhorror	stablediffusionapi	"2024-05-24T10:09:06Z"	29	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2024-05-24T10:06:52Z"	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # GodHorror API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/6156582341716545162.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "godhorror" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/godhorror) Model link: [View model](https://modelslab.com/models/godhorror) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "godhorror", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
timmyAlvice/house_md_transfer_learning	timmyAlvice	"2025-03-23T09:42:25Z"	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2025-03-11T09:00:32Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
John6666/prefect-pony-xl-v3-sdxl	John6666	"2024-09-11T03:32:21Z"	388	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "animagine", "pony", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2024-09-11T03:22:17Z"	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - animagine - pony --- Original model is [here](https://civitai.com/models/439889/prefect-pony-xl?modelVersionId=828380). This model created by [Goofy_Ai](https://civitai.com/user/Goofy_Ai).
MrinmoySaikia/t5-small-finetuned-wikisql	MrinmoySaikia	"2024-04-29T05:59:19Z"	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-04-28T21:45:00Z"	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-wikisql results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.30.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3
RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf	RichardErkhov	"2024-04-17T10:23:47Z"	164	0	null	[ "gguf", "arxiv:2012.05628", "endpoints_compatible", "region:us" ]	null	"2024-04-17T10:20:46Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt2-small-italian-embeddings - GGUF - Model creator: https://huggingface.co/GroNLP/ - Original model: https://huggingface.co/GroNLP/gpt2-small-italian-embeddings/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [gpt2-small-italian-embeddings.Q2_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q2_K.gguf) \| Q2_K \| 0.06GB \| \| [gpt2-small-italian-embeddings.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ3_XS.gguf) \| IQ3_XS \| 0.06GB \| \| [gpt2-small-italian-embeddings.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ3_S.gguf) \| IQ3_S \| 0.06GB \| \| [gpt2-small-italian-embeddings.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K_S.gguf) \| Q3_K_S \| 0.06GB \| \| [gpt2-small-italian-embeddings.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ3_M.gguf) \| IQ3_M \| 0.07GB \| \| [gpt2-small-italian-embeddings.Q3_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K.gguf) \| Q3_K \| 0.07GB \| \| [gpt2-small-italian-embeddings.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K_M.gguf) \| Q3_K_M \| 0.07GB \| \| [gpt2-small-italian-embeddings.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K_L.gguf) \| Q3_K_L \| 0.07GB \| \| [gpt2-small-italian-embeddings.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ4_XS.gguf) \| IQ4_XS \| 0.07GB \| \| [gpt2-small-italian-embeddings.Q4_0.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_0.gguf) \| Q4_0 \| 0.08GB \| \| [gpt2-small-italian-embeddings.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ4_NL.gguf) \| IQ4_NL \| 0.08GB \| \| [gpt2-small-italian-embeddings.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_K_S.gguf) \| Q4_K_S \| 0.08GB \| \| [gpt2-small-italian-embeddings.Q4_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_K.gguf) \| Q4_K \| 0.08GB \| \| [gpt2-small-italian-embeddings.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_K_M.gguf) \| Q4_K_M \| 0.08GB \| \| [gpt2-small-italian-embeddings.Q4_1.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_1.gguf) \| Q4_1 \| 0.08GB \| \| [gpt2-small-italian-embeddings.Q5_0.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_0.gguf) \| Q5_0 \| 0.09GB \| \| [gpt2-small-italian-embeddings.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_K_S.gguf) \| Q5_K_S \| 0.09GB \| \| [gpt2-small-italian-embeddings.Q5_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_K.gguf) \| Q5_K \| 0.09GB \| \| [gpt2-small-italian-embeddings.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_K_M.gguf) \| Q5_K_M \| 0.09GB \| \| [gpt2-small-italian-embeddings.Q5_1.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_1.gguf) \| Q5_1 \| 0.1GB \| \| [gpt2-small-italian-embeddings.Q6_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q6_K.gguf) \| Q6_K \| 0.1GB \| Original model description: --- language: it tags: - adaption - recycled - gpt2-small pipeline_tag: text-generation --- # GPT-2 recycled for Italian (small, adapted lexical embeddings) [Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) • [Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475) ## Model description This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingface.co/gpt2)) model. The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary. For details, check out our paper on [arXiv](https://arxiv.org/abs/2012.05628) and the code on [Github](https://github.com/wietsedv/gpt2-recycle). ## Related models ### Dutch - [`gpt2-small-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-small-dutch-embeddings): Small model size with only retrained lexical embeddings. - [`gpt2-small-dutch`](https://huggingface.co/GroNLP/gpt2-small-dutch): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended) - [`gpt2-medium-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-dutch-embeddings): Medium model size with only retrained lexical embeddings. ### Italian - [`gpt2-small-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-small-italian-embeddings): Small model size with only retrained lexical embeddings. - [`gpt2-small-italian`](https://huggingface.co/GroNLP/gpt2-small-italian): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (Recommended) - [`gpt2-medium-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings): Medium model size with only retrained lexical embeddings. ## How to use ```python from transformers import pipeline pipe = pipeline("text-generation", model="GroNLP/gpt2-small-italian-embeddings") ``` ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-small-italian-embeddings") model = AutoModel.from_pretrained("GroNLP/gpt2-small-italian-embeddings") # PyTorch model = TFAutoModel.from_pretrained("GroNLP/gpt2-small-italian-embeddings") # Tensorflow ``` ## BibTeX entry ```bibtex @misc{devries2020good, title={As good as new. How to successfully recycle English GPT-2 to make models for other languages}, author={Wietse de Vries and Malvina Nissim}, year={2020}, eprint={2012.05628}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
wyl88/rdt_5000	wyl88	"2025-03-08T14:01:37Z"	1	0	null	[ "pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	"2025-03-06T13:39:20Z"	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://huggingface.co/robotics-diffusion-transformer/rdt-1b - Docs: [More Information Needed]
cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1	cleanrl	"2023-03-02T23:00:22Z"	0	0	cleanrl	[ "cleanrl", "tensorboard", "Asterix-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-03-02T23:00:21Z"	--- tags: - Asterix-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Asterix-v5 type: Asterix-v5 metrics: - type: mean_reward value: 338180.00 +/- 103580.12 name: mean_reward verified: false --- # (CleanRL) PPO Agent Playing Asterix-v5 This is a trained model of a PPO agent playing Asterix-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_machado_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_machado_atari_wrapper --env-id Asterix-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1/raw/main/cleanba_ppo_envpool_machado_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1/raw/main/poetry.lock poetry install --all-extras python cleanba_ppo_envpool_machado_atari_wrapper.py --distributed --learner-device-ids 1 2 3 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 1 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'actor_devices': ['gpu:0'], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 15360, 'capture_video': False, 'clip_coef': 0.1, 'concurrency': True, 'cuda': True, 'distributed': True, 'ent_coef': 0.01, 'env_id': 'Asterix-v5', 'exp_name': 'cleanba_ppo_envpool_machado_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'global_learner_decices': ['gpu:1', 'gpu:2', 'gpu:3', 'gpu:5', 'gpu:6', 'gpu:7'], 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3], 'learner_devices': ['gpu:1', 'gpu:2', 'gpu:3'], 'learning_rate': 0.00025, 'local_batch_size': 7680, 'local_minibatch_size': 1920, 'local_num_envs': 60, 'local_rank': 0, 'max_grad_norm': 0.5, 'minibatch_size': 3840, 'norm_adv': True, 'num_envs': 120, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 3255, 'profile': False, 'save_model': True, 'seed': 1, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanba', 'world_size': 2} ```
tensorblock/my_Llama-3.2-3B-Instruct-GGUF	tensorblock	"2025-01-01T17:43:21Z"	27	0	transformers	[ "transformers", "gguf", "TensorBlock", "GGUF", "base_model:pavan01729/my_Llama-3.2-3B-Instruct", "base_model:quantized:pavan01729/my_Llama-3.2-3B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-01-01T17:28:26Z"	--- library_name: transformers tags: - TensorBlock - GGUF base_model: pavan01729/my_Llama-3.2-3B-Instruct --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## pavan01729/my_Llama-3.2-3B-Instruct - GGUF This repo contains GGUF format model files for [pavan01729/my_Llama-3.2-3B-Instruct](https://huggingface.co/pavan01729/my_Llama-3.2-3B-Instruct). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> Cutting Knowledge Date: December 2023 Today Date: 02 Jan 2025 {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [my_Llama-3.2-3B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q2_K.gguf) \| Q2_K \| 1.364 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [my_Llama-3.2-3B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q3_K_S.gguf) \| Q3_K_S \| 1.543 GB \| very small, high quality loss \| \| [my_Llama-3.2-3B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q3_K_M.gguf) \| Q3_K_M \| 1.687 GB \| very small, high quality loss \| \| [my_Llama-3.2-3B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q3_K_L.gguf) \| Q3_K_L \| 1.815 GB \| small, substantial quality loss \| \| [my_Llama-3.2-3B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q4_0.gguf) \| Q4_0 \| 1.917 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [my_Llama-3.2-3B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q4_K_S.gguf) \| Q4_K_S \| 1.928 GB \| small, greater quality loss \| \| [my_Llama-3.2-3B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q4_K_M.gguf) \| Q4_K_M \| 2.019 GB \| medium, balanced quality - recommended \| \| [my_Llama-3.2-3B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q5_0.gguf) \| Q5_0 \| 2.270 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [my_Llama-3.2-3B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q5_K_S.gguf) \| Q5_K_S \| 2.270 GB \| large, low quality loss - recommended \| \| [my_Llama-3.2-3B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q5_K_M.gguf) \| Q5_K_M \| 2.322 GB \| large, very low quality loss - recommended \| \| [my_Llama-3.2-3B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q6_K.gguf) \| Q6_K \| 2.644 GB \| very large, extremely low quality loss \| \| [my_Llama-3.2-3B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q8_0.gguf) \| Q8_0 \| 3.422 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/my_Llama-3.2-3B-Instruct-GGUF --include "my_Llama-3.2-3B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/my_Llama-3.2-3B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
Themira/smollm-mt5-en-si	Themira	"2025-03-25T14:42:57Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceTB/SmolLM2-135M-Instruct", "base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct", "region:us" ]	null	"2025-03-14T18:12:11Z"	--- base_model: HuggingFaceTB/SmolLM2-135M-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero	OpenDILabCommunity	"2024-02-01T07:03:04Z"	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "TicTacToe-play-with-bot", "en", "arxiv:2310.08348", "license:apache-2.0", "model-index", "region:us" ]	reinforcement-learning	"2024-02-01T07:02:57Z"	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - TicTacToe-play-with-bot benchmark_name: OpenAI/Gym/Atari task_name: TicTacToe-play-with-bot pipeline_tag: reinforcement-learning model-index: - name: GumbelMuZero results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: TicTacToe-play-with-bot type: TicTacToe-play-with-bot metrics: - type: mean_reward value: 0.7 +/- 0.46 name: mean_reward --- # Play TicTacToe-play-with-bot with GumbelMuZero Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This implementation applies GumbelMuZero to the OpenAI/Gym/Atari TicTacToe-play-with-bot environment using [LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine). LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348). ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env,video] pip3 install LightZero ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from lzero.agent import GumbelMuZeroAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = GumbelMuZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-GumbelMuZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from lzero.agent import GumbelMuZeroAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero") # Instantiate the agent agent = GumbelMuZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-GumbelMuZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from lzero.agent import GumbelMuZeroAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = GumbelMuZeroAgent(env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-GumbelMuZero") # Train the agent return_ = agent.train(step=int(10000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="TicTacToe-play-with-bot", algo_name="GumbelMuZero", github_repo_url="https://github.com/opendilab/LightZero", github_doc_model_url=None, github_doc_env_url=None, installation_guide=''' pip3 install DI-engine[common_env,video] pip3 install LightZero ''', usage_file_by_git_clone="./gumbel_muzero/tictactoe_play_with_bot_gumbel_muzero_deploy.py", usage_file_by_huggingface_ding="./gumbel_muzero/tictactoe_play_with_bot_gumbel_muzero_download.py", train_file="./gumbel_muzero/tictactoe_play_with_bot_gumbel_muzero.py", repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero", platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)", model_description="LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).", create_repo=True ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'main_config': { 'exp_name': 'TicTacToe-play-with-bot-GumbelMuZero', 'seed': 0, 'env': { 'env_id': 'TicTacToe-play-with-bot', 'battle_mode': 'play_with_bot_mode', 'collector_env_num': 8, 'evaluator_env_num': 5, 'n_evaluator_episode': 5, 'manager': { 'shared_memory': False } }, 'policy': { 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'model': { 'observation_shape': [3, 3, 3], 'action_space_size': 9, 'image_channel': 3, 'num_res_blocks': 1, 'num_channels': 16, 'fc_reward_layers': [8], 'fc_value_layers': [8], 'fc_policy_layers': [8], 'support_scale': 10, 'reward_support_size': 21, 'value_support_size': 21 }, 'use_rnd_model': False, 'sampled_algo': False, 'gumbel_algo': True, 'mcts_ctree': True, 'collector_env_num': 8, 'evaluator_env_num': 5, 'env_type': 'board_games', 'action_type': 'varied_action_space', 'battle_mode': 'play_with_bot_mode', 'monitor_extra_statistics': True, 'game_segment_length': 5, 'transform2string': False, 'gray_scale': False, 'use_augmentation': False, 'augmentation': ['shift', 'intensity'], 'ignore_done': False, 'update_per_collect': 50, 'model_update_ratio': 0.1, 'batch_size': 256, 'optim_type': 'Adam', 'learning_rate': 0.003, 'target_update_freq': 100, 'target_update_freq_for_intrinsic_reward': 1000, 'weight_decay': 0.0001, 'momentum': 0.9, 'grad_clip_value': 0.5, 'n_episode': 8, 'num_simulations': 30, 'discount_factor': 1, 'td_steps': 9, 'num_unroll_steps': 3, 'reward_loss_weight': 1, 'value_loss_weight': 0.25, 'policy_loss_weight': 1, 'policy_entropy_loss_weight': 0, 'ssl_loss_weight': 0, 'lr_piecewise_constant_decay': False, 'threshold_training_steps_for_final_lr': 50000, 'manual_temperature_decay': False, 'threshold_training_steps_for_final_temperature': 100000, 'fixed_temperature_value': 0.25, 'use_ture_chance_label_in_chance_encoder': False, 'use_priority': True, 'priority_prob_alpha': 0.6, 'priority_prob_beta': 0.4, 'root_dirichlet_alpha': 0.3, 'root_noise_weight': 0.25, 'random_collect_episode_num': 0, 'eps': { 'eps_greedy_exploration_in_collect': False, 'type': 'linear', 'start': 1.0, 'end': 0.05, 'decay': 100000 }, 'cfg_type': 'GumbelMuZeroPolicyDict', 'max_num_considered_actions': 3, 'reanalyze_ratio': 0.0, 'eval_freq': 2000, 'replay_buffer_size': 10000 }, 'wandb_logger': { 'gradient_logger': False, 'video_logger': False, 'plot_logger': False, 'action_logger': False, 'return_logger': False } }, 'create_config': { 'env': { 'type': 'tictactoe', 'import_names': ['zoo.board_games.tictactoe.envs.tictactoe_env'] }, 'env_manager': { 'type': 'subprocess' }, 'policy': { 'type': 'gumbel_muzero', 'import_names': ['lzero.policy.gumbel_muzero'] } } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](<TODO>) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/LightZero) - Doc: [Algorithm link](<TODO>) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 91.5 KB - Last Update Date: 2024-02-01 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Atari - Task: TicTacToe-play-with-bot - Gym version: 0.25.1 - DI-engine version: v0.5.0 - PyTorch version: 2.0.1+cu117 - Doc: [Environments link](<TODO>)
mnavas/roberta-finetuned-WebClassification-v2-smalllinguaEN	mnavas	"2023-05-05T13:32:49Z"	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-05-05T12:06:16Z"	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: roberta-finetuned-WebClassification-v2-smalllinguaEN results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-WebClassification-v2-smalllinguaEN This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5844 - Accuracy: 0.7143 - F1: 0.7143 - Precision: 0.7143 - Recall: 0.7143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.3084 \| 0.0714 \| 0.0714 \| 0.0714 \| 0.0714 \| \| No log \| 2.0 \| 14 \| 2.2951 \| 0.2857 \| 0.2857 \| 0.2857 \| 0.2857 \| \| No log \| 3.0 \| 21 \| 2.2725 \| 0.2143 \| 0.2143 \| 0.2143 \| 0.2143 \| \| No log \| 4.0 \| 28 \| 2.0608 \| 0.2143 \| 0.2143 \| 0.2143 \| 0.2143 \| \| No log \| 5.0 \| 35 \| 1.8552 \| 0.3571 \| 0.3571 \| 0.3571 \| 0.3571 \| \| No log \| 6.0 \| 42 \| 1.6846 \| 0.5714 \| 0.5714 \| 0.5714 \| 0.5714 \| \| No log \| 7.0 \| 49 \| 1.5844 \| 0.7143 \| 0.7143 \| 0.7143 \| 0.7143 \| \| No log \| 8.0 \| 56 \| 1.4531 \| 0.7143 \| 0.7143 \| 0.7143 \| 0.7143 \| \| No log \| 9.0 \| 63 \| 1.3746 \| 0.7143 \| 0.7143 \| 0.7143 \| 0.7143 \| \| No log \| 10.0 \| 70 \| 1.3663 \| 0.7143 \| 0.7143 \| 0.7143 \| 0.7143 \| ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.0+cpu - Datasets 2.10.1 - Tokenizers 0.13.2
trapoom555/Phi-2-Text-Embedding-cft	trapoom555	"2024-08-05T16:43:32Z"	0	3	transformers	[ "transformers", "safetensors", "sentence-embedding", "sentence-similarity", "feature-extraction", "en", "arxiv:2408.00690", "license:mit", "endpoints_compatible", "region:us" ]	sentence-similarity	"2024-05-07T14:49:52Z"	--- license: mit language: - en tags: - sentence-embedding - sentence-similarity - transformers - feature-extraction pipeline_tag: sentence-similarity --- # Phi-2-Text-Embedding-cft ## Description This is a fine-tuned version of [Phi-2](https://huggingface.co/microsoft/phi-2) to perform Text Embedding tasks. The model is fine-tuned using the Contrastive Fine-tuning and LoRA technique on NLI datasets. The paper can be found [here](https://arxiv.org/abs/2408.00690). ## Base Model [Phi-2](https://huggingface.co/microsoft/phi-2) ## Usage 1. Clone Phi-2 repository ```bash git clone https://huggingface.co/microsoft/phi-2 ``` 2. Change a tokenizer setting in `tokenizer_config.json` ```json "add_eos_token": true ``` 3. Use the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch import numpy as np class PhiSentenceEmbedding: def __init__(self, model_path='microsoft/phi-2', adapter_path=None): self.tokenizer = AutoTokenizer.from_pretrained(model_path) self.model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) if adapter_path != None: # Load fine-tuned LoRA self.model.load_adapter(adapter_path) def get_last_hidden_state(self, text): inputs = self.tokenizer(text, return_tensors="pt").to('cuda') with torch.no_grad(): out = self.model(inputs, output_hidden_states=True).hidden_states[-1][0, -1, :] return out.squeeze().float().cpu().numpy() def encode(self, sentences: list[str], kwargs) -> list[np.ndarray]: """ Returns a list of embeddings for the given sentences. Args: sentences: List of sentences to encode Returns: List of embeddings for the given sentences """ out = [] for s in sentences: out.append(self.get_last_hidden_state(s)) return out phi_sentence_embedding = PhiSentenceEmbedding(<your-cloned-base-model-path>, 'trapoom555/Phi-2-Text-Embedding-cft') example_sentences = ["I don't like apples", "I like apples"] encoded_sentences = phi_sentence_embedding.encode(example_sentences) print(encoded_sentences) ``` ## Training Details \| Training Details \| Value \| \|-------------------------\|-------------------\| \| Loss \| InfoNCE \| \| Batch Size \| 60 \| \| InfoNCE Temperature \| 0.05 \| \| Learning Rate \| 5e-05 \| \| Warmup Steps \| 100 \| \| Learning Rate Scheduler \| CosineAnnealingLR \| \| LoRA Rank \| 8 \| \| LoRA Alpha \| 32 \| \| LoRA Dropout \| 0.1 \| \| Training Precision \| bf16 \| \| Max Epoch \| 1 \| \| GPU \| RTX3090 \| \| Num GPUs \| 4 \| ## Training Scripts The training script for this model is written in this [Github repository](https://github.com/trapoom555/Language-Model-STS-CFT/tree/main). ## Checkpoints We provide checkpoints every 500 training steps which can be found [here](https://huggingface.co/trapoom555/Phi-2-Text-Embedding-cft-checkpoints). ## Evaluation Results \| Benchmarks \| Before cft \| After cft \| \|----------------\|----------------\|---------------\| \| STS12 \| 23.04 \| 61.62 \| \| STS13 \| 20.79 \| 71.87 \| \| STS14 \| 17.06 \| 60.46 \| \| STS15 \| 24.56 \| 71.18 \| \| STS16 \| 48.68 \| 74.77 \| \| STS17 \| 41.43 \| 80.20 \| \| STSBenchmark \| 37.87 \| 79.46 \| \| BOISSES \| 28.04 \| 64.06 \| \| SICK-R \| 48.40 \| 74.37 \| \| Overall \| 32.21 \| 70.89 \| ## Contributors Trapoom Ukarapol, Zhicheng Lee, Amy Xin ## Foot Notes This work is the final project of the Natural Language Processing Spring 2024 course at Tsinghua University 🟣. We would like to express our sincere gratitude to this course !
snshrivas10/tiny-chatbot-dpo	snshrivas10	"2024-05-19T06:33:48Z"	4	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-05-19T06:31:42Z"	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-chatbot-dpo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tiny-chatbot-dpo This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
prxy5604/f04f9214-3f11-48bc-9511-fa4ab80fd7ed	prxy5604	"2025-01-18T08:08:18Z"	8	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:tlphams/gollm-12.8b-instruct-v2.3", "base_model:adapter:tlphams/gollm-12.8b-instruct-v2.3", "license:cc-by-nc-4.0", "region:us" ]	null	"2025-01-18T06:20:35Z"	--- library_name: peft license: cc-by-nc-4.0 base_model: tlphams/gollm-12.8b-instruct-v2.3 tags: - axolotl - generated_from_trainer model-index: - name: f04f9214-3f11-48bc-9511-fa4ab80fd7ed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: tlphams/gollm-12.8b-instruct-v2.3 bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 500a091925e5b6f2_train_data.json ds_type: json format: custom path: /workspace/input_data/500a091925e5b6f2_train_data.json type: field_input: selected_word field_instruction: original field_output: perturbed format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: prxy5604/f04f9214-3f11-48bc-9511-fa4ab80fd7ed hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/500a091925e5b6f2_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 68317063-c692-4732-a3b0-9be4ed3ef2e3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 68317063-c692-4732-a3b0-9be4ed3ef2e3 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f04f9214-3f11-48bc-9511-fa4ab80fd7ed This model is a fine-tuned version of [tlphams/gollm-12.8b-instruct-v2.3](https://huggingface.co/tlphams/gollm-12.8b-instruct-v2.3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8424 \| 0.0003 \| 1 \| 0.4176 \| \| 1.7364 \| 0.0160 \| 50 \| 0.1728 \| \| 1.6649 \| 0.0320 \| 100 \| 0.1228 \| \| 1.8686 \| 0.0480 \| 150 \| 0.1104 \| \| 1.6596 \| 0.0640 \| 200 \| 0.1085 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
simonycl/best_model-sst-2-64-42	simonycl	"2023-07-26T02:15:01Z"	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-07-26T02:03:35Z"	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-42 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # best_model-sst-2-64-42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4849 - Accuracy: 0.8281 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 1.3914 \| 0.8125 \| \| No log \| 2.0 \| 8 \| 1.3910 \| 0.8203 \| \| 0.3843 \| 3.0 \| 12 \| 1.3922 \| 0.8203 \| \| 0.3843 \| 4.0 \| 16 \| 1.3920 \| 0.8203 \| \| 0.5793 \| 5.0 \| 20 \| 1.3923 \| 0.8203 \| \| 0.5793 \| 6.0 \| 24 \| 1.3989 \| 0.8203 \| \| 0.5793 \| 7.0 \| 28 \| 1.4029 \| 0.8281 \| \| 0.3663 \| 8.0 \| 32 \| 1.4103 \| 0.8281 \| \| 0.3663 \| 9.0 \| 36 \| 1.3999 \| 0.8281 \| \| 0.2779 \| 10.0 \| 40 \| 1.4010 \| 0.8281 \| \| 0.2779 \| 11.0 \| 44 \| 1.3978 \| 0.8281 \| \| 0.2779 \| 12.0 \| 48 \| 1.3963 \| 0.8203 \| \| 0.3589 \| 13.0 \| 52 \| 1.4087 \| 0.8203 \| \| 0.3589 \| 14.0 \| 56 \| 1.4067 \| 0.8281 \| \| 0.3185 \| 15.0 \| 60 \| 1.4148 \| 0.8281 \| \| 0.3185 \| 16.0 \| 64 \| 1.4171 \| 0.8359 \| \| 0.3185 \| 17.0 \| 68 \| 1.4140 \| 0.8359 \| \| 0.1743 \| 18.0 \| 72 \| 1.3982 \| 0.8359 \| \| 0.1743 \| 19.0 \| 76 \| 1.3650 \| 0.8359 \| \| 0.1416 \| 20.0 \| 80 \| 1.3456 \| 0.8359 \| \| 0.1416 \| 21.0 \| 84 \| 1.3210 \| 0.8359 \| \| 0.1416 \| 22.0 \| 88 \| 1.3070 \| 0.8359 \| \| 0.0354 \| 23.0 \| 92 \| 1.3015 \| 0.8359 \| \| 0.0354 \| 24.0 \| 96 \| 1.3319 \| 0.8438 \| \| 0.0035 \| 25.0 \| 100 \| 1.3656 \| 0.8281 \| \| 0.0035 \| 26.0 \| 104 \| 1.3587 \| 0.8281 \| \| 0.0035 \| 27.0 \| 108 \| 1.3243 \| 0.8359 \| \| 0.0006 \| 28.0 \| 112 \| 1.2945 \| 0.8438 \| \| 0.0006 \| 29.0 \| 116 \| 1.2898 \| 0.8438 \| \| 0.0028 \| 30.0 \| 120 \| 1.3066 \| 0.8438 \| \| 0.0028 \| 31.0 \| 124 \| 1.3055 \| 0.8438 \| \| 0.0028 \| 32.0 \| 128 \| 1.3202 \| 0.8438 \| \| 0.0049 \| 33.0 \| 132 \| 1.3351 \| 0.8438 \| \| 0.0049 \| 34.0 \| 136 \| 1.3190 \| 0.8438 \| \| 0.0102 \| 35.0 \| 140 \| 1.3141 \| 0.8438 \| \| 0.0102 \| 36.0 \| 144 \| 1.3142 \| 0.8438 \| \| 0.0102 \| 37.0 \| 148 \| 1.3647 \| 0.8281 \| \| 0.0034 \| 38.0 \| 152 \| 1.4250 \| 0.8203 \| \| 0.0034 \| 39.0 \| 156 \| 1.4708 \| 0.8203 \| \| 0.0001 \| 40.0 \| 160 \| 1.4570 \| 0.8203 \| \| 0.0001 \| 41.0 \| 164 \| 1.4446 \| 0.8203 \| \| 0.0001 \| 42.0 \| 168 \| 1.4345 \| 0.8281 \| \| 0.0001 \| 43.0 \| 172 \| 1.4272 \| 0.8281 \| \| 0.0001 \| 44.0 \| 176 \| 1.4185 \| 0.8281 \| \| 0.0001 \| 45.0 \| 180 \| 1.4048 \| 0.8281 \| \| 0.0001 \| 46.0 \| 184 \| 1.3962 \| 0.8281 \| \| 0.0001 \| 47.0 \| 188 \| 1.4924 \| 0.8203 \| \| 0.0002 \| 48.0 \| 192 \| 1.5361 \| 0.8125 \| \| 0.0002 \| 49.0 \| 196 \| 1.5831 \| 0.8125 \| \| 0.0292 \| 50.0 \| 200 \| 1.4789 \| 0.8281 \| \| 0.0292 \| 51.0 \| 204 \| 1.2642 \| 0.8359 \| \| 0.0292 \| 52.0 \| 208 \| 1.2154 \| 0.8516 \| \| 0.0001 \| 53.0 \| 212 \| 1.1895 \| 0.8516 \| \| 0.0001 \| 54.0 \| 216 \| 1.1775 \| 0.8438 \| \| 0.0001 \| 55.0 \| 220 \| 1.1730 \| 0.8438 \| \| 0.0001 \| 56.0 \| 224 \| 1.1746 \| 0.8438 \| \| 0.0001 \| 57.0 \| 228 \| 1.1782 \| 0.8516 \| \| 0.0001 \| 58.0 \| 232 \| 1.1838 \| 0.8516 \| \| 0.0001 \| 59.0 \| 236 \| 1.2456 \| 0.8281 \| \| 0.025 \| 60.0 \| 240 \| 1.3887 \| 0.8281 \| \| 0.025 \| 61.0 \| 244 \| 1.4950 \| 0.8125 \| \| 0.025 \| 62.0 \| 248 \| 1.5753 \| 0.8047 \| \| 0.0001 \| 63.0 \| 252 \| 1.6287 \| 0.8047 \| \| 0.0001 \| 64.0 \| 256 \| 1.6608 \| 0.8047 \| \| 0.0001 \| 65.0 \| 260 \| 1.6803 \| 0.8047 \| \| 0.0001 \| 66.0 \| 264 \| 1.6919 \| 0.7969 \| \| 0.0001 \| 67.0 \| 268 \| 1.5961 \| 0.8047 \| \| 0.0001 \| 68.0 \| 272 \| 1.4858 \| 0.8125 \| \| 0.0001 \| 69.0 \| 276 \| 1.4104 \| 0.8281 \| \| 0.0001 \| 70.0 \| 280 \| 1.3623 \| 0.8281 \| \| 0.0001 \| 71.0 \| 284 \| 1.3333 \| 0.8359 \| \| 0.0001 \| 72.0 \| 288 \| 1.3172 \| 0.8359 \| \| 0.0 \| 73.0 \| 292 \| 1.3107 \| 0.8359 \| \| 0.0 \| 74.0 \| 296 \| 1.5801 \| 0.8047 \| \| 0.0014 \| 75.0 \| 300 \| 1.7857 \| 0.8047 \| \| 0.0014 \| 76.0 \| 304 \| 1.8724 \| 0.7969 \| \| 0.0014 \| 77.0 \| 308 \| 1.9146 \| 0.7969 \| \| 0.0001 \| 78.0 \| 312 \| 1.9250 \| 0.7969 \| \| 0.0001 \| 79.0 \| 316 \| 1.9265 \| 0.7969 \| \| 0.0001 \| 80.0 \| 320 \| 1.9268 \| 0.7969 \| \| 0.0001 \| 81.0 \| 324 \| 1.9243 \| 0.7969 \| \| 0.0001 \| 82.0 \| 328 \| 1.9215 \| 0.7969 \| \| 0.0 \| 83.0 \| 332 \| 1.9188 \| 0.7969 \| \| 0.0 \| 84.0 \| 336 \| 1.9159 \| 0.7969 \| \| 0.0 \| 85.0 \| 340 \| 1.9137 \| 0.7969 \| \| 0.0 \| 86.0 \| 344 \| 1.9119 \| 0.7969 \| \| 0.0 \| 87.0 \| 348 \| 1.9103 \| 0.7969 \| \| 0.0009 \| 88.0 \| 352 \| 1.6541 \| 0.8047 \| \| 0.0009 \| 89.0 \| 356 \| 1.2749 \| 0.8438 \| \| 0.0 \| 90.0 \| 360 \| 1.2046 \| 0.8438 \| \| 0.0 \| 91.0 \| 364 \| 1.1909 \| 0.8438 \| \| 0.0 \| 92.0 \| 368 \| 1.1860 \| 0.8594 \| \| 0.0 \| 93.0 \| 372 \| 1.1901 \| 0.8594 \| \| 0.0 \| 94.0 \| 376 \| 1.1966 \| 0.8516 \| \| 0.0001 \| 95.0 \| 380 \| 1.2014 \| 0.8516 \| \| 0.0001 \| 96.0 \| 384 \| 1.2061 \| 0.8438 \| \| 0.0001 \| 97.0 \| 388 \| 1.2109 \| 0.8438 \| \| 0.0 \| 98.0 \| 392 \| 1.2170 \| 0.8516 \| \| 0.0 \| 99.0 \| 396 \| 1.2210 \| 0.8516 \| \| 0.0 \| 100.0 \| 400 \| 1.2237 \| 0.8516 \| \| 0.0 \| 101.0 \| 404 \| 1.2258 \| 0.8516 \| \| 0.0 \| 102.0 \| 408 \| 1.2276 \| 0.8438 \| \| 0.0 \| 103.0 \| 412 \| 1.2290 \| 0.8438 \| \| 0.0 \| 104.0 \| 416 \| 1.2301 \| 0.8438 \| \| 0.0 \| 105.0 \| 420 \| 1.2313 \| 0.8438 \| \| 0.0 \| 106.0 \| 424 \| 1.2324 \| 0.8438 \| \| 0.0 \| 107.0 \| 428 \| 1.2334 \| 0.8438 \| \| 0.0 \| 108.0 \| 432 \| 1.2345 \| 0.8438 \| \| 0.0 \| 109.0 \| 436 \| 1.2356 \| 0.8438 \| \| 0.0 \| 110.0 \| 440 \| 1.2366 \| 0.8438 \| \| 0.0 \| 111.0 \| 444 \| 1.2375 \| 0.8516 \| \| 0.0 \| 112.0 \| 448 \| 1.2384 \| 0.8516 \| \| 0.0 \| 113.0 \| 452 \| 1.2400 \| 0.8516 \| \| 0.0 \| 114.0 \| 456 \| 1.2415 \| 0.8516 \| \| 0.0 \| 115.0 \| 460 \| 1.2428 \| 0.8516 \| \| 0.0 \| 116.0 \| 464 \| 1.2439 \| 0.8516 \| \| 0.0 \| 117.0 \| 468 \| 1.2450 \| 0.8516 \| \| 0.0 \| 118.0 \| 472 \| 1.2459 \| 0.8516 \| \| 0.0 \| 119.0 \| 476 \| 1.2467 \| 0.8516 \| \| 0.0 \| 120.0 \| 480 \| 1.2476 \| 0.8516 \| \| 0.0 \| 121.0 \| 484 \| 1.2485 \| 0.8516 \| \| 0.0 \| 122.0 \| 488 \| 1.2495 \| 0.8516 \| \| 0.0 \| 123.0 \| 492 \| 1.2495 \| 0.8516 \| \| 0.0 \| 124.0 \| 496 \| 1.2491 \| 0.8516 \| \| 0.0 \| 125.0 \| 500 \| 1.2491 \| 0.8516 \| \| 0.0 \| 126.0 \| 504 \| 1.2494 \| 0.8516 \| \| 0.0 \| 127.0 \| 508 \| 1.2498 \| 0.8516 \| \| 0.0 \| 128.0 \| 512 \| 1.2503 \| 0.8516 \| \| 0.0 \| 129.0 \| 516 \| 1.2509 \| 0.8516 \| \| 0.0 \| 130.0 \| 520 \| 1.2514 \| 0.8516 \| \| 0.0 \| 131.0 \| 524 \| 1.2519 \| 0.8516 \| \| 0.0 \| 132.0 \| 528 \| 1.2527 \| 0.8516 \| \| 0.0 \| 133.0 \| 532 \| 1.2535 \| 0.8516 \| \| 0.0 \| 134.0 \| 536 \| 1.2542 \| 0.8516 \| \| 0.0 \| 135.0 \| 540 \| 1.2549 \| 0.8516 \| \| 0.0 \| 136.0 \| 544 \| 1.2554 \| 0.8516 \| \| 0.0 \| 137.0 \| 548 \| 1.3879 \| 0.8359 \| \| 0.0001 \| 138.0 \| 552 \| 1.6893 \| 0.7969 \| \| 0.0001 \| 139.0 \| 556 \| 1.8348 \| 0.7969 \| \| 0.0 \| 140.0 \| 560 \| 1.8942 \| 0.7969 \| \| 0.0 \| 141.0 \| 564 \| 1.8778 \| 0.7969 \| \| 0.0 \| 142.0 \| 568 \| 1.7187 \| 0.8047 \| \| 0.0001 \| 143.0 \| 572 \| 1.6119 \| 0.8203 \| \| 0.0001 \| 144.0 \| 576 \| 1.5523 \| 0.8281 \| \| 0.0 \| 145.0 \| 580 \| 1.5189 \| 0.8281 \| \| 0.0 \| 146.0 \| 584 \| 1.5008 \| 0.8281 \| \| 0.0 \| 147.0 \| 588 \| 1.4916 \| 0.8281 \| \| 0.0 \| 148.0 \| 592 \| 1.4872 \| 0.8281 \| \| 0.0 \| 149.0 \| 596 \| 1.4854 \| 0.8281 \| \| 0.0 \| 150.0 \| 600 \| 1.4849 \| 0.8281 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
ilanasto/a2c-PandaReachDense-v3	ilanasto	"2024-05-04T07:35:22Z"	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-05-04T07:31:08Z"	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.11 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
SG161222/Paragon_V1.0	SG161222	"2023-06-03T06:19:16Z"	103	54	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2023-05-03T07:17:32Z"	--- license: creativeml-openrail-m --- <b>Please read this!</b><br> This model is in the testing phase. The necessary VAE is already baked into the model.<br><hr> <b>The recommended negative prompt:</b><br><br> (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation, <a href="https://civitai.com/models/7808/easynegative">easynegative</a>, <a href="https://huggingface.co/zwv9/idk-who-is-this-model-belong-to/blob/main/bad-hands-5.pt">bad-hands-5</a><br><br> <b>Recommended parameters for generation:</b><br><br> <b>Sampling method:</b> Euler A<br> <b>CFG Scale:</b> 5-12<br> <b>Clip Skip:</b> 2<br><br> <b>Hires.Fix Parameters:</b><br><br> <b>Upscaler:</b> Latent or other<br> <b>Hires Steps:</b> 0 or other<br> <b>Denoising Strength:</b> 0.35 - 0.7<br> <b>Upscaled by:</b> 1.1 - 2.0<br><hr> <b>Examples:</b><br><br> <a href='https://postimg.cc/3kxXkXSJ' target='_blank'><img src='https://i.postimg.cc/0ypcHZ7m/Pic1.png' border='0' alt='Pic1'/></a> <a href='https://postimg.cc/2qmVqr8d' target='_blank'><img src='https://i.postimg.cc/q76n5vPF/Pic2.png' border='0' alt='Pic2'/></a> <a href='https://postimg.cc/k6GM84rS' target='_blank'><img src='https://i.postimg.cc/sX9MkQwT/Pic3.png' border='0' alt='Pic3'/></a> <a href='https://postimg.cc/gX7zKWdT' target='_blank'><img src='https://i.postimg.cc/j2xDtx1t/Pic4.png' border='0' alt='Pic4'/></a> <a href='https://postimg.cc/Js81xKVM' target='_blank'><img src='https://i.postimg.cc/mgztb6mz/Pic5.png' border='0' alt='Pic5'/></a> <a href='https://postimg.cc/Pp0HwQQG' target='_blank'><img src='https://i.postimg.cc/Zn55Xf9q/Pic6.png' border='0' alt='Pic6'/></a>
broalantap/GPT2-large-16-48000steps	broalantap	"2024-11-02T14:25:46Z"	145	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-11-02T14:24:06Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sriram-sanjeev9s/T5_model_1	sriram-sanjeev9s	"2024-04-02T05:48:13Z"	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt14", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-04-02T05:38:46Z"	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: T5_model_1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 config: fr-en split: validation args: fr-en metrics: - name: Bleu type: bleu value: 8.741 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # T5_model_1 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 1.4948 - Bleu: 8.741 - Gen Len: 17.974 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 10 \| 1.5554 \| 8.7554 \| 17.9983 \| \| No log \| 2.0 \| 20 \| 1.4948 \| 8.741 \| 17.974 \| ### Framework versions - Transformers 4.32.1 - Pytorch 1.12.1 - Datasets 2.18.0 - Tokenizers 0.13.2
PrunaAI/NeverSleep-Llama-3-Lumimaid-8B-v0.1-bnb-8bit-smashed	PrunaAI	"2024-07-21T07:20:20Z"	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:quantized:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-07-21T07:16:10Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo NeverSleep/Llama-3-Lumimaid-8B-v0.1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/NeverSleep-Llama-3-Lumimaid-8B-v0.1-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("NeverSleep/Llama-3-Lumimaid-8B-v0.1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model NeverSleep/Llama-3-Lumimaid-8B-v0.1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
atasoglu/mbert-base-cased-nli-stsb-tr	atasoglu	"2024-04-20T18:49:12Z"	23	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "tr", "dataset:nli_tr", "dataset:emrecan/stsb-mt-turkish", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	"2024-04-20T18:44:50Z"	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 datasets: - nli_tr - emrecan/stsb-mt-turkish language: - tr --- # atasoglu/mbert-base-cased-nli-stsb-tr This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model was adapted from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) and fine-tuned on these datasets: - [nli_tr](https://huggingface.co/datasets/nli_tr) - [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('atasoglu/mbert-base-cased-nli-stsb-tr') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('atasoglu/mbert-base-cased-nli-stsb-tr') model = AutoModel.from_pretrained('atasoglu/mbert-base-cased-nli-stsb-tr') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results Achieved results on the [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) test split are given below: ```txt Cosine-Similarity : Pearson: 0.8152 Spearman: 0.8130 Manhattan-Distance: Pearson: 0.8049 Spearman: 0.8128 Euclidean-Distance: Pearson: 0.8049 Spearman: 0.8126 Dot-Product-Similarity: Pearson: 0.7878 Spearman: 0.7822 ``` ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 180 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 18, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 108, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
LoneStriker/PiVoT-0.1-Starling-LM-RP-5.0bpw-h6-exl2	LoneStriker	"2023-11-28T16:35:59Z"	5	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-11-28T16:33:02Z"	--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- # PiVoT-0.1-Starling-LM-RP ![image/png](./PiVoT.png) # Model Details ### Description PiVoT-0.1-Starling-LM-RP is RP finetuned model based on Starling-LM-alpha. Using Synatra-RP dataset. <!-- prompt-template start --> ## Prompt template: OpenChat ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: ``` Follow me on twitter: https://twitter.com/stablefluffy Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕 Contact me on Telegram: https://t.me/AlzarTakkarsen
sd-concepts-library/3d-female-cyborgs	sd-concepts-library	"2022-09-17T20:15:59Z"	0	39	null	[ "license:mit", "region:us" ]	null	"2022-09-17T20:15:45Z"	--- license: mit --- ### 3d Female Cyborgs on Stable Diffusion This is the `<A female cyborg>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<A female cyborg> 0](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/3.jpeg) ![<A female cyborg> 1](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/0.jpeg) ![<A female cyborg> 2](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/1.jpeg) ![<A female cyborg> 3](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/2.jpeg) ![<A female cyborg> 4](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/4.jpeg)
disanda/first_try_4	disanda	"2023-07-09T07:21:57Z"	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2023-07-09T07:20:27Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: first_try_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # first_try_4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.5505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7226 \| 1.0 \| 157 \| 2.5273 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.12.0+cu102 - Datasets 2.12.0 - Tokenizers 0.13.3
DEVECOAI/Qwen2.5-Coder-32B-Instruct-bnb-4bit_lr2e-05_r16_rsTrue	DEVECOAI	"2025-04-04T10:40:45Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-04T10:40:06Z"	--- base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: DEVECOAI - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
CyberHarem/handa_roco_theidolmstermillionlive	CyberHarem	"2023-09-25T01:03:58Z"	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/handa_roco_theidolmstermillionlive", "license:mit", "region:us" ]	text-to-image	"2023-09-25T00:51:04Z"	--- license: mit datasets: - CyberHarem/handa_roco_theidolmstermillionlive pipeline_tag: text-to-image tags: - art --- # Lora of handa_roco_theidolmstermillionlive This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2380, you need to download `2380/handa_roco_theidolmstermillionlive.pt` as the embedding and `2380/handa_roco_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 2380, with the score of 0.759. The trigger words are: 1. `handa_roco_theidolmstermillionlive` 2. `long_hair, blush, bow, yellow_eyes, smile, hair_bow, bangs, twintails, green_eyes, grey_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.664 \| [Download](5100/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![pattern_6-5100](5100/previews/pattern_6.png) \| ![pattern_7-5100](5100/previews/pattern_7.png) \| ![pattern_8-5100](5100/previews/pattern_8.png) \| ![pattern_9-5100](5100/previews/pattern_9.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.724 \| [Download](4760/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![pattern_8-4760](4760/previews/pattern_8.png) \| ![pattern_9-4760](4760/previews/pattern_9.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.639 \| [Download](4420/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![pattern_6-4420](4420/previews/pattern_6.png) \| ![pattern_7-4420](4420/previews/pattern_7.png) \| ![pattern_8-4420](4420/previews/pattern_8.png) \| ![pattern_9-4420](4420/previews/pattern_9.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.681 \| [Download](4080/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![pattern_8-4080](4080/previews/pattern_8.png) \| ![pattern_9-4080](4080/previews/pattern_9.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.629 \| [Download](3740/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![pattern_6-3740](3740/previews/pattern_6.png) \| ![pattern_7-3740](3740/previews/pattern_7.png) \| ![pattern_8-3740](3740/previews/pattern_8.png) \| ![pattern_9-3740](3740/previews/pattern_9.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.660 \| [Download](3400/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![pattern_8-3400](3400/previews/pattern_8.png) \| ![pattern_9-3400](3400/previews/pattern_9.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.660 \| [Download](3060/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![pattern_6-3060](3060/previews/pattern_6.png) \| ![pattern_7-3060](3060/previews/pattern_7.png) \| ![pattern_8-3060](3060/previews/pattern_8.png) \| ![pattern_9-3060](3060/previews/pattern_9.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.679 \| [Download](2720/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![pattern_8-2720](2720/previews/pattern_8.png) \| ![pattern_9-2720](2720/previews/pattern_9.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.759 \| [Download](2380/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![pattern_6-2380](2380/previews/pattern_6.png) \| ![pattern_7-2380](2380/previews/pattern_7.png) \| ![pattern_8-2380](2380/previews/pattern_8.png) \| ![pattern_9-2380](2380/previews/pattern_9.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.755 \| [Download](2040/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![pattern_8-2040](2040/previews/pattern_8.png) \| ![pattern_9-2040](2040/previews/pattern_9.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.737 \| [Download](1700/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![pattern_6-1700](1700/previews/pattern_6.png) \| ![pattern_7-1700](1700/previews/pattern_7.png) \| ![pattern_8-1700](1700/previews/pattern_8.png) \| ![pattern_9-1700](1700/previews/pattern_9.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.609 \| [Download](1360/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![pattern_6-1360](1360/previews/pattern_6.png) \| ![pattern_7-1360](1360/previews/pattern_7.png) \| ![pattern_8-1360](1360/previews/pattern_8.png) \| ![pattern_9-1360](1360/previews/pattern_9.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.574 \| [Download](1020/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![pattern_6-1020](1020/previews/pattern_6.png) \| ![pattern_7-1020](1020/previews/pattern_7.png) \| ![pattern_8-1020](1020/previews/pattern_8.png) \| ![pattern_9-1020](1020/previews/pattern_9.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.514 \| [Download](680/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![pattern_6-680](680/previews/pattern_6.png) \| ![pattern_7-680](680/previews/pattern_7.png) \| ![pattern_8-680](680/previews/pattern_8.png) \| ![pattern_9-680](680/previews/pattern_9.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.331 \| [Download](340/handa_roco_theidolmstermillionlive.zip) \| [<NSFW, click to see>](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![pattern_6-340](340/previews/pattern_6.png) \| ![pattern_7-340](340/previews/pattern_7.png) \| ![pattern_8-340](340/previews/pattern_8.png) \| ![pattern_9-340](340/previews/pattern_9.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
sn56/ef3fc409-5f78-48ab-a9e3-eb62f77e5e20	sn56	"2025-02-07T12:46:05Z"	9	0	peft	[ "peft", "safetensors", "gptj", "axolotl", "generated_from_trainer", "base_model:furiosa-ai/mlperf-gpt-j-6b", "base_model:adapter:furiosa-ai/mlperf-gpt-j-6b", "region:us" ]	null	"2025-02-07T12:21:57Z"	--- library_name: peft base_model: furiosa-ai/mlperf-gpt-j-6b tags: - axolotl - generated_from_trainer model-index: - name: ef3fc409-5f78-48ab-a9e3-eb62f77e5e20 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: furiosa-ai/mlperf-gpt-j-6b bf16: true chat_template: llama3 data_processes: 24 dataset_prepared_path: null datasets: - data_files: - a6dae33cde59515e_train_data.json ds_type: json format: custom path: /workspace/input_data/a6dae33cde59515e_train_data.json type: field_input: selftext field_instruction: title field_output: answers.text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 4 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: sn56/ef3fc409-5f78-48ab-a9e3-eb62f77e5e20 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 9.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_warmup_steps: 50 max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/a6dae33cde59515e_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-8 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 205560767 sequence_len: 1024 shuffle: true strict: false tf32: true tokenizer_type: AutoTokenizer torch_compile: true total_train_batch_size: 32 train_batch_size: 8 train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: sn56-miner wandb_mode: disabled wandb_name: null wandb_project: god wandb_run: vxah wandb_runid: null warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ef3fc409-5f78-48ab-a9e3-eb62f77e5e20 This model is a fine-tuned version of [furiosa-ai/mlperf-gpt-j-6b](https://huggingface.co/furiosa-ai/mlperf-gpt-j-6b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 205560767 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 14.9727 \| 0.0010 \| 1 \| 3.9163 \| \| 11.3203 \| 0.0513 \| 50 \| 2.8247 \| \| 11.5312 \| 0.1026 \| 100 \| 2.7625 \| \| 11.2695 \| 0.1540 \| 150 \| 2.7405 \| \| 11.3359 \| 0.2053 \| 200 \| 2.7362 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
yamatazen/Ayla-Light-12B-Stock	yamatazen	"2025-02-17T08:42:47Z"	1	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:yamatazen/Ayla-Light-12B", "base_model:merge:yamatazen/Ayla-Light-12B", "base_model:yamatazen/Ayla-Light-12B-v2", "base_model:merge:yamatazen/Ayla-Light-12B-v2", "base_model:yamatazen/Ayla-Light-12B-v3", "base_model:merge:yamatazen/Ayla-Light-12B-v3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-17T04:11:51Z"	--- base_model: - yamatazen/Ayla-Light-12B-v2 - yamatazen/Ayla-Light-12B - yamatazen/Ayla-Light-12B-v3 library_name: transformers tags: - mergekit - merge --- ![image/png](https://huggingface.co/yamatazen/Ayla-Light-12B-Stock/resolve/main/stock.png?download=true) # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [yamatazen/Ayla-Light-12B](https://huggingface.co/yamatazen/Ayla-Light-12B) as a base. ### Models Merged The following models were included in the merge: * [yamatazen/Ayla-Light-12B-v2](https://huggingface.co/yamatazen/Ayla-Light-12B-v2) * [yamatazen/Ayla-Light-12B-v3](https://huggingface.co/yamatazen/Ayla-Light-12B-v3) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: yamatazen/Ayla-Light-12B models: - model: yamatazen/Ayla-Light-12B-v2 - model: yamatazen/Ayla-Light-12B-v3 merge_method: model_stock dtype: bfloat16 parameters: normalize: true ```
damgomz/ft_1_12e6_x8	damgomz	"2024-07-14T04:16:43Z"	11	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-06-19T16:47:07Z"	--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 153199.52336907387 \| \| Emissions (Co2eq in kg) \| 0.092703332620456 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 1.8086008221386256 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.159581013076256 \| \| Consumed energy (kWh) \| 1.9681818352148808 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.2949090824854672 \| \| Emissions (Co2eq in kg) \| 0.06000314665288726 \| ## Note 12 juillet 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/fp_bs32_lr1e4_x8 \| \| model_name \| ft_1_12e6_x8 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 1.2e-05 \| \| batch_size \| 1 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 29328 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| F-beta Score ---\|---\|---\|--- \| 0 \| 0.000000 \| 0.707225 \| 0.336807 \| \| 1 \| 0.272179 \| 0.248273 \| 0.899627 \| \| 2 \| 0.180017 \| 0.233954 \| 0.896014 \| \| 3 \| 0.113639 \| 0.252010 \| 0.919735 \| \| 4 \| 0.059565 \| 0.308335 \| 0.918050 \| \| 5 \| 0.032741 \| 0.333578 \| 0.926764 \| \| 6 \| 0.020775 \| 0.390008 \| 0.922906 \|
SeyedAli/Melanoma-Classification	SeyedAli	"2024-03-02T16:08:40Z"	18	1	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-03-02T10:57:00Z"	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: Melanoma-Classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Melanoma-Classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [SeyedAli/Skin-Lesion-Dataset](https://huggingface.co/datasets/SeyedAli/Skin-Lesion-Dataset) dataset. It achieves the following results on the evaluation set: - Loss: 0.5750 - Accuracy: 0.8167 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9779 \| 0.08 \| 100 \| 1.1158 \| 0.6041 \| \| 0.9934 \| 0.16 \| 200 \| 1.0227 \| 0.6501 \| \| 0.9562 \| 0.24 \| 300 \| 0.9276 \| 0.6748 \| \| 1.0995 \| 0.32 \| 400 \| 0.9088 \| 0.6836 \| \| 0.8198 \| 0.39 \| 500 \| 0.8581 \| 0.6949 \| \| 0.8034 \| 0.47 \| 600 \| 0.8444 \| 0.6967 \| \| 0.8319 \| 0.55 \| 700 \| 0.8196 \| 0.7148 \| \| 0.787 \| 0.63 \| 800 \| 0.8360 \| 0.6975 \| \| 0.8642 \| 0.71 \| 900 \| 0.8250 \| 0.7008 \| \| 0.8329 \| 0.79 \| 1000 \| 0.7939 \| 0.7172 \| \| 0.9678 \| 0.87 \| 1100 \| 0.7661 \| 0.7332 \| \| 0.8226 \| 0.95 \| 1200 \| 0.7284 \| 0.7373 \| \| 0.7967 \| 1.03 \| 1300 \| 0.7355 \| 0.7411 \| \| 0.6531 \| 1.1 \| 1400 \| 0.7561 \| 0.7247 \| \| 0.5719 \| 1.18 \| 1500 \| 0.6839 \| 0.7638 \| \| 0.6123 \| 1.26 \| 1600 \| 0.6857 \| 0.7584 \| \| 0.6504 \| 1.34 \| 1700 \| 0.6970 \| 0.7531 \| \| 0.6214 \| 1.42 \| 1800 \| 0.6841 \| 0.7576 \| \| 0.4925 \| 1.5 \| 1900 \| 0.6624 \| 0.7642 \| \| 0.5797 \| 1.58 \| 2000 \| 0.6287 \| 0.7709 \| \| 0.6018 \| 1.66 \| 2100 \| 0.6537 \| 0.7622 \| \| 0.6334 \| 1.74 \| 2200 \| 0.6413 \| 0.7713 \| \| 0.4111 \| 1.82 \| 2300 \| 0.6242 \| 0.7786 \| \| 0.4779 \| 1.89 \| 2400 \| 0.6260 \| 0.7790 \| \| 0.5488 \| 1.97 \| 2500 \| 0.6146 \| 0.7807 \| \| 0.3212 \| 2.05 \| 2600 \| 0.6975 \| 0.7707 \| \| 0.4282 \| 2.13 \| 2700 \| 0.6344 \| 0.7790 \| \| 0.2822 \| 2.21 \| 2800 \| 0.6985 \| 0.7845 \| \| 0.3003 \| 2.29 \| 2900 \| 0.5954 \| 0.7993 \| \| 0.2982 \| 2.37 \| 3000 \| 0.6156 \| 0.7940 \| \| 0.2628 \| 2.45 \| 3100 \| 0.6318 \| 0.7963 \| \| 0.2987 \| 2.53 \| 3200 \| 0.6495 \| 0.8030 \| \| 0.2714 \| 2.6 \| 3300 \| 0.6018 \| 0.8052 \| \| 0.3059 \| 2.68 \| 3400 \| 0.5944 \| 0.8078 \| \| 0.2762 \| 2.76 \| 3500 \| 0.6296 \| 0.7936 \| \| 0.3685 \| 2.84 \| 3600 \| 0.6277 \| 0.8017 \| \| 0.2299 \| 2.92 \| 3700 \| 0.5834 \| 0.8125 \| \| 0.3414 \| 3.0 \| 3800 \| 0.5750 \| 0.8167 \| \| 0.1082 \| 3.08 \| 3900 \| 0.6201 \| 0.8196 \| \| 0.049 \| 3.16 \| 4000 \| 0.6475 \| 0.8161 \| \| 0.102 \| 3.24 \| 4100 \| 0.6791 \| 0.8097 \| \| 0.0483 \| 3.31 \| 4200 \| 0.6582 \| 0.8216 \| \| 0.1204 \| 3.39 \| 4300 \| 0.6603 \| 0.8222 \| \| 0.0611 \| 3.47 \| 4400 \| 0.7174 \| 0.8190 \| \| 0.0555 \| 3.55 \| 4500 \| 0.6841 \| 0.8236 \| \| 0.0188 \| 3.63 \| 4600 \| 0.7009 \| 0.8240 \| \| 0.1292 \| 3.71 \| 4700 \| 0.7040 \| 0.8204 \| \| 0.0661 \| 3.79 \| 4800 \| 0.7074 \| 0.8238 \| \| 0.1061 \| 3.87 \| 4900 \| 0.6984 \| 0.8210 \| \| 0.0861 \| 3.95 \| 5000 \| 0.6913 \| 0.8230 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
suku9/gpt2-moses-dpo	suku9	"2025-04-10T02:40:25Z"	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "trl", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-10T02:40:19Z"	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF	mradermacher	"2024-12-08T18:33:10Z"	484	1	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "tango", "en", "es", "dataset:spanish-ir/messirve", "dataset:tatakof/messi_mod-v0.0.2", "base_model:sandbox-ai/Llama-3.1-Tango-8b-f16", "base_model:quantized:sandbox-ai/Llama-3.1-Tango-8b-f16", "license:llama3.1", "endpoints_compatible", "region:us", "imatrix" ]	null	"2024-12-08T14:08:32Z"	--- base_model: sandbox-ai/Llama-3.1-Tango-8b-f16 datasets: - spanish-ir/messirve - tatakof/messi_mod-v0.0.2 language: - en - es library_name: transformers license: llama3.1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - tango --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/sandbox-ai/Llama-3.1-Tango-8b-f16 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.1 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.8 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.8 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.8 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
KBLab/megatron.bert-large.unigram-32k-pretok.25k-steps	KBLab	"2024-04-23T08:44:03Z"	93	0	transformers	[ "transformers", "safetensors", "megatron-bert", "feature-extraction", "sv", "endpoints_compatible", "region:us" ]	feature-extraction	"2024-04-23T08:29:42Z"	--- language: - sv --- # megatron.bert-large.unigram-32k-pretok.25k-steps This BERT model was trained using the NeMo library. The size of the model is a regular bert-large. The model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden. Training was done for 25k training steps using a batch size of 8k. The model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters: - [megatron.bert-base.bpe-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.bpe-32k-no_pretok.25k-steps) - [megatron.bert-base.bpe-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.bpe-64k-no_pretok.25k-steps) - [megatron.bert-base.spe-bpe-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-32k-no_pretok.25k-steps) - [megatron.bert-base.spe-bpe-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-32k-pretok.25k-steps) - [megatron.bert-base.spe-bpe-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-64k-no_pretok.25k-steps) - [megatron.bert-base.spe-bpe-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-64k-pretok.25k-steps) - [megatron.bert-base.unigram-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-32k-no_pretok.25k-steps) - [megatron.bert-base.unigram-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-32k-pretok.25k-steps) - [megatron.bert-base.unigram-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-64k-no_pretok.25k-steps) - [megatron.bert-base.unigram-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-64k-pretok.25k-steps) - [megatron.bert-base.wordpiece-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-32k-no_pretok.25k-steps) - [megatron.bert-base.wordpiece-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps) - [megatron.bert-base.wordpiece-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-64k-no_pretok.25k-steps) - [megatron.bert-base.wordpiece-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-64k-pretok.25k-steps) - [megatron.bert-large.bpe-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.bpe-64k-no_pretok.25k-steps) - [megatron.bert-large.spe-bpe-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.spe-bpe-32k-pretok.25k-steps) - [megatron.bert-large.unigram-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.unigram-32k-pretok.25k-steps) - [megatron.bert-large.unigram-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.unigram-64k-pretok.25k-steps) - [megatron.bert-large.wordpiece-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.wordpiece-32k-pretok.25k-steps) - [megatron.bert-large.wordpiece-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.wordpiece-64k-pretok.25k-steps) ## Acknowledgements The training was performed on the Luxembourg national supercomputer MeluXina. The authors gratefully acknowledge the LuxProvide teams for their expert support.
texanrangee/98daa160-8406-4bb3-98a2-7a8e6abe22d9	texanrangee	"2025-03-22T04:37:10Z"	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-03-22T03:21:46Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
localmodels/WizardLM-7B-Uncensored-4bit	localmodels	"2023-05-29T03:54:19Z"	0	4	null	[ "region:us" ]	null	"2023-05-28T14:23:42Z"	## WizardLM 7B Uncensored 4-bit From ehartford: https://huggingface.co/ehartford/WizardLM-7B-Uncensored ### Folders ggml: q4_0 and q4_1 gptq: works with Triton and CUDA branches
DayStay/dqn-SpaceInvadersNoFrameskip-v4	DayStay	"2023-11-17T10:03:09Z"	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-11-17T10:02:33Z"	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 653.00 +/- 139.29 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DayStay -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DayStay -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DayStay ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
cs552-mlp/phi3-lora-arc3-gptq-3bits	cs552-mlp	"2024-06-12T12:21:25Z"	106	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	"2024-06-12T12:20:09Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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TheBloke/Swallow-13B-AWQ	TheBloke	"2023-12-19T21:58:07Z"	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "ja", "base_model:tokyotech-llm/Swallow-13b-hf", "base_model:quantized:tokyotech-llm/Swallow-13b-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	"2023-12-19T21:32:03Z"	--- base_model: tokyotech-llm/Swallow-13b-hf inference: false language: - en - ja library_name: transformers license: llama2 model_creator: tokyotech-llm model_name: Swallow 13B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Swallow 13B - AWQ - Model creator: [tokyotech-llm](https://huggingface.co/tokyotech-llm) - Original model: [Swallow 13B](https://huggingface.co/tokyotech-llm/Swallow-13b-hf) <!-- description start --> ## Description This repo contains AWQ model files for [tokyotech-llm's Swallow 13B](https://huggingface.co/tokyotech-llm/Swallow-13b-hf). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Swallow-13B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Swallow-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Swallow-13B-GGUF) * [tokyotech-llm's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/tokyotech-llm/Swallow-13b-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Swallow-13B-AWQ/tree/main) \| 4 \| 128 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data/viewer/) \| 4096 \| 7.48 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Swallow-13B-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Swallow-13B-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/Swallow-13B-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''{prompt} ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/Swallow-13B-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Swallow-13B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/Swallow-13B-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: tokyotech-llm's Swallow 13B # Swallow Our Swallow model has undergone continuous pre-training from the Llama 2 family, primarily with the addition of Japanese language data. The tuned versions use supervised fine-tuning (SFT). Links to other models can be found in the index. ## Swallow Model Index \|Model\|Swallow-hf\|Swallow-instruct-hf\| \|---\|---\|---\| \|7B\| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-hf) \| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-hf)\| \|13B\| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-hf) \| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf)\| \|70B\| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-hf) \| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-hf)\| ![logo](./logo.png) This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/). Read our [blog post](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) or our paper (preprint coming soon) for more details! ## Model Details * Model type: Please refer to LLaMA-2 technical report for details on the model architecture. * Language(s): Japanese English * Library: [Megatron-LM](https://github.com/rioyokotalab/Megatron-Llama2) * Tokenizer: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process. * Contact: swallow[at]nlp.c.titech.ac.jp ## Base Model Performance ### Japanese version \|Model\|Size\|JCommonsenseQA\|JEMHopQA\|NIILC\|JSQuAD\|XL-Sum\|MGSM\|WMT20-en-ja\|WMT20-ja-en\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| \| \|4-shot\|4-shot\|4-shot\|4-shot\|1-shot\|4-shot\|4-shot\|4-shot\| \|Llama 2\|7B\|0.3852\|0.4240\|0.3410\|0.7917\|0.1905\|0.0760\|0.1783\|0.1738\| \|Swallow\|7B\|0.4808\|0.5078\|0.5968\|0.8573\|0.1830\|0.1240\|0.2510\|0.1511\| \|Llama 2\|13B\|0.6997\|0.4415\|0.4170\|0.8533\|0.2139\|0.1320\|0.2146\|0.1982\| \|Swallow\|13B\|0.7837\|0.5063\|0.6398\|0.9005\|0.2168\|0.2040\|0.2720\|0.1771\| \|Llama 2\|70B\|0.8686\|0.4656\|0.5256\|0.9080\|0.2361\|0.3560\|0.2643\|0.2398\| \|Swallow\|70B\|0.9348\|0.6290\|0.6960\|0.9176\|0.2266\|0.4840\|0.3043\|0.2298\| ## Usage First install additional dependencies in [requirements.txt](./requirements.txt): ```sh pip install -r requirements.txt ``` ### Use the instruct model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-7b-instruct-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto") PROMPT_DICT = { "prompt_input": ( "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:" ), "prompt_no_input": ( "以下に、あるタスクを説明する指示があります。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 応答:" ), } def create_prompt(instruction, input=None): """ Generates a prompt based on the given instruction and an optional input. If input is provided, it uses the 'prompt_input' template from PROMPT_DICT. If no input is provided, it uses the 'prompt_no_input' template. Args: instruction (str): The instruction describing the task. input (str, optional): Additional input providing context for the task. Default is None. Returns: str: The generated prompt. """ if input: # Use the 'prompt_input' template when additional input is provided return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input) else: # Use the 'prompt_no_input' template when no additional input is provided return PROMPT_DICT["prompt_no_input"].format(instruction=instruction) # Example usage instruction_example = "以下のトピックに関する詳細な情報を提供してください。" input_example = "東京工業大学の主なキャンパスについて教えてください" prompt = create_prompt(instruction_example, input_example) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ### Use the base model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") prompt = "東京工業大学の主なキャンパスは、" input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## Training Datasets ### Continual Pre-Training The following datasets were used for continual pre-training. - [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) - [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - Swallow Corpus - [The Pile](https://huggingface.co/datasets/EleutherAI/pile) ### Instruction Tuning The following datasets were used for the instruction tuning. - [Anthropic HH-RLHF](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) - [Databricks Dolly 15-k](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) ## Risks and Limitations The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations. ## Acknowledgements We thank Meta Research for releasing Llama 2 under an open license for others to build on. Our project is supported by the [ABCI Large-scale Language Model Building Support Program](https://abci.ai/en/link/llm_support_program.html) of the National Institute of Advanced Industrial Science and Technology. ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. ## Authors Here are the team members: - From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Hiroki Iida](https://meshidenn.github.io/) - [Mengsay Loem](https://loem-ms.github.io/) - [Shota Hirai](https://huggingface.co/Kotemo428) - [Kakeru Hattori](https://aya-se.vercel.app/) - [Masanari Ohi](https://twitter.com/stjohn2007) - From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2)
sadhaklal/a-and-not-b	sadhaklal	"2024-02-21T07:45:12Z"	0	0	pytorch	[ "pytorch", "license:apache-2.0", "region:us" ]	null	"2024-02-21T04:31:18Z"	--- license: apache-2.0 library_name: pytorch --- # a-and-not-b A neuron that performs the A AND (NOT B) logical computation. It generates the following truth table: \| A \| B \| C \| \| - \| - \| - \| \| 0 \| 0 \| 0 \| \| 0 \| 1 \| 0 \| \| 1 \| 0 \| 1 \| \| 1 \| 1 \| 0 \| It is inspired by McCulloch & Pitts' 1943 paper 'A Logical Calculus of the Ideas Immanent in Nervous Activity'. It doesn't contain any parameters. It takes as input two column vectors of zeros and ones. It outputs a single column vector of zeros and ones. Its mechanism is outlined in Figure 10-3 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'. ![](https://raw.githubusercontent.com/sambitmukherjee/handson-ml3-pytorch/main/chapter10/Figure_10-3.png) Like all the other neurons in Figure 10-3, it is activated when at least two of its input connections are active. Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/logical_computations_with_neurons.ipynb ## Usage ``` import torch import torch.nn as nn from huggingface_hub import PyTorchModelHubMixin # Let's create two column vectors containing `0`s and `1`s. batch = {'a': torch.tensor([[0], [0], [1], [1]]), 'b': torch.tensor([[0], [1], [0], [1]])} class A_AND_NOT_B(nn.Module, PyTorchModelHubMixin): def __init__(self): super().__init__() self.operation = "C = A AND (NOT B)" def forward(self, x): a = x['a'] b = x['b'] b = -1 * b inputs = torch.cat([a, a, b], axis=1) column_sum = torch.sum(inputs, dim=1, keepdim=True) output = (column_sum >= 2).long() return output # Instantiate: a_and_not_b = A_AND_NOT_B.from_pretrained("sadhaklal/a-and-not-b") # Forward pass: output = a_and_not_b(batch) print(output) ```
huggingtweets/th3nfthunt3r	huggingtweets	"2022-10-16T18:36:40Z"	141	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2022-10-16T18:35:50Z"	--- language: en thumbnail: http://www.huggingtweets.com/th3nfthunt3r/1665945395711/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1563458962158022656/CWXK4AUr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Th3 NFT Hunt3r</div> <div style="text-align: center; font-size: 14px;">@th3nfthunt3r</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Th3 NFT Hunt3r. \| Data \| Th3 NFT Hunt3r \| \| --- \| --- \| \| Tweets downloaded \| 364 \| \| Retweets \| 50 \| \| Short tweets \| 113 \| \| Tweets kept \| 201 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/13l2dy5v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @th3nfthunt3r's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xgt6nuf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xgt6nuf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/th3nfthunt3r') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
mradermacher/SmolLM2-MagpieUltra-8k-GGUF	mradermacher	"2025-01-29T15:46:07Z"	221	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "trl", "sft", "en", "base_model:HuggingFaceTB/SmolLM2-MagpieUltra-8k", "base_model:quantized:HuggingFaceTB/SmolLM2-MagpieUltra-8k", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-01-29T15:19:41Z"	--- base_model: HuggingFaceTB/SmolLM2-MagpieUltra-8k language: - en library_name: transformers model_name: SmolLM2-MagpieUltra-8k quantized_by: mradermacher tags: - generated_from_trainer - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/HuggingFaceTB/SmolLM2-MagpieUltra-8k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q4_K_S.gguf) \| Q4_K_S \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q5_K_S.gguf) \| Q5_K_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q5_K_M.gguf) \| Q5_K_M \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q6_K.gguf) \| Q6_K \| 1.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q8_0.gguf) \| Q8_0 \| 1.9 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.f16.gguf) \| f16 \| 3.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
RichardErkhov/alonzogarbanzo_-_Bloom-1b7-ropes-IT-baseline-4bits	RichardErkhov	"2025-03-04T21:51:25Z"	0	0	null	[ "safetensors", "bloom", "4-bit", "bitsandbytes", "region:us" ]	null	"2025-03-04T21:50:29Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Bloom-1b7-ropes-IT-baseline - bnb 4bits - Model creator: https://huggingface.co/alonzogarbanzo/ - Original model: https://huggingface.co/alonzogarbanzo/Bloom-1b7-ropes-IT-baseline/ Original model description: --- license: bigscience-bloom-rail-1.0 base_model: bigscience/bloom-1b7 tags: - generated_from_trainer model-index: - name: Bloom-1b7-ropes-IT-baseline results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Bloom-1b7-ropes-IT-baseline This model is a fine-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Instruction Tuned on the ropes task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/ropes ## Training procedure Given a set of prompts: ``` python prompts = [ "Given the following background and situation, answer the question: ", "Based on the background information and the current situation, what is the answer to the question? ", "Considering the background and the described situation, provide an answer to this question: ", ] ``` Each example is concatenated with the prompt, background, situation, question and answer: ``` python input_text = f"{prompt}Background: {background} Situation: {situation} Question: {question} Answer: {answer_text}." ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results Final results: {'loss': 0.024, 'grad_norm': 1.3331243991851807, 'learning_rate': 8.000000000000001e-07, 'epoch': 10.0} Average results: {'train_runtime': 862.219, 'train_samples_per_second': 2.32, 'train_steps_per_second': 0.58, 'train_loss': 0.4160269268453121, 'epoch': 10.0} ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
RogerB/afro-xlmr-small-finetuned-kintweetsD	RogerB	"2023-07-09T16:16:36Z"	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2023-07-09T15:59:59Z"	--- license: afl-3.0 tags: - generated_from_trainer model-index: - name: afro-xlmr-small-finetuned-kintweetsD results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # afro-xlmr-small-finetuned-kintweetsD This model is a fine-tuned version of [Davlan/afro-xlmr-small](https://huggingface.co/Davlan/afro-xlmr-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8179 \| 1.0 \| 900 \| 1.6363 \| \| 1.7094 \| 2.0 \| 1800 \| 1.5927 \| \| 1.6816 \| 3.0 \| 2700 \| 1.6023 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
tinycompany/BiBo-R1-v0.2	tinycompany	"2025-03-15T21:23:54Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-15T21:22:15Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mci29/sn29_w1m1_h9i7	mci29	"2025-02-07T20:12:38Z"	320	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-07T20:09:32Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rhplus0831/maid-yuzu-v2-mid	rhplus0831	"2024-02-03T04:17:12Z"	4	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "base_model:smelborp/MixtralOrochi8x7B", "base_model:merge:smelborp/MixtralOrochi8x7B", "base_model:ycros/BagelMIsteryTour-v2-8x7B", "base_model:merge:ycros/BagelMIsteryTour-v2-8x7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-02-03T03:43:41Z"	--- base_model: - smelborp/MixtralOrochi8x7B - ycros/BagelMIsteryTour-v2-8x7B library_name: transformers tags: - mergekit - merge --- # maid-yuzu-v2-mid This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) * [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: model: path: smelborp/MixtralOrochi8x7B dtype: bfloat16 merge_method: slerp parameters: t: - value: 0.375 slices: - sources: - layer_range: [0, 32] model: model: path: smelborp/MixtralOrochi8x7B - layer_range: [0, 32] model: model: path: ycros/BagelMIsteryTour-v2-8x7B ```
TheBloke/llama2_7b_merge_orcafamily-GPTQ	TheBloke	"2023-11-20T18:19:45Z"	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "dataset:Open-Orca/SlimOrca", "dataset:beaugogh/openorca-multiplechoice-10k", "base_model:yeen214/llama2_7b_merge_orcafamily", "base_model:quantized:yeen214/llama2_7b_merge_orcafamily", "license:mit", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	"2023-11-20T17:55:43Z"	--- base_model: yeen214/llama2_7b_merge_orcafamily datasets: - Open-Orca/SlimOrca - beaugogh/openorca-multiplechoice-10k inference: false language: - en license: mit metrics: - accuracy model_creator: yeen heui yeen model_name: Llama2 7B Merge Orcafamily model_type: llama prompt_template: 'Info on prompt template will be added shortly. ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama2 7B Merge Orcafamily - GPTQ - Model creator: [yeen heui yeen](https://huggingface.co/yeen214) - Original model: [Llama2 7B Merge Orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily) <!-- description start --> # Description This repo contains GPTQ model files for [yeen heui yeen's Llama2 7B Merge Orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GGUF) * [yeen heui yeen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: TBC ``` Info on prompt template will be added shortly. ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `mit`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [yeen heui yeen's Llama2 7B Merge Orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily). <!-- licensing end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) \| 4096 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) \| 4096 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) \| 4096 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) \| 4096 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) \| 4096 \| 7.62 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) \| 4096 \| 4.02 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/llama2_7b_merge_orcafamily-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/llama2_7b_merge_orcafamily-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `llama2_7b_merge_orcafamily-GPTQ`: ```shell mkdir llama2_7b_merge_orcafamily-GPTQ huggingface-cli download TheBloke/llama2_7b_merge_orcafamily-GPTQ --local-dir llama2_7b_merge_orcafamily-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir llama2_7b_merge_orcafamily-GPTQ huggingface-cli download TheBloke/llama2_7b_merge_orcafamily-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir llama2_7b_merge_orcafamily-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir llama2_7b_merge_orcafamily-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/llama2_7b_merge_orcafamily-GPTQ --local-dir llama2_7b_merge_orcafamily-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/llama2_7b_merge_orcafamily-GPTQ`. - To download from a specific branch, enter for example `TheBloke/llama2_7b_merge_orcafamily-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `llama2_7b_merge_orcafamily-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/llama2_7b_merge_orcafamily-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''Info on prompt template will be added shortly. ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/llama2_7b_merge_orcafamily-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''Info on prompt template will be added shortly. ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: yeen heui yeen's Llama2 7B Merge Orcafamily This model is based on the LLama 7b model as a backbone, and datasets from various Orcas have been fine-tuned and merged. The three models were combined, and the model with the best ARC and MMLU performance was given the highest weight. First: fine-tuning beaugogh/openorca-multiplechoice-10k on llama2 7b, but using the NEFTune method. Second: model fine-tuned with the SlimOrca dataset on llama2 7b. Third : Model with beaugogh/openorca-multiplechoice-10k fine-tuned on llama2 7b. We'll add the results once we have the official results
ClarenceDan/b1539395-e53a-46d2-bdd9-e7009913d702	ClarenceDan	"2025-01-18T11:18:23Z"	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "region:us" ]	null	"2025-01-18T11:00:21Z"	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: b1539395-e53a-46d2-bdd9-e7009913d702 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-1.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4ee00b5ffc79c413_train_data.json ds_type: json format: custom path: /workspace/input_data/4ee00b5ffc79c413_train_data.json type: field_instruction: seq field_output: labels_str format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/b1539395-e53a-46d2-bdd9-e7009913d702 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/4ee00b5ffc79c413_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 7ffb5512-24ac-400f-b2e1-903a11f0a7da wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 7ffb5512-24ac-400f-b2e1-903a11f0a7da warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b1539395-e53a-46d2-bdd9-e7009913d702 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0767 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.3681 \| 0.0000 \| 1 \| 2.1789 \| \| 1.9003 \| 0.0001 \| 3 \| 2.1549 \| \| 1.6763 \| 0.0002 \| 6 \| 1.7226 \| \| 1.1284 \| 0.0003 \| 9 \| 1.0767 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
shibajustfor/274b9903-7e15-48a6-8eb8-6843490553e3	shibajustfor	"2025-02-24T00:50:05Z"	0	0	peft	[ "peft", "qwen2", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2-0.5B-Instruct", "region:us" ]	null	"2025-02-24T00:50:01Z"	--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2-0.5B-Instruct model-index: - name: shibajustfor/274b9903-7e15-48a6-8eb8-6843490553e3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shibajustfor/274b9903-7e15-48a6-8eb8-6843490553e3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2688 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Harshini2004/mbart_en_te_model	Harshini2004	"2025-02-19T17:32:12Z"	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "dataset:flores", "base_model:facebook/mbart-large-50-many-to-many-mmt", "base_model:finetune:facebook/mbart-large-50-many-to-many-mmt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2025-02-19T17:26:44Z"	--- library_name: transformers base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer datasets: - flores model-index: - name: mbart_en_te_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart_en_te_model This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the flores dataset. It achieves the following results on the evaluation set: - Loss: 0.8832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 100 \| 0.9786 \| \| No log \| 2.0 \| 200 \| 0.8959 \| \| No log \| 3.0 \| 300 \| 0.8832 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.1 - Tokenizers 0.21.0
harikc456/vizdoom_health_gathering_supreme	harikc456	"2023-03-25T03:39:54Z"	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-03-25T03:39:41Z"	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.07 +/- 5.48 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r harikc456/vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF	mradermacher	"2025-01-02T23:31:44Z"	13	0	transformers	[ "transformers", "gguf", "Safetensors", "text-generation-inference", "merge", "en", "base_model:MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28", "base_model:quantized:MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-01-02T23:07:26Z"	--- base_model: MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28 language: - en library_name: transformers license: apache-2.0 model_creator: MaziyarPanahi model_name: Experiment26Neuralsirkrishna_Strangemerges_30Experiment28 quantized_by: mradermacher tags: - Safetensors - text-generation-inference - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2	Zoyd	"2024-06-04T15:55:14Z"	76	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	"2024-06-04T15:48:07Z"	--- base_model: - Nitral-AI/Poppy-1.35-Phase1 - Nitral-AI/Pp-72xra1 library_name: transformers tags: - mergekit - merge license: other language: - en --- Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2)</center> \| <center>3478 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_0bpw_exl2)</center> \| <center>3894 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2)</center> \| <center>5559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_0bpw_exl2)</center> \| <center>6489 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2)</center> \| <center>6909 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2)</center> \| <center>8123 MB</center> \| <center>8</center> \| ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/Boje781GkTdYgORTYGI6r.png) # "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences. # Note: This variant is an attempt to get something closer to 0.72 while maintaining the improvements of 1.30. # : [Presets in repo folder](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets). # If you want to use vision functionality: You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). And need to load the specified mmproj file: [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16). ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Nitral-AI/Pp-72xra1 layer_range: [0, 32] - model: Nitral-AI/Poppy-1.35-Phase1 layer_range: [0, 32] merge_method: slerp base_model: Nitral-AI/Pp-72xra1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
omersubasi/xlm-roberta-base-finetuned-panx-it	omersubasi	"2023-12-08T05:57:10Z"	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2023-12-08T05:52:04Z"	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8218390804597702 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2503 - F1: 0.8218 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8253 \| 1.0 \| 70 \| 0.3503 \| 0.7160 \| \| 0.2781 \| 2.0 \| 140 \| 0.2643 \| 0.8148 \| \| 0.1871 \| 3.0 \| 210 \| 0.2503 \| 0.8218 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.1.0+cu118 - Datasets 1.16.1 - Tokenizers 0.15.0
swanggl/movie-genre-classifier	swanggl	"2025-03-28T07:14:34Z"	49	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2025-03-27T21:00:24Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>503</h1> <p>We had to rate limit you. To continue using our service, please log in or create an account.</p> </div> </main> </body> </html>
fnlp/SmolLM-135M-GQA-d_kv_128	fnlp	"2025-03-13T07:23:55Z"	27	0	null	[ "safetensors", "llama", "text-generation", "dataset:HuggingFaceTB/smollm-corpus", "arxiv:2502.14837", "base_model:HuggingFaceTB/SmolLM-135M", "base_model:finetune:HuggingFaceTB/SmolLM-135M", "license:apache-2.0", "region:us" ]	text-generation	"2025-03-04T11:30:23Z"	--- license: apache-2.0 datasets: - HuggingFaceTB/smollm-corpus base_model: - HuggingFaceTB/SmolLM-135M pipeline_tag: text-generation --- Research Paper ["Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs"](https://arxiv.org/abs/2502.14837) ## Inference - Step 1: Download the [monkey patch file](https://github.com/JT-Ushio/MHA2MLA/blob/main/src/mha2mla/monkey_patch.py). ```shell wget https://raw.githubusercontent.com/JT-Ushio/MHA2MLA/refs/heads/main/src/mha2mla/monkey_patch.py ``` - Step 2(Option): For MHA2MLA models using Partial-RoPE 2-nrom method, Download the [qk_2-norm file](https://github.com/JT-Ushio/MHA2MLA/tree/main/utils). Take `qk_tensor_135M.pth` as an example: ```shell wget https://github.com/JT-Ushio/MHA2MLA/raw/refs/heads/main/utils/qk_tensor_135M.pth ``` - Step 3: Download the [MHA2MLA models](https://huggingface.co/fnlp/SmolLM-135M-GQA-d_kv_128) and run inference. Take `fnlp/SmolLM-135M-GQA-d_kv_128` as an example: ```python import torch from transformers import AutoConfig, AutoTokenizer, LlamaForCausalLM from monkey_patch import infer_monkey_patch model_name = "fnlp/SmolLM-135M-GQA-d_kv_128" # Monkey Patch: MHA -> MLA config = AutoConfig.from_pretrained(model_name) if "RoPE" in config: config.RoPE["qk_tensor_path"] = "qk_tensor_135M.pth" # Configuration for Specific Models infer_monkey_patch(config.RoPE) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = LlamaForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.bfloat16).cuda() # Generate text = "Which American-born Sinclair won the Nobel Prize for Literature in 1930?" inputs = tokenizer(text, return_tensors="pt").to(model.device) generation_kwargs = {"do_sample": False, "use_cache": True, "max_new_tokens": 128} output = model.generate(inputs, generation_kwargs) print(tokenizer.decode(output[0], skip_special_tokens=True)) # - Sinclair Lewis ``` ## Citation ``` @misc{ji2025economicalinferenceenablingdeepseeks, title={Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs}, author={Tao Ji and Bin Guo and Yuanbin Wu and Qipeng Guo and Lixing Shen and Zhan Chen and Xipeng Qiu and Qi Zhang and Tao Gui}, year={2025}, eprint={2502.14837}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.14837}, } ```
Remex23/llama-2-finetune-elsa	Remex23	"2024-06-04T13:26:36Z"	0	0	null	[ "llama-2", "fine-tuning", "causal-lm", "en", "license:apache-2.0", "region:us" ]	null	"2024-06-04T13:08:10Z"	--- language: en tags: - llama-2 - fine-tuning - causal-lm license: apache-2.0 --- # Llama-2-finetune-Elsa This is a fine-tuned version of the Llama-2-7b-chat model using the `Remex23/counselchat-llama2-full` dataset.
qtdy/roberta-base-klue-ynat-classification	qtdy	"2025-02-13T02:07:41Z"	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-02-13T02:07:13Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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minji222/mistral_lora_clm_with_added_tokens	minji222	"2024-04-09T07:38:53Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-09T05:26:00Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aardvark-labs/stp-classifier-4-2	aardvark-labs	"2025-03-13T12:17:31Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-03-13T10:03:48Z"	--- library_name: transformers ---
RE-N-Y/cc3m-transformer_blocks.18-1	RE-N-Y	"2024-11-18T04:19:08Z"	5	0	finebooru	[ "finebooru", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	"2024-11-18T04:11:47Z"	--- library_name: finebooru tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/RE-N-Y/finebooru - Docs: [More Information Needed]
Ahmed007/hossam-t5	Ahmed007	"2024-02-18T09:09:48Z"	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:UBC-NLP/octopus", "base_model:finetune:UBC-NLP/octopus", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-02-18T01:30:28Z"	--- base_model: UBC-NLP/octopus tags: - generated_from_trainer model-index: - name: hossam-t5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hossam-t5 This model is a fine-tuned version of [UBC-NLP/octopus](https://huggingface.co/UBC-NLP/octopus) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
mav23/Qwen2.5-14B-UpToDate-GGUF	mav23	"2024-11-19T03:05:39Z"	14	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "sft", "en", "base_model:CultriX/Qwen2.5-14B-MegaMerge-pt2", "base_model:quantized:CultriX/Qwen2.5-14B-MegaMerge-pt2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-11-19T01:06:53Z"	--- base_model: CultriX/Qwen2.5-14B-MegaMerge-pt2 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: CultriX - License: apache-2.0 - Finetuned from model : CultriX/Qwen2.5-14B-MegaMerge-pt2 This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
John6666/hentai-cinematic-pony-v2-sdxl	John6666	"2024-07-11T22:53:44Z"	60	2	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "cinematic", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2024-07-11T22:49:18Z"	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - cinematic - pony --- Original model is [here](https://civitai.com/models/492705/hentaicinematicpony?modelVersionId=637173).
mradermacher/Llama-3-22B-Instruct-v0.1-GGUF	mradermacher	"2024-05-31T09:01:44Z"	9	2	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-05-30T21:36:35Z"	--- base_model: DataGuard/Llama-3-22B-Instruct-v0.1 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DataGuard/Llama-3-22B-Instruct-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q2_K.gguf) \| Q2_K \| 8.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 9.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 10.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ3_S.gguf) \| IQ3_S \| 10.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ3_M.gguf) \| IQ3_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 11.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 12.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 13.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 13.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 16.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q6_K.gguf) \| Q6_K \| 18.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q8_0.gguf) \| Q8_0 \| 24.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
sail-rvc/Principal_of_the_Thing__RVC_V2_-_500_Epochs_	sail-rvc	"2023-07-14T07:30:06Z"	1	0	transformers	[ "transformers", "rvc", "sail-rvc", "audio-to-audio", "endpoints_compatible", "region:us" ]	audio-to-audio	"2023-07-14T07:29:51Z"	--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # Principal_of_the_Thing__RVC_V2_-_500_Epochs_ ## RVC Model ![banner](https://i.imgur.com/xocCjhH.jpg) This model repo was automatically generated. Date: 2023-07-14 07:30:06 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
jwhj/Qwen2.5-Math-1.5B-SFT	jwhj	"2024-12-10T06:28:58Z"	158	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-12-10T05:37:18Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
P3ps/distilbert-amazon-shoe-reviews	P3ps	"2023-04-20T11:34:31Z"	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-04-20T11:07:39Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-amazon-shoe-reviews results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-amazon-shoe-reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9519 - Accuracy: 0.5757 - F1: [0.63178677 0.45622938 0.50453543 0.55380711 0.73119358] - Precision: [0.62256809 0.46798542 0.48583569 0.58248799 0.71751969] - Recall: [0.64128257 0.4450495 0.52473228 0.52781809 0.74539877] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------------------------------------------------------:\|:--------------------------------------------------------:\|:--------------------------------------------------------:\| \| 0.9652 \| 1.0 \| 2813 \| 0.9519 \| 0.5757 \| [0.63178677 0.45622938 0.50453543 0.55380711 0.73119358] \| [0.62256809 0.46798542 0.48583569 0.58248799 0.71751969] \| [0.64128257 0.4450495 0.52473228 0.52781809 0.74539877] \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
sehilnlf/model_v3_v2	sehilnlf	"2024-05-26T18:34:55Z"	28	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large", "base_model:finetune:facebook/bart-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-05-26T06:54:37Z"	--- license: apache-2.0 base_model: facebook/bart-large tags: - text2text-generation - generated_from_trainer metrics: - sacrebleu model-index: - name: model_v3_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_v3_v2 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5669 - Sacrebleu: 66.8302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\| \| No log \| 0.99 \| 54 \| 0.6545 \| 66.3234 \| \| No log \| 1.99 \| 109 \| 0.5940 \| 66.8342 \| \| No log \| 2.96 \| 162 \| 0.5669 \| 66.8302 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
0xagentai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_poisonous_bear	0xagentai	"2025-04-20T05:13:03Z"	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am jumping poisonous bear", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-15T08:22:48Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
q631599119/model2	q631599119	"2025-03-21T00:50:12Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-03-21T00:50:11Z"	--- license: apache-2.0 ---
zyh571p/whisper-small-finetuned	zyh571p	"2024-05-23T18:13:32Z"	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-05-23T14:33:50Z"	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 0.0 \| 4.5662 \| 1000 \| 0.0000 \| 0.1349 \| \| 0.0 \| 9.1324 \| 2000 \| 0.0000 \| 0.0337 \| \| 0.0 \| 13.6986 \| 3000 \| 0.0000 \| 0.0337 \| \| 0.0 \| 18.2648 \| 4000 \| 0.0000 \| 0.0337 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ReLURavioli/rlur0120	ReLURavioli	"2025-03-04T11:06:37Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-04T11:06:06Z"	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: ReLURavioli - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ethicalabs/Kurtis-Qwen2.5-0.5B-Instruct-PEFT	ethicalabs	"2025-03-02T17:29:36Z"	0	0	peft	[ "peft", "safetensors", "text-generation-inference", "text-generation", "en", "dataset:mrs83/kurtis_mental_health_final", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "license:mit", "region:us" ]	text-generation	"2025-03-02T16:56:58Z"	--- library_name: peft license: mit datasets: - mrs83/kurtis_mental_health_final language: - en base_model: - Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation tags: - text-generation-inference --- # Model Card for Kurtis Kurtis is a mental-health AI assistant designed with empathy at its core. Unlike other AI models that aim for peak efficiency, Kurtis prioritizes understanding, emotional nuance, and meaningful conversations. It won’t solve complex math problems or write code, nor will it generate images or videos. Instead, Kurtis focuses on being a thoughtful companion, offering support, perspective, and human-like dialogue. It doesn’t strive to break records or chase artificial intelligence supremacy—its goal is to create a space for genuine interaction. Whether you need someone to talk to, reflect on ideas with, or engage in insightful discussion, Kurtis is there to listen and respond in an understanding way.
maxfrax/distilbert-base-uncased-finetuned-emotion	maxfrax	"2024-02-16T17:32:59Z"	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-02-16T17:20:28Z"	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.926 - name: F1 type: f1 value: 0.9258243133918047 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2134 - Accuracy: 0.926 - F1: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3212 \| 0.906 \| 0.9047 \| \| No log \| 2.0 \| 500 \| 0.2134 \| 0.926 \| 0.9258 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
genow2/123	genow2	"2025-04-18T21:46:05Z"	0	0	null	[ "region:us" ]	null	"2025-04-18T21:46:05Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
tomrb/bettercallbloom-560m	tomrb	"2022-10-17T12:39:37Z"	133	0	transformers	[ "transformers", "pytorch", "bloom", "text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2022-10-16T13:09:33Z"	--- language: en widget: - text: "my example goes here in the requested language" license: mit --- # WORK IN PROGRESS # bettercallbloom-560m Finetuned bloom-560m model on the PileOfLaw - r/legal_advice ## Model description ## Intended uses & limitations ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 35.13 \| 45.99 \| 87.65 \| 83.4 \| 29.41 \| 65.85 \| 1.16 \| 1,17 \| 37.50 \| 75.20 \|
oljik/tut_unsloth_3b_lora_model	oljik	"2025-02-18T23:32:57Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-02-18T23:32:44Z"	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: oljik - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Benjaminpwh/xlsr-toratan-60-copt	Benjaminpwh	"2025-03-26T17:51:13Z"	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "pretraining", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-03-26T17:14:39Z"	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: xlsr-toratan-60-copt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlsr-toratan-60-copt This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
ClarenceDan/a5ce742d-11ad-4f04-9cb9-5b3888224913	ClarenceDan	"2025-03-04T17:05:48Z"	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:DeepMount00/Llama-3-8b-Ita", "base_model:adapter:DeepMount00/Llama-3-8b-Ita", "license:llama3", "region:us" ]	null	"2025-03-04T16:28:54Z"	--- library_name: peft license: llama3 base_model: DeepMount00/Llama-3-8b-Ita tags: - axolotl - generated_from_trainer model-index: - name: a5ce742d-11ad-4f04-9cb9-5b3888224913 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: DeepMount00/Llama-3-8b-Ita bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e5bda4a33546a6d1_train_data.json ds_type: json format: custom path: /workspace/input_data/e5bda4a33546a6d1_train_data.json type: field_input: lang field_instruction: sentence1 field_output: sentence2 format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/a5ce742d-11ad-4f04-9cb9-5b3888224913 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/e5bda4a33546a6d1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <\|eot_id\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 791b8a0f-4f7d-4883-814e-024386776e92 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 791b8a0f-4f7d-4883-814e-024386776e92 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # a5ce742d-11ad-4f04-9cb9-5b3888224913 This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.7645 \| 0.0001 \| 1 \| 4.7227 \| \| 5.0736 \| 0.0003 \| 3 \| 4.6863 \| \| 4.0429 \| 0.0006 \| 6 \| 3.8167 \| \| 2.2522 \| 0.0009 \| 9 \| 1.8158 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Prisma-Multimodal/imagenet-sweep-vanilla-x64-Spatial_max_0-hook_resid_post-989.203430175781-99	Prisma-Multimodal	"2025-01-30T08:36:29Z"	10	0	null	[ "region:us" ]	null	"2025-01-30T08:36:17Z"	# CLIP Sparse Autoencoder Checkpoint This model is a sparse autoencoder trained on CLIP's internal representations. ## Model Details ### Architecture - Layer: 0 - Layer Type: hook_resid_post - Model: open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K - Dictionary Size: 49152 - Input Dimension: 768 - Expansion Factor: 64 - CLS Token Only: False ### Training - Training Images: 1299936 - Learning Rate: 0.0006 - L1 Coefficient: 0.0000 - Batch Size: 4096 - Context Size: 49 ## Performance Metrics ### Sparsity - L0 (Active Features): 989.2034 - Dead Features: 0 - Mean Passes Since Fired: 8.6203 ### Reconstruction - Explained Variance: 0.9999 - Explained Variance Std: 0.0004 - MSE Loss: 0.0000 - L1 Loss: 2368.7610 - Overall Loss: 0.0000 ## Training Details - Training Duration: 4395 seconds - Final Learning Rate: 0.0000 - Warm Up Steps: 200 - Gradient Clipping: 1 ## Additional Information - Original Checkpoint Path: /network/scratch/p/praneet.suresh/imgnet_checkpoints/52afe93d-tinyclip_sae_16_hyperparam_sweep_lr/n_images_1300020.pt - Wandb Run: https://wandb.ai/perceptual-alignment/vanilla-imagenet-Spatial_only-012-sweep/runs/ny207vem - Random Seed: 42
dmartincc/vedt-lg	dmartincc	"2025-03-12T17:22:02Z"	32	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-06-27T15:29:14Z"	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - f1 - accuracy model-index: - name: vedt-lg results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: F1 type: f1 value: 0.93 - name: Accuracy type: accuracy value: 0.92 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vedt-lg This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1817 - F1: 0.93 - Roc Auc: 0.95 - Accuracy: 0.92 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----:\|:-------:\|:--------:\| \| 0.5369 \| 1.0 \| 122 \| 0.5339 \| 0.53 \| 0.67 \| 0.41 \| \| 0.3995 \| 2.0 \| 245 \| 0.3591 \| 0.8 \| 0.84 \| 0.73 \| \| 0.2357 \| 3.0 \| 367 \| 0.2492 \| 0.89 \| 0.92 \| 0.88 \| \| 0.1409 \| 4.0 \| 490 \| 0.2015 \| 0.91 \| 0.93 \| 0.9 \| \| 0.1137 \| 4.98 \| 610 \| 0.1817 \| 0.93 \| 0.95 \| 0.92 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1
mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF	mradermacher	"2025-01-28T10:44:50Z"	550	0	transformers	[ "transformers", "gguf", "en", "dataset:pankajmathur/orca_mini_v1_dataset", "dataset:pankajmathur/orca_mini_v8_sharegpt_format", "base_model:pankajmathur/orca_mini_v9_5_1B-Instruct_preview", "base_model:quantized:pankajmathur/orca_mini_v9_5_1B-Instruct_preview", "license:llama3.2", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-01-28T10:05:29Z"	--- base_model: pankajmathur/orca_mini_v9_5_1B-Instruct_preview datasets: - pankajmathur/orca_mini_v1_dataset - pankajmathur/orca_mini_v8_sharegpt_format language: - en library_name: transformers license: llama3.2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/pankajmathur/orca_mini_v9_5_1B-Instruct_preview <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.5 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q2_K.gguf) \| i1-Q2_K \| 0.7 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.9 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.9 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q6_K.gguf) \| i1-Q6_K \| 1.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Fragko/qwen2-VL-7b-instruct-leaves-from-field-diagnosis	Fragko	"2025-03-12T14:19:25Z"	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-VL-2B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-2B-Instruct", "endpoints_compatible", "region:us" ]	null	"2025-03-12T00:56:36Z"	--- base_model: Qwen/Qwen2-VL-2B-Instruct library_name: transformers model_name: qwen2-VL-7b-instruct-leaves-from-field-diagnosis tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-VL-7b-instruct-leaves-from-field-diagnosis This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Fragko/qwen2-VL-7b-instruct-leaves-from-field-diagnosis", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gfragko-technical-university-of-crete/qwen2-VL-7b-instruct-leaves-from-field-diagnosis/runs/anfp2n14) This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.50.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.3.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t75_e35_non_member_shadow11	FounderOfHuggingface	"2023-12-07T12:01:09Z"	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	"2023-12-07T12:01:07Z"	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2
MayBashendy/ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run2_AugV5_k7_task1_organization	MayBashendy	"2025-01-14T19:11:01Z"	7	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-01-14T18:57:36Z"	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run2_AugV5_k7_task1_organization results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run2_AugV5_k7_task1_organization This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0657 - Qwk: 0.5355 - Mse: 1.0657 - Rmse: 1.0323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.0571 \| 2 \| 5.3430 \| -0.0131 \| 5.3430 \| 2.3115 \| \| No log \| 0.1143 \| 4 \| 3.1272 \| 0.0649 \| 3.1272 \| 1.7684 \| \| No log \| 0.1714 \| 6 \| 2.6556 \| -0.1003 \| 2.6556 \| 1.6296 \| \| No log \| 0.2286 \| 8 \| 2.4195 \| -0.1156 \| 2.4195 \| 1.5555 \| \| No log \| 0.2857 \| 10 \| 1.7140 \| 0.0267 \| 1.7140 \| 1.3092 \| \| No log \| 0.3429 \| 12 \| 1.2703 \| 0.1423 \| 1.2703 \| 1.1271 \| \| No log \| 0.4 \| 14 \| 1.2489 \| 0.2203 \| 1.2489 \| 1.1175 \| \| No log \| 0.4571 \| 16 \| 1.1863 \| 0.2751 \| 1.1863 \| 1.0892 \| \| No log \| 0.5143 \| 18 \| 1.2975 \| 0.1746 \| 1.2975 \| 1.1391 \| \| No log \| 0.5714 \| 20 \| 1.3932 \| 0.1370 \| 1.3932 \| 1.1803 \| \| No log \| 0.6286 \| 22 \| 1.3680 \| 0.1204 \| 1.3680 \| 1.1696 \| \| No log \| 0.6857 \| 24 \| 1.2413 \| 0.3295 \| 1.2413 \| 1.1141 \| \| No log \| 0.7429 \| 26 \| 1.1591 \| 0.3779 \| 1.1591 \| 1.0766 \| \| No log \| 0.8 \| 28 \| 1.2529 \| 0.2372 \| 1.2529 \| 1.1193 \| \| No log \| 0.8571 \| 30 \| 1.1474 \| 0.3339 \| 1.1474 \| 1.0712 \| \| No log \| 0.9143 \| 32 \| 1.1505 \| 0.3121 \| 1.1505 \| 1.0726 \| \| No log \| 0.9714 \| 34 \| 1.1191 \| 0.3563 \| 1.1191 \| 1.0579 \| \| No log \| 1.0286 \| 36 \| 1.1045 \| 0.3838 \| 1.1045 \| 1.0510 \| \| No log \| 1.0857 \| 38 \| 1.0767 \| 0.3972 \| 1.0767 \| 1.0376 \| \| No log \| 1.1429 \| 40 \| 1.0334 \| 0.3862 \| 1.0334 \| 1.0166 \| \| No log \| 1.2 \| 42 \| 0.8723 \| 0.4907 \| 0.8723 \| 0.9340 \| \| No log \| 1.2571 \| 44 \| 0.8736 \| 0.5391 \| 0.8736 \| 0.9347 \| \| No log \| 1.3143 \| 46 \| 0.8995 \| 0.5487 \| 0.8995 \| 0.9484 \| \| No log \| 1.3714 \| 48 \| 0.9249 \| 0.5679 \| 0.9249 \| 0.9617 \| \| No log \| 1.4286 \| 50 \| 0.9743 \| 0.5092 \| 0.9743 \| 0.9871 \| \| No log \| 1.4857 \| 52 \| 0.9736 \| 0.4893 \| 0.9736 \| 0.9867 \| \| No log \| 1.5429 \| 54 \| 0.9967 \| 0.4736 \| 0.9967 \| 0.9984 \| \| No log \| 1.6 \| 56 \| 0.9438 \| 0.5147 \| 0.9438 \| 0.9715 \| \| No log \| 1.6571 \| 58 \| 1.0154 \| 0.5401 \| 1.0154 \| 1.0077 \| \| No log \| 1.7143 \| 60 \| 0.9127 \| 0.5038 \| 0.9127 \| 0.9554 \| \| No log \| 1.7714 \| 62 \| 1.0786 \| 0.5033 \| 1.0786 \| 1.0385 \| \| No log \| 1.8286 \| 64 \| 1.0134 \| 0.5134 \| 1.0134 \| 1.0067 \| \| No log \| 1.8857 \| 66 \| 0.8451 \| 0.5707 \| 0.8451 \| 0.9193 \| \| No log \| 1.9429 \| 68 \| 0.9047 \| 0.5867 \| 0.9047 \| 0.9512 \| \| No log \| 2.0 \| 70 \| 0.8410 \| 0.6429 \| 0.8410 \| 0.9171 \| \| No log \| 2.0571 \| 72 \| 0.8599 \| 0.5858 \| 0.8599 \| 0.9273 \| \| No log \| 2.1143 \| 74 \| 1.0333 \| 0.5201 \| 1.0333 \| 1.0165 \| \| No log \| 2.1714 \| 76 \| 0.9665 \| 0.5218 \| 0.9665 \| 0.9831 \| \| No log \| 2.2286 \| 78 \| 0.8244 \| 0.5851 \| 0.8244 \| 0.9080 \| \| No log \| 2.2857 \| 80 \| 0.8443 \| 0.5752 \| 0.8443 \| 0.9189 \| \| No log \| 2.3429 \| 82 \| 0.8400 \| 0.5657 \| 0.8400 \| 0.9165 \| \| No log \| 2.4 \| 84 \| 0.9719 \| 0.5372 \| 0.9719 \| 0.9858 \| \| No log \| 2.4571 \| 86 \| 0.9467 \| 0.5528 \| 0.9467 \| 0.9730 \| \| No log \| 2.5143 \| 88 \| 0.7803 \| 0.6361 \| 0.7803 \| 0.8834 \| \| No log \| 2.5714 \| 90 \| 0.7549 \| 0.6035 \| 0.7549 \| 0.8688 \| \| No log \| 2.6286 \| 92 \| 0.8418 \| 0.5549 \| 0.8418 \| 0.9175 \| \| No log \| 2.6857 \| 94 \| 0.7118 \| 0.6245 \| 0.7118 \| 0.8437 \| \| No log \| 2.7429 \| 96 \| 0.9979 \| 0.5331 \| 0.9979 \| 0.9989 \| \| No log \| 2.8 \| 98 \| 1.3117 \| 0.4883 \| 1.3117 \| 1.1453 \| \| No log \| 2.8571 \| 100 \| 1.0906 \| 0.5105 \| 1.0906 \| 1.0443 \| \| No log \| 2.9143 \| 102 \| 0.6861 \| 0.6388 \| 0.6861 \| 0.8283 \| \| No log \| 2.9714 \| 104 \| 0.7704 \| 0.5976 \| 0.7704 \| 0.8777 \| \| No log \| 3.0286 \| 106 \| 0.7716 \| 0.6420 \| 0.7716 \| 0.8784 \| \| No log \| 3.0857 \| 108 \| 0.6576 \| 0.6805 \| 0.6576 \| 0.8109 \| \| No log \| 3.1429 \| 110 \| 0.8588 \| 0.5971 \| 0.8588 \| 0.9267 \| \| No log \| 3.2 \| 112 \| 1.0898 \| 0.5213 \| 1.0898 \| 1.0439 \| \| No log \| 3.2571 \| 114 \| 1.1132 \| 0.5205 \| 1.1132 \| 1.0551 \| \| No log \| 3.3143 \| 116 \| 1.0380 \| 0.5333 \| 1.0380 \| 1.0188 \| \| No log \| 3.3714 \| 118 \| 1.0357 \| 0.5401 \| 1.0357 \| 1.0177 \| \| No log \| 3.4286 \| 120 \| 0.9567 \| 0.6132 \| 0.9567 \| 0.9781 \| \| No log \| 3.4857 \| 122 \| 1.0190 \| 0.5522 \| 1.0190 \| 1.0095 \| \| No log \| 3.5429 \| 124 \| 1.2592 \| 0.4356 \| 1.2592 \| 1.1222 \| \| No log \| 3.6 \| 126 \| 1.1839 \| 0.4543 \| 1.1839 \| 1.0881 \| \| No log \| 3.6571 \| 128 \| 1.0883 \| 0.4830 \| 1.0883 \| 1.0432 \| \| No log \| 3.7143 \| 130 \| 1.0148 \| 0.5338 \| 1.0148 \| 1.0074 \| \| No log \| 3.7714 \| 132 \| 0.7750 \| 0.6389 \| 0.7750 \| 0.8803 \| \| No log \| 3.8286 \| 134 \| 0.7688 \| 0.6161 \| 0.7688 \| 0.8768 \| \| No log \| 3.8857 \| 136 \| 0.7777 \| 0.6601 \| 0.7777 \| 0.8819 \| \| No log \| 3.9429 \| 138 \| 0.9604 \| 0.6227 \| 0.9604 \| 0.9800 \| \| No log \| 4.0 \| 140 \| 1.2622 \| 0.5311 \| 1.2622 \| 1.1235 \| \| No log \| 4.0571 \| 142 \| 1.2558 \| 0.5144 \| 1.2558 \| 1.1206 \| \| No log \| 4.1143 \| 144 \| 1.0652 \| 0.5312 \| 1.0652 \| 1.0321 \| \| No log \| 4.1714 \| 146 \| 0.8674 \| 0.6355 \| 0.8674 \| 0.9313 \| \| No log \| 4.2286 \| 148 \| 0.7753 \| 0.6502 \| 0.7753 \| 0.8805 \| \| No log \| 4.2857 \| 150 \| 0.8393 \| 0.6358 \| 0.8393 \| 0.9161 \| \| No log \| 4.3429 \| 152 \| 0.8959 \| 0.6019 \| 0.8959 \| 0.9465 \| \| No log \| 4.4 \| 154 \| 0.8972 \| 0.5988 \| 0.8972 \| 0.9472 \| \| No log \| 4.4571 \| 156 \| 0.7496 \| 0.6873 \| 0.7496 \| 0.8658 \| \| No log \| 4.5143 \| 158 \| 0.7267 \| 0.6988 \| 0.7267 \| 0.8524 \| \| No log \| 4.5714 \| 160 \| 0.7081 \| 0.6854 \| 0.7081 \| 0.8415 \| \| No log \| 4.6286 \| 162 \| 0.7194 \| 0.6824 \| 0.7194 \| 0.8481 \| \| No log \| 4.6857 \| 164 \| 0.8391 \| 0.6738 \| 0.8391 \| 0.9160 \| \| No log \| 4.7429 \| 166 \| 1.1286 \| 0.5640 \| 1.1286 \| 1.0623 \| \| No log \| 4.8 \| 168 \| 1.0770 \| 0.5831 \| 1.0770 \| 1.0378 \| \| No log \| 4.8571 \| 170 \| 0.8046 \| 0.6916 \| 0.8046 \| 0.8970 \| \| No log \| 4.9143 \| 172 \| 0.7048 \| 0.6692 \| 0.7048 \| 0.8395 \| \| No log \| 4.9714 \| 174 \| 0.7162 \| 0.6755 \| 0.7162 \| 0.8463 \| \| No log \| 5.0286 \| 176 \| 0.7766 \| 0.6634 \| 0.7766 \| 0.8812 \| \| No log \| 5.0857 \| 178 \| 0.9326 \| 0.6175 \| 0.9326 \| 0.9657 \| \| No log \| 5.1429 \| 180 \| 1.0926 \| 0.5442 \| 1.0926 \| 1.0453 \| \| No log \| 5.2 \| 182 \| 0.9450 \| 0.6135 \| 0.9450 \| 0.9721 \| \| No log \| 5.2571 \| 184 \| 0.7525 \| 0.6745 \| 0.7525 \| 0.8674 \| \| No log \| 5.3143 \| 186 \| 0.7507 \| 0.6302 \| 0.7507 \| 0.8664 \| \| No log \| 5.3714 \| 188 \| 0.7302 \| 0.6167 \| 0.7302 \| 0.8545 \| \| No log \| 5.4286 \| 190 \| 0.7342 \| 0.6394 \| 0.7342 \| 0.8568 \| \| No log \| 5.4857 \| 192 \| 0.8565 \| 0.6562 \| 0.8565 \| 0.9255 \| \| No log \| 5.5429 \| 194 \| 0.9119 \| 0.5911 \| 0.9119 \| 0.9550 \| \| No log \| 5.6 \| 196 \| 0.9576 \| 0.5911 \| 0.9576 \| 0.9786 \| \| No log \| 5.6571 \| 198 \| 0.8575 \| 0.6347 \| 0.8575 \| 0.9260 \| \| No log \| 5.7143 \| 200 \| 0.7727 \| 0.6947 \| 0.7727 \| 0.8790 \| \| No log \| 5.7714 \| 202 \| 0.7947 \| 0.6674 \| 0.7947 \| 0.8915 \| \| No log \| 5.8286 \| 204 \| 0.9209 \| 0.6373 \| 0.9209 \| 0.9597 \| \| No log \| 5.8857 \| 206 \| 1.0566 \| 0.6122 \| 1.0566 \| 1.0279 \| \| No log \| 5.9429 \| 208 \| 0.9640 \| 0.6463 \| 0.9640 \| 0.9819 \| \| No log \| 6.0 \| 210 \| 0.8122 \| 0.6719 \| 0.8122 \| 0.9012 \| \| No log \| 6.0571 \| 212 \| 0.7881 \| 0.6924 \| 0.7881 \| 0.8877 \| \| No log \| 6.1143 \| 214 \| 0.8804 \| 0.6246 \| 0.8804 \| 0.9383 \| \| No log \| 6.1714 \| 216 \| 0.9973 \| 0.5694 \| 0.9973 \| 0.9986 \| \| No log \| 6.2286 \| 218 \| 0.9968 \| 0.5608 \| 0.9968 \| 0.9984 \| \| No log \| 6.2857 \| 220 \| 0.8269 \| 0.6476 \| 0.8269 \| 0.9093 \| \| No log \| 6.3429 \| 222 \| 0.7584 \| 0.6718 \| 0.7584 \| 0.8708 \| \| No log \| 6.4 \| 224 \| 0.6644 \| 0.6765 \| 0.6644 \| 0.8151 \| \| No log \| 6.4571 \| 226 \| 0.6248 \| 0.6804 \| 0.6248 \| 0.7905 \| \| No log \| 6.5143 \| 228 \| 0.7130 \| 0.6969 \| 0.7130 \| 0.8444 \| \| No log \| 6.5714 \| 230 \| 0.9473 \| 0.6137 \| 0.9473 \| 0.9733 \| \| No log \| 6.6286 \| 232 \| 0.9530 \| 0.6280 \| 0.9530 \| 0.9762 \| \| No log \| 6.6857 \| 234 \| 0.7536 \| 0.7182 \| 0.7536 \| 0.8681 \| \| No log \| 6.7429 \| 236 \| 0.7443 \| 0.7166 \| 0.7443 \| 0.8627 \| \| No log \| 6.8 \| 238 \| 0.8401 \| 0.6413 \| 0.8401 \| 0.9166 \| \| No log \| 6.8571 \| 240 \| 0.9515 \| 0.6228 \| 0.9515 \| 0.9754 \| \| No log \| 6.9143 \| 242 \| 0.8301 \| 0.6694 \| 0.8301 \| 0.9111 \| \| No log \| 6.9714 \| 244 \| 0.7428 \| 0.6982 \| 0.7428 \| 0.8619 \| \| No log \| 7.0286 \| 246 \| 0.8660 \| 0.6178 \| 0.8660 \| 0.9306 \| \| No log \| 7.0857 \| 248 \| 0.9069 \| 0.5964 \| 0.9069 \| 0.9523 \| \| No log \| 7.1429 \| 250 \| 0.9427 \| 0.6036 \| 0.9427 \| 0.9709 \| \| No log \| 7.2 \| 252 \| 0.8597 \| 0.6288 \| 0.8597 \| 0.9272 \| \| No log \| 7.2571 \| 254 \| 0.8272 \| 0.6288 \| 0.8272 \| 0.9095 \| \| No log \| 7.3143 \| 256 \| 0.9297 \| 0.6086 \| 0.9297 \| 0.9642 \| \| No log \| 7.3714 \| 258 \| 0.8701 \| 0.6293 \| 0.8701 \| 0.9328 \| \| No log \| 7.4286 \| 260 \| 0.7681 \| 0.6944 \| 0.7681 \| 0.8764 \| \| No log \| 7.4857 \| 262 \| 0.7007 \| 0.7263 \| 0.7007 \| 0.8371 \| \| No log \| 7.5429 \| 264 \| 0.6556 \| 0.7099 \| 0.6556 \| 0.8097 \| \| No log \| 7.6 \| 266 \| 0.6917 \| 0.7006 \| 0.6917 \| 0.8317 \| \| No log \| 7.6571 \| 268 \| 0.8437 \| 0.6470 \| 0.8437 \| 0.9185 \| \| No log \| 7.7143 \| 270 \| 0.8517 \| 0.6517 \| 0.8517 \| 0.9229 \| \| No log \| 7.7714 \| 272 \| 0.7675 \| 0.7046 \| 0.7675 \| 0.8761 \| \| No log \| 7.8286 \| 274 \| 0.7646 \| 0.7054 \| 0.7646 \| 0.8744 \| \| No log \| 7.8857 \| 276 \| 0.7847 \| 0.6628 \| 0.7847 \| 0.8858 \| \| No log \| 7.9429 \| 278 \| 0.8038 \| 0.6628 \| 0.8038 \| 0.8965 \| \| No log \| 8.0 \| 280 \| 0.7983 \| 0.6527 \| 0.7983 \| 0.8935 \| \| No log \| 8.0571 \| 282 \| 0.7692 \| 0.6788 \| 0.7692 \| 0.8770 \| \| No log \| 8.1143 \| 284 \| 0.7306 \| 0.6965 \| 0.7306 \| 0.8548 \| \| No log \| 8.1714 \| 286 \| 0.7067 \| 0.6590 \| 0.7067 \| 0.8406 \| \| No log \| 8.2286 \| 288 \| 0.7578 \| 0.6885 \| 0.7578 \| 0.8705 \| \| No log \| 8.2857 \| 290 \| 0.8385 \| 0.6512 \| 0.8385 \| 0.9157 \| \| No log \| 8.3429 \| 292 \| 0.9729 \| 0.5842 \| 0.9729 \| 0.9864 \| \| No log \| 8.4 \| 294 \| 0.8527 \| 0.6420 \| 0.8527 \| 0.9234 \| \| No log \| 8.4571 \| 296 \| 0.7301 \| 0.6967 \| 0.7301 \| 0.8545 \| \| No log \| 8.5143 \| 298 \| 0.6768 \| 0.6643 \| 0.6768 \| 0.8227 \| \| No log \| 8.5714 \| 300 \| 0.6640 \| 0.6516 \| 0.6640 \| 0.8149 \| \| No log \| 8.6286 \| 302 \| 0.6898 \| 0.6664 \| 0.6898 \| 0.8306 \| \| No log \| 8.6857 \| 304 \| 0.8909 \| 0.6159 \| 0.8909 \| 0.9439 \| \| No log \| 8.7429 \| 306 \| 1.1835 \| 0.4966 \| 1.1835 \| 1.0879 \| \| No log \| 8.8 \| 308 \| 1.2569 \| 0.4960 \| 1.2569 \| 1.1211 \| \| No log \| 8.8571 \| 310 \| 1.1361 \| 0.5189 \| 1.1361 \| 1.0659 \| \| No log \| 8.9143 \| 312 \| 1.0565 \| 0.5399 \| 1.0565 \| 1.0279 \| \| No log \| 8.9714 \| 314 \| 0.8676 \| 0.6551 \| 0.8676 \| 0.9315 \| \| No log \| 9.0286 \| 316 \| 0.7602 \| 0.6899 \| 0.7602 \| 0.8719 \| \| No log \| 9.0857 \| 318 \| 0.7732 \| 0.6707 \| 0.7732 \| 0.8793 \| \| No log \| 9.1429 \| 320 \| 0.8802 \| 0.5899 \| 0.8802 \| 0.9382 \| \| No log \| 9.2 \| 322 \| 1.0857 \| 0.5209 \| 1.0857 \| 1.0419 \| \| No log \| 9.2571 \| 324 \| 1.1553 \| 0.5265 \| 1.1553 \| 1.0748 \| \| No log \| 9.3143 \| 326 \| 1.0771 \| 0.5189 \| 1.0771 \| 1.0378 \| \| No log \| 9.3714 \| 328 \| 1.0802 \| 0.5209 \| 1.0802 \| 1.0393 \| \| No log \| 9.4286 \| 330 \| 0.9214 \| 0.5783 \| 0.9214 \| 0.9599 \| \| No log \| 9.4857 \| 332 \| 0.8058 \| 0.6211 \| 0.8058 \| 0.8977 \| \| No log \| 9.5429 \| 334 \| 0.7409 \| 0.6878 \| 0.7409 \| 0.8607 \| \| No log \| 9.6 \| 336 \| 0.7973 \| 0.6509 \| 0.7973 \| 0.8929 \| \| No log \| 9.6571 \| 338 \| 0.9476 \| 0.5867 \| 0.9476 \| 0.9735 \| \| No log \| 9.7143 \| 340 \| 0.8953 \| 0.5806 \| 0.8953 \| 0.9462 \| \| No log \| 9.7714 \| 342 \| 0.7900 \| 0.6858 \| 0.7900 \| 0.8888 \| \| No log \| 9.8286 \| 344 \| 0.7239 \| 0.6774 \| 0.7239 \| 0.8508 \| \| No log \| 9.8857 \| 346 \| 0.7151 \| 0.6791 \| 0.7151 \| 0.8457 \| \| No log \| 9.9429 \| 348 \| 0.7722 \| 0.6819 \| 0.7722 \| 0.8787 \| \| No log \| 10.0 \| 350 \| 0.9402 \| 0.6286 \| 0.9402 \| 0.9696 \| \| No log \| 10.0571 \| 352 \| 1.0030 \| 0.6179 \| 1.0030 \| 1.0015 \| \| No log \| 10.1143 \| 354 \| 0.8716 \| 0.6648 \| 0.8716 \| 0.9336 \| \| No log \| 10.1714 \| 356 \| 0.7326 \| 0.7014 \| 0.7326 \| 0.8559 \| \| No log \| 10.2286 \| 358 \| 0.7226 \| 0.6905 \| 0.7226 \| 0.8501 \| \| No log \| 10.2857 \| 360 \| 0.7907 \| 0.6880 \| 0.7907 \| 0.8892 \| \| No log \| 10.3429 \| 362 \| 0.9463 \| 0.5667 \| 0.9463 \| 0.9728 \| \| No log \| 10.4 \| 364 \| 1.0349 \| 0.5381 \| 1.0349 \| 1.0173 \| \| No log \| 10.4571 \| 366 \| 0.9543 \| 0.5932 \| 0.9543 \| 0.9769 \| \| No log \| 10.5143 \| 368 \| 0.8436 \| 0.6396 \| 0.8436 \| 0.9185 \| \| No log \| 10.5714 \| 370 \| 0.8043 \| 0.6596 \| 0.8043 \| 0.8968 \| \| No log \| 10.6286 \| 372 \| 0.7737 \| 0.7008 \| 0.7737 \| 0.8796 \| \| No log \| 10.6857 \| 374 \| 0.8094 \| 0.6618 \| 0.8094 \| 0.8996 \| \| No log \| 10.7429 \| 376 \| 0.7938 \| 0.6666 \| 0.7938 \| 0.8909 \| \| No log \| 10.8 \| 378 \| 0.7080 \| 0.7159 \| 0.7080 \| 0.8414 \| \| No log \| 10.8571 \| 380 \| 0.6661 \| 0.6835 \| 0.6661 \| 0.8162 \| \| No log \| 10.9143 \| 382 \| 0.6601 \| 0.6835 \| 0.6601 \| 0.8125 \| \| No log \| 10.9714 \| 384 \| 0.6558 \| 0.6619 \| 0.6558 \| 0.8098 \| \| No log \| 11.0286 \| 386 \| 0.6875 \| 0.7120 \| 0.6875 \| 0.8291 \| \| No log \| 11.0857 \| 388 \| 0.7488 \| 0.6509 \| 0.7488 \| 0.8653 \| \| No log \| 11.1429 \| 390 \| 0.8062 \| 0.6638 \| 0.8062 \| 0.8979 \| \| No log \| 11.2 \| 392 \| 0.7661 \| 0.6570 \| 0.7661 \| 0.8753 \| \| No log \| 11.2571 \| 394 \| 0.7196 \| 0.7086 \| 0.7196 \| 0.8483 \| \| No log \| 11.3143 \| 396 \| 0.7788 \| 0.6609 \| 0.7788 \| 0.8825 \| \| No log \| 11.3714 \| 398 \| 0.8422 \| 0.6489 \| 0.8422 \| 0.9177 \| \| No log \| 11.4286 \| 400 \| 0.9245 \| 0.6167 \| 0.9245 \| 0.9615 \| \| No log \| 11.4857 \| 402 \| 0.8394 \| 0.6395 \| 0.8394 \| 0.9162 \| \| No log \| 11.5429 \| 404 \| 0.8095 \| 0.6505 \| 0.8095 \| 0.8997 \| \| No log \| 11.6 \| 406 \| 0.7775 \| 0.6481 \| 0.7775 \| 0.8818 \| \| No log \| 11.6571 \| 408 \| 0.8326 \| 0.6582 \| 0.8326 \| 0.9125 \| \| No log \| 11.7143 \| 410 \| 0.7396 \| 0.7004 \| 0.7396 \| 0.8600 \| \| No log \| 11.7714 \| 412 \| 0.6857 \| 0.7117 \| 0.6857 \| 0.8280 \| \| No log \| 11.8286 \| 414 \| 0.7590 \| 0.7058 \| 0.7590 \| 0.8712 \| \| No log \| 11.8857 \| 416 \| 0.9415 \| 0.6233 \| 0.9415 \| 0.9703 \| \| No log \| 11.9429 \| 418 \| 1.0937 \| 0.6062 \| 1.0937 \| 1.0458 \| \| No log \| 12.0 \| 420 \| 1.0981 \| 0.5797 \| 1.0981 \| 1.0479 \| \| No log \| 12.0571 \| 422 \| 1.0543 \| 0.5637 \| 1.0543 \| 1.0268 \| \| No log \| 12.1143 \| 424 \| 1.0139 \| 0.5377 \| 1.0139 \| 1.0069 \| \| No log \| 12.1714 \| 426 \| 0.9686 \| 0.5629 \| 0.9686 \| 0.9842 \| \| No log \| 12.2286 \| 428 \| 1.0128 \| 0.5138 \| 1.0128 \| 1.0064 \| \| No log \| 12.2857 \| 430 \| 1.0498 \| 0.5128 \| 1.0498 \| 1.0246 \| \| No log \| 12.3429 \| 432 \| 0.8833 \| 0.6268 \| 0.8833 \| 0.9398 \| \| No log \| 12.4 \| 434 \| 0.7097 \| 0.6949 \| 0.7097 \| 0.8424 \| \| No log \| 12.4571 \| 436 \| 0.6709 \| 0.6879 \| 0.6709 \| 0.8191 \| \| No log \| 12.5143 \| 438 \| 0.6722 \| 0.6978 \| 0.6722 \| 0.8198 \| \| No log \| 12.5714 \| 440 \| 0.7612 \| 0.7090 \| 0.7612 \| 0.8725 \| \| No log \| 12.6286 \| 442 \| 0.9488 \| 0.6221 \| 0.9488 \| 0.9741 \| \| No log \| 12.6857 \| 444 \| 0.9146 \| 0.6440 \| 0.9146 \| 0.9563 \| \| No log \| 12.7429 \| 446 \| 0.7522 \| 0.6964 \| 0.7522 \| 0.8673 \| \| No log \| 12.8 \| 448 \| 0.6023 \| 0.6598 \| 0.6023 \| 0.7761 \| \| No log \| 12.8571 \| 450 \| 0.6168 \| 0.6413 \| 0.6168 \| 0.7854 \| \| No log \| 12.9143 \| 452 \| 0.6096 \| 0.6681 \| 0.6096 \| 0.7808 \| \| No log \| 12.9714 \| 454 \| 0.6104 \| 0.6865 \| 0.6104 \| 0.7813 \| \| No log \| 13.0286 \| 456 \| 0.6612 \| 0.7128 \| 0.6612 \| 0.8131 \| \| No log \| 13.0857 \| 458 \| 0.6447 \| 0.7036 \| 0.6447 \| 0.8029 \| \| No log \| 13.1429 \| 460 \| 0.6339 \| 0.7079 \| 0.6339 \| 0.7962 \| \| No log \| 13.2 \| 462 \| 0.6410 \| 0.6216 \| 0.6410 \| 0.8006 \| \| No log \| 13.2571 \| 464 \| 0.6306 \| 0.6425 \| 0.6306 \| 0.7941 \| \| No log \| 13.3143 \| 466 \| 0.6213 \| 0.6835 \| 0.6213 \| 0.7882 \| \| No log \| 13.3714 \| 468 \| 0.6497 \| 0.7204 \| 0.6497 \| 0.8061 \| \| No log \| 13.4286 \| 470 \| 0.7284 \| 0.6667 \| 0.7284 \| 0.8535 \| \| No log \| 13.4857 \| 472 \| 0.7837 \| 0.6530 \| 0.7837 \| 0.8853 \| \| No log \| 13.5429 \| 474 \| 0.7644 \| 0.6530 \| 0.7644 \| 0.8743 \| \| No log \| 13.6 \| 476 \| 0.7373 \| 0.6657 \| 0.7373 \| 0.8586 \| \| No log \| 13.6571 \| 478 \| 0.7246 \| 0.6857 \| 0.7246 \| 0.8512 \| \| No log \| 13.7143 \| 480 \| 0.6883 \| 0.6970 \| 0.6883 \| 0.8297 \| \| No log \| 13.7714 \| 482 \| 0.6774 \| 0.7007 \| 0.6774 \| 0.8230 \| \| No log \| 13.8286 \| 484 \| 0.7164 \| 0.7116 \| 0.7164 \| 0.8464 \| \| No log \| 13.8857 \| 486 \| 0.7260 \| 0.7116 \| 0.7260 \| 0.8521 \| \| No log \| 13.9429 \| 488 \| 0.7837 \| 0.6726 \| 0.7837 \| 0.8852 \| \| No log \| 14.0 \| 490 \| 0.8038 \| 0.6715 \| 0.8038 \| 0.8965 \| \| No log \| 14.0571 \| 492 \| 0.8259 \| 0.6629 \| 0.8259 \| 0.9088 \| \| No log \| 14.1143 \| 494 \| 0.7970 \| 0.6973 \| 0.7970 \| 0.8928 \| \| No log \| 14.1714 \| 496 \| 0.7024 \| 0.7072 \| 0.7024 \| 0.8381 \| \| No log \| 14.2286 \| 498 \| 0.7095 \| 0.6971 \| 0.7095 \| 0.8423 \| \| 0.3637 \| 14.2857 \| 500 \| 0.7234 \| 0.6954 \| 0.7234 \| 0.8505 \| \| 0.3637 \| 14.3429 \| 502 \| 0.7055 \| 0.7079 \| 0.7055 \| 0.8399 \| \| 0.3637 \| 14.4 \| 504 \| 0.7237 \| 0.7030 \| 0.7237 \| 0.8507 \| \| 0.3637 \| 14.4571 \| 506 \| 0.7712 \| 0.6960 \| 0.7712 \| 0.8782 \| \| 0.3637 \| 14.5143 \| 508 \| 0.7300 \| 0.6960 \| 0.7300 \| 0.8544 \| \| 0.3637 \| 14.5714 \| 510 \| 0.6766 \| 0.7263 \| 0.6766 \| 0.8226 \| \| 0.3637 \| 14.6286 \| 512 \| 0.6835 \| 0.7247 \| 0.6835 \| 0.8268 \| \| 0.3637 \| 14.6857 \| 514 \| 0.7758 \| 0.6851 \| 0.7758 \| 0.8808 \| \| 0.3637 \| 14.7429 \| 516 \| 0.8551 \| 0.6773 \| 0.8551 \| 0.9247 \| \| 0.3637 \| 14.8 \| 518 \| 0.8200 \| 0.6915 \| 0.8200 \| 0.9055 \| \| 0.3637 \| 14.8571 \| 520 \| 0.7410 \| 0.6865 \| 0.7410 \| 0.8608 \| \| 0.3637 \| 14.9143 \| 522 \| 0.6627 \| 0.6997 \| 0.6627 \| 0.8140 \| \| 0.3637 \| 14.9714 \| 524 \| 0.6722 \| 0.7049 \| 0.6722 \| 0.8199 \| \| 0.3637 \| 15.0286 \| 526 \| 0.7373 \| 0.6982 \| 0.7373 \| 0.8587 \| \| 0.3637 \| 15.0857 \| 528 \| 0.8253 \| 0.7017 \| 0.8253 \| 0.9085 \| \| 0.3637 \| 15.1429 \| 530 \| 0.8180 \| 0.6814 \| 0.8180 \| 0.9044 \| \| 0.3637 \| 15.2 \| 532 \| 0.7457 \| 0.6816 \| 0.7457 \| 0.8636 \| \| 0.3637 \| 15.2571 \| 534 \| 0.7131 \| 0.7012 \| 0.7131 \| 0.8445 \| \| 0.3637 \| 15.3143 \| 536 \| 0.7075 \| 0.7152 \| 0.7075 \| 0.8411 \| \| 0.3637 \| 15.3714 \| 538 \| 0.6597 \| 0.7086 \| 0.6597 \| 0.8122 \| \| 0.3637 \| 15.4286 \| 540 \| 0.6856 \| 0.7224 \| 0.6856 \| 0.8280 \| \| 0.3637 \| 15.4857 \| 542 \| 0.7557 \| 0.6901 \| 0.7557 \| 0.8693 \| \| 0.3637 \| 15.5429 \| 544 \| 0.9779 \| 0.6161 \| 0.9779 \| 0.9889 \| \| 0.3637 \| 15.6 \| 546 \| 1.1443 \| 0.5428 \| 1.1443 \| 1.0697 \| \| 0.3637 \| 15.6571 \| 548 \| 1.1605 \| 0.5411 \| 1.1605 \| 1.0773 \| \| 0.3637 \| 15.7143 \| 550 \| 1.0657 \| 0.5355 \| 1.0657 \| 1.0323 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
texanrangee/ca8a279b-d498-45c6-b1c3-e8a347ae9c4a	texanrangee	"2025-03-15T12:05:23Z"	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-03-15T10:08:43Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Manuel221097/dije	Manuel221097	"2024-04-11T14:06:37Z"	0	0	null	[ "license:cc-by-nc-sa-2.0", "region:us" ]	null	"2024-04-11T14:06:36Z"	--- license: cc-by-nc-sa-2.0 ---
AndyJamesTurner/suicideDetector	AndyJamesTurner	"2024-04-17T13:49:57Z"	0	0	sklearn	[ "sklearn", "skops", "text-classification", "license:mit", "region:us" ]	text-classification	"2024-04-12T10:08:45Z"	--- license: mit library_name: sklearn tags: - sklearn - skops - text-classification model_format: pickle model_file: model.pkl --- # Model description Suicide Detection text classification model. PYTHON 3.10 ONLY ## Training Procedure Trained using 0.7 of the the Suicide and Depression Detection dataset (https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch) The model vectorises each text using a trained tfidf vectorizer and then classifies using xgboost. See main.py for further details. ### Hyperparameters <details> <summary> Click to expand </summary> \| Hyperparameter \| Value \| \|-------------------------------------\|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| memory \| \| \| steps \| [('tfidf', TfidfVectorizer(min_df=100, ngram_range=(1, 3),<br /> preprocessor=<function preprocessor at 0x7f8d443a30a0>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=None, n_jobs=None,<br /> num_parallel_tree=None, random_state=None, ...))] \| \| verbose \| True \| \| tfidf \| TfidfVectorizer(min_df=100, ngram_range=(1, 3),<br /> preprocessor=<function preprocessor at 0x7f8d443a30a0>) \| \| classifier \| XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=None, n_jobs=None,<br /> num_parallel_tree=None, random_state=None, ...) \| \| tfidf__analyzer \| word \| \| tfidf__binary \| False \| \| tfidf__decode_error \| strict \| \| tfidf__dtype \| <class 'numpy.float64'> \| \| tfidf__encoding \| utf-8 \| \| tfidf__input \| content \| \| tfidf__lowercase \| True \| \| tfidf__max_df \| 1.0 \| \| tfidf__max_features \| \| \| tfidf__min_df \| 100 \| \| tfidf__ngram_range \| (1, 3) \| \| tfidf__norm \| l2 \| \| tfidf__preprocessor \| <function preprocessor at 0x7f8d443a30a0> \| \| tfidf__smooth_idf \| True \| \| tfidf__stop_words \| \| \| tfidf__strip_accents \| \| \| tfidf__sublinear_tf \| False \| \| tfidf__token_pattern \| (?u)\b\w\w+\b \| \| tfidf__tokenizer \| \| \| tfidf__use_idf \| True \| \| tfidf__vocabulary \| \| \| classifier__objective \| binary:logistic \| \| classifier__base_score \| \| \| classifier__booster \| \| \| classifier__callbacks \| \| \| classifier__colsample_bylevel \| \| \| classifier__colsample_bynode \| \| \| classifier__colsample_bytree \| \| \| classifier__device \| \| \| classifier__early_stopping_rounds \| \| \| classifier__enable_categorical \| False \| \| classifier__eval_metric \| \| \| classifier__feature_types \| \| \| classifier__gamma \| \| \| classifier__grow_policy \| \| \| classifier__importance_type \| \| \| classifier__interaction_constraints \| \| \| classifier__learning_rate \| \| \| classifier__max_bin \| \| \| classifier__max_cat_threshold \| \| \| classifier__max_cat_to_onehot \| \| \| classifier__max_delta_step \| \| \| classifier__max_depth \| \| \| classifier__max_leaves \| \| \| classifier__min_child_weight \| \| \| classifier__missing \| nan \| \| classifier__monotone_constraints \| \| \| classifier__multi_strategy \| \| \| classifier__n_estimators \| \| \| classifier__n_jobs \| \| \| classifier__num_parallel_tree \| \| \| classifier__random_state \| \| \| classifier__reg_alpha \| \| \| classifier__reg_lambda \| \| \| classifier__sampling_method \| \| \| classifier__scale_pos_weight \| \| \| classifier__subsample \| \| \| classifier__tree_method \| \| \| classifier__validate_parameters \| \| \| classifier__verbosity \| \| </details> ### Model Plot <style>#sk-container-id-1 {/* Definition of color scheme common for light and dark mode /--sklearn-color-text: black;--sklearn-color-line: gray;/ Definition of color scheme for unfitted estimators /--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/ Definition of color scheme for fitted estimators /--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/ Specific color for light theme /--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/ Redefinition of color scheme for dark theme /--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;} }#sk-container-id-1 {color: var(--sklearn-color-text); }#sk-container-id-1 pre {padding: 0; }#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; }#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); }#sk-container-id-1 div.sk-container {/ jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative; }#sk-container-id-1 div.sk-text-repr-fallback {display: none; }div.sk-parallel-item, div.sk-serial, div.sk-item {/ draw centered vertical line to link estimators /background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; }/ Parallel-specific style estimator block /#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; }#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; }#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column; }#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; }#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; }#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0; }/ Serial-specific style estimator block /#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; }/ Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is clickable and can be expanded/collapsed. - Pipeline and ColumnTransformer use this feature and define the default style - Estimators will overwrite some part of the style using the `sk-estimator` class // Pipeline and ColumnTransformer style (default) /#sk-container-id-1 div.sk-toggleable {/ Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer /background-color: var(--sklearn-color-background); }/ Toggleable label / #sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center; }#sk-container-id-1 label.sk-toggleable__label-arrow:before {/ Arrow on the left of the label /content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); }#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); }/ Toggleable content - dropdown /#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/ unfitted /background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-toggleable__content.fitted {/ fitted /background-color: var(--sklearn-color-fitted-level-0); }#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/ unfitted /background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-toggleable__content.fitted pre {/ unfitted /background-color: var(--sklearn-color-fitted-level-0); }#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {/ Expand drop-down /max-height: 200px;max-width: 100%;overflow: auto; }#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; }/ Pipeline/ColumnTransformer-specific style /#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); }/ Estimator-specific style // Colorize estimator box / #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/ unfitted /background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/ fitted /background-color: var(--sklearn-color-fitted-level-2); }#sk-container-id-1 div.sk-label label.sk-toggleable__label, #sk-container-id-1 div.sk-label label {/ The background is the default theme color /color: var(--sklearn-color-text-on-default-background); }/ On hover, darken the color of the background / #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); }/ Label box, darken color on hover, fitted / #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); }/ Estimator label /#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; }#sk-container-id-1 div.sk-label-container {text-align: center; }/ Estimator-specific / #sk-container-id-1 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/ unfitted /background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-estimator.fitted {/ fitted /background-color: var(--sklearn-color-fitted-level-0); }/ on hover / #sk-container-id-1 div.sk-estimator:hover {/ unfitted /background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-estimator.fitted:hover {/ fitted /background-color: var(--sklearn-color-fitted-level-2); }/ Specification for estimator info (e.g. "i" and "?") // Common style for "i" and "?" /.sk-estimator-doc-link, a:link.sk-estimator-doc-link, a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/ unfitted /border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1); }.sk-estimator-doc-link.fitted, a:link.sk-estimator-doc-link.fitted, a:visited.sk-estimator-doc-link.fitted {/ fitted /border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); }/ On hover / div.sk-estimator:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover, div.sk-label-container:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover {/ unfitted /background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover, div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover {/ fitted /background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }/ Span, style for the box shown on hovering the info icon / .sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/ unfitted /background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3); }.sk-estimator-doc-link.fitted span {/ fitted /background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); }.sk-estimator-doc-link:hover span {display: block; }/ "?"-specific style due to the `<a>` HTML tag /#sk-container-id-1 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/ unfitted /color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; }#sk-container-id-1 a.estimator_doc_link.fitted {/ fitted /border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); }/ On hover / #sk-container-id-1 a.estimator_doc_link:hover {/ unfitted /background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }#sk-container-id-1 a.estimator_doc_link.fitted:hover {/ fitted /background-color: var(--sklearn-color-fitted-level-3); } </style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7f8d443a30a0>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">  Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7f8d443a30a0>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> TfidfVectorizer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></label><div class="sk-toggleable__content fitted"><pre>TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7f8d443a30a0>)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">XGBClassifier</label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div> ## Evaluation Results \| Metric \| Value \| \|----------\|----------\| \| accuracy \| 0.910317 \| \| f1 score \| 0.910317 \| \| ROC AUC \| 0.969008 \| # How to Get Started with the Model ```python import sklearn import dill as pickle from skops import hub_utils from pathlib import Path suicide_detector_repo = Path("./suicide-detector") hub_utils.download( repo_id="AndyJamesTurner/suicideDetector", dst=suicide_detector_repo ) with open(suicide_detector_repo / "model.pkl", 'rb') as file: clf = pickle.load(file) classification = clf.predict(["I want to kill myself"])[0] ``` # Model Evaluation The model was evaluated on a 0.3 holdout split using f1 score, accuracy, confusion matrix and ROC curves. ## Confusion matrix ![Confusion matrix](confusion_matrix.png) ## ROC Curve ![ROC Curve](roc_curve.png) # Classification Report \| index \| precision \| recall \| f1-score \| support \| \|--------------\|-------------\|----------\|------------\|--------------\| \| not suicide \| 0.891721 \| 0.934126 \| 0.912431 \| 34824 \| \| suicide \| 0.930785 \| 0.886491 \| 0.908098 \| 34799 \| \| accuracy \| 0.910317 \| 0.910317 \| 0.910317 \| 0.910317 \| \| macro avg \| 0.911253 \| 0.910308 \| 0.910265 \| 69623 \| \| weighted avg \| 0.911246 \| 0.910317 \| 0.910265 \| 69623 \| # Model Authors This model was created by the following authors: Andy Turner
punchnami/resnet50-pothole-classification	punchnami	"2024-02-18T00:31:38Z"	28	0	transformers	[ "transformers", "tensorboard", "safetensors", "resnet", "image-classification", "vision", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-02-18T00:20:01Z"	--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - image-classification - vision - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: output_resnet results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6705298013245033 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output_resnet This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4783 - Accuracy: 0.6705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cpu - Datasets 2.17.0 - Tokenizers 0.15.1
mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF	mradermacher	"2025-03-02T20:38:38Z"	57	0	transformers	[ "transformers", "gguf", "medit-lite", "model-pruning", "text-generation", "pl", "en", "base_model:meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune", "base_model:quantized:meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	"2025-03-01T19:10:03Z"	--- base_model: meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune language: - pl - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - medit-lite - model-pruning - text-generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-Q2_K.gguf) \| i1-Q2_K \| 5.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-IQ3_M.gguf) \| i1-IQ3_M \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.0 \| optimal size/speed/quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
devasheeshG/whisper_medium_fp16_transformers	devasheeshG	"2023-07-11T21:09:33Z"	107	2	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "audio", "speech", "wav2vec2", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2023-07-02T11:04:37Z"	--- license: apache-2.0 pipeline_tag: automatic-speech-recognition tags: - pytorch - audio - speech - automatic-speech-recognition - whisper - wav2vec2 model-index: - name: whisper_medium_fp16_transformers results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: librispeech_asr name: LibriSpeech (clean) config: clean split: test args: language: en metrics: - type: wer value: 0 name: Test WER description: Word Error Rate - type: mer value: 0 name: Test MER description: Match Error Rate - type: wil value: 0 name: Test WIL description: Word Information Lost - type: wip value: 0 name: Test WIP description: Word Information Preserved - type: cer value: 0 name: Test CER description: Character Error Rate - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: librispeech_asr name: LibriSpeech (other) config: other split: test args: language: en metrics: - type: wer value: 0 name: Test WER description: Word Error Rate - type: mer value: 0 name: Test MER description: Match Error Rate - type: wil value: 0 name: Test WIL description: Word Information Lost - type: wip value: 0 name: Test WIP description: Word Information Preserved - type: cer value: 0 name: Test CER description: Character Error Rate - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: mozilla-foundation/common_voice_14_0 name: Common Voice (14.0) (Hindi) config: hi split: test args: language: hi metrics: - type: wer value: 54.97 name: Test WER description: Word Error Rate - type: mer value: 47.86 name: Test MER description: Match Error Rate - type: wil value: 66.83 name: Test WIL description: Word Information Lost - type: wip value: 33.16 name: Test WIP description: Word Information Preserved - type: cer value: 30.23 name: Test CER description: Character Error Rate widget: - example_title: Hinglish Sample src: https://huggingface.co/devasheeshG/whisper_medium_fp16_transformers/resolve/main/test.wav - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su --- ## Versions: - CUDA: 12.1 - cuDNN Version: 8.9.2.26_1.0-1_amd64 * tensorflow Version: 2.12.0 * torch Version: 2.1.0.dev20230606+cu12135 * transformers Version: 4.30.2 * accelerate Version: 0.20.3 ## Model Benchmarks: - RAM: 2.8 GB (Original_Model: 5.5GB) - VRAM: 1812 MB (Original_Model: 6GB) - test.wav: 23 s (Multilingual Speech i.e. English+Hindi) - Time in seconds for Processing by each device \| Device Name \| float32 (Original) \| float16 \| CudaCores \| TensorCores \| \| ----------------- \| ------------------ \| ------- \| --------- \| ----------- \| \| 3060 \| 1.7 \| 1.1 \| 3,584 \| 112 \| \| 1660 Super \| OOM \| 3.3 \| 1,408 \| N/A \| \| Collab (Tesla T4) \| 2.8 \| 2.2 \| 2,560 \| 320 \| \| Collab (CPU) \| 35 \| N/A \| N/A \| N/A \| \| M1 (CPU) \| - \| - \| - \| - \| \| M1 (GPU -> 'mps') \| - \| - \| - \| - \| - NOTE: TensorCores are efficient in mixed-precision calculations - CPU -> torch.float16 not supported on CPU (AMD Ryzen 5 3600 or Collab CPU) - Punchuation: True ## Model Error Benchmarks: - WER: Word Error Rate - MER: Match Error Rate - WIL: Word Information Lost - WIP: Word Information Preserved - CER: Character Error Rate ### Hindi to Hindi (test.tsv) [Common Voice 14.0](https://commonvoice.mozilla.org/en/datasets) Test done on RTX 3060 on 2557 Samples \| \| WER \| MER \| WIL \| WIP \| CER \| \| ----------------------- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| Original_Model (54 min) \| 52.02 \| 47.86 \| 66.82 \| 33.17 \| 23.76 \| \| This_Model (38 min) \| 54.97 \| 47.86 \| 66.83 \| 33.16 \| 30.23 \| ### Hindi to English (test.csv) [Custom Dataset](https://huggingface.co/datasets/devasheeshG/common_voices_14_0_hi2en_hi2hi) Test done on RTX 3060 on 1000 Samples \| \| WER \| MER \| WIL \| WIP \| CER \| \| ----------------------- \| --- \| --- \| --- \| --- \| --- \| \| Original_Model (30 min) \| - \| - \| - \| - \| - \| \| This_Model (20 min) \| - \| - \| - \| - \| - \| ### English ([LibriSpeech](https://huggingface.co/datasets/librispeech_asr) -> test-clean) Test done on RTX 3060 on __ Samples \| \| WER \| MER \| WIL \| WIP \| CER \| \| -------------- \| --- \| --- \| --- \| --- \| --- \| \| Original_Model \| - \| - \| - \| - \| - \| \| This_Model \| - \| - \| - \| - \| - \| ### English ([LibriSpeech](https://huggingface.co/datasets/librispeech_asr) -> test-other) Test done on RTX 3060 on __ Samples \| \| WER \| MER \| WIL \| WIP \| CER \| \| -------------- \| --- \| --- \| --- \| --- \| --- \| \| Original_Model \| - \| - \| - \| - \| - \| \| This_Model \| - \| - \| - \| - \| - \| - 'jiwer' library is used for calculations ## Code for conversion: - ### [Will be soon Uploaded on Github](https://github.com/devasheeshG) ## Usage A file ``__init__.py`` is contained inside this repo which contains all the code to use this model. Firstly, clone this repo and place all the files inside a folder. ### Make sure you have git-lfs installed (https://git-lfs.com) ```bash git lfs install git clone https://huggingface.co/devasheeshG/whisper_medium_fp16_transformers ``` Please try in jupyter notebook ```python # Import the Model from whisper_medium_fp16_transformers import Model, load_audio, pad_or_trim ``` ```python # Initilise the model model = Model( model_name_or_path='whisper_medium_fp16_transformers', cuda_visible_device="0", device='cuda', ) ``` ```python # Load Audio audio = load_audio('whisper_medium_fp16_transformers/test.wav') audio = pad_or_trim(audio) ``` ```python # Transcribe (First transcription takes time) model.transcribe(audio) ``` ## Credits It is fp16 version of ``openai/whisper-medium``
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0	anas-awadalla	"2022-02-26T05:24:05Z"	4	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]	question-answering	"2022-03-02T23:29:05Z"	--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

RichardErkhov/Digest0703_-_test_llm-gguf

RichardErkhov

"2025-04-05T01:18:43Z"

0

null

[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-04-05T00:05:47Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) test_llm - GGUF - Model creator: https://huggingface.co/Digest0703/ - Original model: https://huggingface.co/Digest0703/test_llm/ | Name | Quant method | Size | | ---- | ---- | ---- | | [test_llm.Q2_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q2_K.gguf) | Q2_K | 1.27GB | | [test_llm.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ3_XS.gguf) | IQ3_XS | 1.38GB | | [test_llm.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ3_S.gguf) | IQ3_S | 1.44GB | | [test_llm.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K_S.gguf) | Q3_K_S | 1.44GB | | [test_llm.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ3_M.gguf) | IQ3_M | 1.49GB | | [test_llm.Q3_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K.gguf) | Q3_K | 1.57GB | | [test_llm.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K_M.gguf) | Q3_K_M | 1.57GB | | [test_llm.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q3_K_L.gguf) | Q3_K_L | 1.69GB | | [test_llm.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ4_XS.gguf) | IQ4_XS | 1.71GB | | [test_llm.Q4_0.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_0.gguf) | Q4_0 | 1.79GB | | [test_llm.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.IQ4_NL.gguf) | IQ4_NL | 1.79GB | | [test_llm.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_K_S.gguf) | Q4_K_S | 1.8GB | | [test_llm.Q4_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_K.gguf) | Q4_K | 1.88GB | | [test_llm.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_K_M.gguf) | Q4_K_M | 1.88GB | | [test_llm.Q4_1.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q4_1.gguf) | Q4_1 | 1.95GB | | [test_llm.Q5_0.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_0.gguf) | Q5_0 | 2.11GB | | [test_llm.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_K_S.gguf) | Q5_K_S | 2.11GB | | [test_llm.Q5_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_K.gguf) | Q5_K | 2.16GB | | [test_llm.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_K_M.gguf) | Q5_K_M | 2.16GB | | [test_llm.Q5_1.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q5_1.gguf) | Q5_1 | 2.28GB | | [test_llm.Q6_K.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q6_K.gguf) | Q6_K | 2.46GB | | [test_llm.Q8_0.gguf](https://huggingface.co/RichardErkhov/Digest0703_-_test_llm-gguf/blob/main/test_llm.Q8_0.gguf) | Q8_0 | 3.19GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

pfunk/CartPole-v1-CP_DQPN_x5-seed3

pfunk

"2023-03-20T02:59:44Z"

0

cleanrl

[ "cleanrl", "tensorboard", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2023-03-20T02:59:41Z"

--- tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQPN_freq** Agent Playing **CartPole-v1** This is a trained model of a DQPN_freq agent playing CartPole-v1. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/CP_DQPN_x5.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[CP_DQPN_x5]" python -m cleanrl_utils.enjoy --exp-name CP_DQPN_x5 --env-id CartPole-v1 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/dqpn_freq.py curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/CartPole-v1-CP_DQPN_x5-seed3/raw/main/poetry.lock poetry install --all-extras python dqpn_freq.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name CP_DQPN_x5 --policy-network-frequency 100 --seed 3 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq.py', 'batch_size': 256, 'buffer_size': 300000, 'capture_video': True, 'cuda': True, 'end_e': 0.1, 'env_id': 'CartPole-v1', 'exp_name': 'CP_DQPN_x5', 'exploration_fraction': 0.2, 'gamma': 1.0, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 1000, 'policy_network_frequency': 100, 'policy_tau': 1.0, 'save_model': True, 'seed': 3, 'start_e': 1.0, 'target_network_frequency': 20, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 500000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

andradejunior/ppo-LunarLander-v2

andradejunior

"2022-12-20T02:34:03Z"

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2022-12-20T02:33:35Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 244.51 +/- 39.99 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mirella-guenther/distil-whisper-distil-large-v3-torgo-2-epochs

mirella-guenther

"2024-06-05T00:23:47Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-06-05T00:23:43Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Kondara/gemma-3-12b-it-Q4_K_M-GGUF

Kondara

"2025-03-13T04:05:18Z"

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:google/gemma-3-12b-it", "base_model:quantized:google/gemma-3-12b-it", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]

image-text-to-text

"2025-03-13T04:04:41Z"

--- base_model: google/gemma-3-12b-it library_name: transformers license: gemma pipeline_tag: image-text-to-text tags: - llama-cpp - gguf-my-repo extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # Kondara/gemma-3-12b-it-Q4_K_M-GGUF This model was converted to GGUF format from [`google/gemma-3-12b-it`](https://huggingface.co/google/gemma-3-12b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/google/gemma-3-12b-it) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kondara/gemma-3-12b-it-Q4_K_M-GGUF --hf-file gemma-3-12b-it-q4_k_m.gguf -c 2048 ```

prushton/dreambooth-myra-3000

prushton

"2023-12-04T01:22:16Z"

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2023-12-03T21:41:37Z"

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of myra tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - prushton/dreambooth-myra-3000 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of myra using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

TheBloke/MelloGPT-AWQ

TheBloke

"2023-12-16T15:02:11Z"

11

3

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:nbertagnolli/counsel-chat", "base_model:steve-cse/MelloGPT", "base_model:quantized:steve-cse/MelloGPT", "license:mit", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]

text-generation

"2023-12-16T14:44:18Z"

--- base_model: steve-cse/MelloGPT datasets: - nbertagnolli/counsel-chat inference: false license: mit model_creator: Steve Boby George model_name: MelloGPT model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # MelloGPT - AWQ - Model creator: [Steve Boby George](https://huggingface.co/steve-cse) - Original model: [MelloGPT](https://huggingface.co/steve-cse/MelloGPT)  ## Description This repo contains AWQ model files for [Steve Boby George's MelloGPT](https://huggingface.co/steve-cse/MelloGPT). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MelloGPT-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MelloGPT-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MelloGPT-GGUF) * [Steve Boby George's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/steve-cse/MelloGPT)   ## Prompt template: Unknown ``` {prompt} ```   ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/MelloGPT-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/MelloGPT-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `MelloGPT-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!   ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/MelloGPT-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''{prompt} ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/MelloGPT-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ```   ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/MelloGPT-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ```   ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/MelloGPT-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ```   ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: Steve Boby George's MelloGPT A fine tuned version of Mistral-7B-v0.1 on Counsel Chat dataset for mental health conversations. In an era where mental health support is of paramount importance, A large language model fine-tuned on mental health counseling conversations stands as a pioneering solution. This approach aims to elevate natural language understanding and generation within the realm of mental health support. Leveraging a diverse dataset of anonymized counseling sessions, the model has been trained to recognize and respond to a wide range of mental health concerns, including anxiety, depression, stress, and more. The fine-tuning process incorporates ethical considerations, privacy concerns, and sensitivity to the nuances of mental health conversations. The resulting model will demonstrate an intricate understanding of mental health issues and provide empathetic and supportive responses, offering a valuable tool for individuals seeking guidance, mental health professionals, and the broader healthcare community.

stablediffusionapi/godhorror

stablediffusionapi

"2024-05-24T10:09:06Z"

29

0

diffusers

[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2024-05-24T10:06:52Z"

--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # GodHorror API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/6156582341716545162.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "godhorror" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/godhorror) Model link: [View model](https://modelslab.com/models/godhorror) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "godhorror", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

timmyAlvice/house_md_transfer_learning

timmyAlvice

"2025-03-23T09:42:25Z"

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

"2025-03-11T09:00:32Z"

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

John6666/prefect-pony-xl-v3-sdxl

John6666

"2024-09-11T03:32:21Z"

388

1

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "animagine", "pony", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

"2024-09-11T03:22:17Z"

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - animagine - pony --- Original model is [here](https://civitai.com/models/439889/prefect-pony-xl?modelVersionId=828380). This model created by [Goofy_Ai](https://civitai.com/user/Goofy_Ai).

MrinmoySaikia/t5-small-finetuned-wikisql

MrinmoySaikia

"2024-04-29T05:59:19Z"

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-04-28T21:45:00Z"

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-wikisql results: [] ---  # t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.30.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3

RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf

RichardErkhov

"2024-04-17T10:23:47Z"

164

0

null

[ "gguf", "arxiv:2012.05628", "endpoints_compatible", "region:us" ]

null

"2024-04-17T10:20:46Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt2-small-italian-embeddings - GGUF - Model creator: https://huggingface.co/GroNLP/ - Original model: https://huggingface.co/GroNLP/gpt2-small-italian-embeddings/ | Name | Quant method | Size | | ---- | ---- | ---- | | [gpt2-small-italian-embeddings.Q2_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q2_K.gguf) | Q2_K | 0.06GB | | [gpt2-small-italian-embeddings.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ3_XS.gguf) | IQ3_XS | 0.06GB | | [gpt2-small-italian-embeddings.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ3_S.gguf) | IQ3_S | 0.06GB | | [gpt2-small-italian-embeddings.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K_S.gguf) | Q3_K_S | 0.06GB | | [gpt2-small-italian-embeddings.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ3_M.gguf) | IQ3_M | 0.07GB | | [gpt2-small-italian-embeddings.Q3_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K.gguf) | Q3_K | 0.07GB | | [gpt2-small-italian-embeddings.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K_M.gguf) | Q3_K_M | 0.07GB | | [gpt2-small-italian-embeddings.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q3_K_L.gguf) | Q3_K_L | 0.07GB | | [gpt2-small-italian-embeddings.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ4_XS.gguf) | IQ4_XS | 0.07GB | | [gpt2-small-italian-embeddings.Q4_0.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_0.gguf) | Q4_0 | 0.08GB | | [gpt2-small-italian-embeddings.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.IQ4_NL.gguf) | IQ4_NL | 0.08GB | | [gpt2-small-italian-embeddings.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_K_S.gguf) | Q4_K_S | 0.08GB | | [gpt2-small-italian-embeddings.Q4_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_K.gguf) | Q4_K | 0.08GB | | [gpt2-small-italian-embeddings.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_K_M.gguf) | Q4_K_M | 0.08GB | | [gpt2-small-italian-embeddings.Q4_1.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q4_1.gguf) | Q4_1 | 0.08GB | | [gpt2-small-italian-embeddings.Q5_0.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_0.gguf) | Q5_0 | 0.09GB | | [gpt2-small-italian-embeddings.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_K_S.gguf) | Q5_K_S | 0.09GB | | [gpt2-small-italian-embeddings.Q5_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_K.gguf) | Q5_K | 0.09GB | | [gpt2-small-italian-embeddings.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_K_M.gguf) | Q5_K_M | 0.09GB | | [gpt2-small-italian-embeddings.Q5_1.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q5_1.gguf) | Q5_1 | 0.1GB | | [gpt2-small-italian-embeddings.Q6_K.gguf](https://huggingface.co/RichardErkhov/GroNLP_-_gpt2-small-italian-embeddings-gguf/blob/main/gpt2-small-italian-embeddings.Q6_K.gguf) | Q6_K | 0.1GB | Original model description: --- language: it tags: - adaption - recycled - gpt2-small pipeline_tag: text-generation --- # GPT-2 recycled for Italian (small, adapted lexical embeddings) [Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) • [Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475) ## Model description This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingface.co/gpt2)) model. The Transformer layer weights in this model are identical to the original English, model but the lexical layer has been retrained for an Italian vocabulary. For details, check out our paper on [arXiv](https://arxiv.org/abs/2012.05628) and the code on [Github](https://github.com/wietsedv/gpt2-recycle). ## Related models ### Dutch - [`gpt2-small-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-small-dutch-embeddings): Small model size with only retrained lexical embeddings. - [`gpt2-small-dutch`](https://huggingface.co/GroNLP/gpt2-small-dutch): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**) - [`gpt2-medium-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-dutch-embeddings): Medium model size with only retrained lexical embeddings. ### Italian - [`gpt2-small-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-small-italian-embeddings): Small model size with only retrained lexical embeddings. - [`gpt2-small-italian`](https://huggingface.co/GroNLP/gpt2-small-italian): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**) - [`gpt2-medium-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings): Medium model size with only retrained lexical embeddings. ## How to use ```python from transformers import pipeline pipe = pipeline("text-generation", model="GroNLP/gpt2-small-italian-embeddings") ``` ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-small-italian-embeddings") model = AutoModel.from_pretrained("GroNLP/gpt2-small-italian-embeddings") # PyTorch model = TFAutoModel.from_pretrained("GroNLP/gpt2-small-italian-embeddings") # Tensorflow ``` ## BibTeX entry ```bibtex @misc{devries2020good, title={As good as new. How to successfully recycle English GPT-2 to make models for other languages}, author={Wietse de Vries and Malvina Nissim}, year={2020}, eprint={2012.05628}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

wyl88/rdt_5000

wyl88

"2025-03-08T14:01:37Z"

1

0

null

[ "pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

"2025-03-06T13:39:20Z"

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://huggingface.co/robotics-diffusion-transformer/rdt-1b - Docs: [More Information Needed]

cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1

cleanrl

"2023-03-02T23:00:22Z"

0

cleanrl

[ "cleanrl", "tensorboard", "Asterix-v5", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2023-03-02T23:00:21Z"

--- tags: - Asterix-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Asterix-v5 type: Asterix-v5 metrics: - type: mean_reward value: 338180.00 +/- 103580.12 name: mean_reward verified: false --- # (CleanRL) **PPO** Agent Playing **Asterix-v5** This is a trained model of a PPO agent playing Asterix-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_machado_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_machado_atari_wrapper --env-id Asterix-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1/raw/main/cleanba_ppo_envpool_machado_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_ppo_envpool_machado_atari_wrapper-seed1/raw/main/poetry.lock poetry install --all-extras python cleanba_ppo_envpool_machado_atari_wrapper.py --distributed --learner-device-ids 1 2 3 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 1 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'actor_devices': ['gpu:0'], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 15360, 'capture_video': False, 'clip_coef': 0.1, 'concurrency': True, 'cuda': True, 'distributed': True, 'ent_coef': 0.01, 'env_id': 'Asterix-v5', 'exp_name': 'cleanba_ppo_envpool_machado_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'global_learner_decices': ['gpu:1', 'gpu:2', 'gpu:3', 'gpu:5', 'gpu:6', 'gpu:7'], 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3], 'learner_devices': ['gpu:1', 'gpu:2', 'gpu:3'], 'learning_rate': 0.00025, 'local_batch_size': 7680, 'local_minibatch_size': 1920, 'local_num_envs': 60, 'local_rank': 0, 'max_grad_norm': 0.5, 'minibatch_size': 3840, 'norm_adv': True, 'num_envs': 120, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 3255, 'profile': False, 'save_model': True, 'seed': 1, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanba', 'world_size': 2} ```

tensorblock/my_Llama-3.2-3B-Instruct-GGUF

tensorblock

"2025-01-01T17:43:21Z"

27

0

transformers

[ "transformers", "gguf", "TensorBlock", "GGUF", "base_model:pavan01729/my_Llama-3.2-3B-Instruct", "base_model:quantized:pavan01729/my_Llama-3.2-3B-Instruct", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-01-01T17:28:26Z"

--- library_name: transformers tags: - TensorBlock - GGUF base_model: pavan01729/my_Llama-3.2-3B-Instruct --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## pavan01729/my_Llama-3.2-3B-Instruct - GGUF This repo contains GGUF format model files for [pavan01729/my_Llama-3.2-3B-Instruct](https://huggingface.co/pavan01729/my_Llama-3.2-3B-Instruct). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Cutting Knowledge Date: December 2023 Today Date: 02 Jan 2025 {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [my_Llama-3.2-3B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q2_K.gguf) | Q2_K | 1.364 GB | smallest, significant quality loss - not recommended for most purposes | | [my_Llama-3.2-3B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q3_K_S.gguf) | Q3_K_S | 1.543 GB | very small, high quality loss | | [my_Llama-3.2-3B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q3_K_M.gguf) | Q3_K_M | 1.687 GB | very small, high quality loss | | [my_Llama-3.2-3B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q3_K_L.gguf) | Q3_K_L | 1.815 GB | small, substantial quality loss | | [my_Llama-3.2-3B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q4_0.gguf) | Q4_0 | 1.917 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [my_Llama-3.2-3B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q4_K_S.gguf) | Q4_K_S | 1.928 GB | small, greater quality loss | | [my_Llama-3.2-3B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q4_K_M.gguf) | Q4_K_M | 2.019 GB | medium, balanced quality - recommended | | [my_Llama-3.2-3B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q5_0.gguf) | Q5_0 | 2.270 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [my_Llama-3.2-3B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q5_K_S.gguf) | Q5_K_S | 2.270 GB | large, low quality loss - recommended | | [my_Llama-3.2-3B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q5_K_M.gguf) | Q5_K_M | 2.322 GB | large, very low quality loss - recommended | | [my_Llama-3.2-3B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q6_K.gguf) | Q6_K | 2.644 GB | very large, extremely low quality loss | | [my_Llama-3.2-3B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/my_Llama-3.2-3B-Instruct-GGUF/blob/main/my_Llama-3.2-3B-Instruct-Q8_0.gguf) | Q8_0 | 3.422 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/my_Llama-3.2-3B-Instruct-GGUF --include "my_Llama-3.2-3B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/my_Llama-3.2-3B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

Themira/smollm-mt5-en-si

Themira

"2025-03-25T14:42:57Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceTB/SmolLM2-135M-Instruct", "base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct", "region:us" ]

null

"2025-03-14T18:12:11Z"

--- base_model: HuggingFaceTB/SmolLM2-135M-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0

OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero

OpenDILabCommunity

"2024-02-01T07:03:04Z"

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "TicTacToe-play-with-bot", "en", "arxiv:2310.08348", "license:apache-2.0", "model-index", "region:us" ]

reinforcement-learning

"2024-02-01T07:02:57Z"

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - TicTacToe-play-with-bot benchmark_name: OpenAI/Gym/Atari task_name: TicTacToe-play-with-bot pipeline_tag: reinforcement-learning model-index: - name: GumbelMuZero results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: TicTacToe-play-with-bot type: TicTacToe-play-with-bot metrics: - type: mean_reward value: 0.7 +/- 0.46 name: mean_reward --- # Play **TicTacToe-play-with-bot** with **GumbelMuZero** Policy ## Model Description  This implementation applies **GumbelMuZero** to the OpenAI/Gym/Atari **TicTacToe-play-with-bot** environment using [LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine). **LightZero** is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348). ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env,video] pip3 install LightZero ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from lzero.agent import GumbelMuZeroAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = GumbelMuZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-GumbelMuZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from lzero.agent import GumbelMuZeroAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero") # Instantiate the agent agent = GumbelMuZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-GumbelMuZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from lzero.agent import GumbelMuZeroAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = GumbelMuZeroAgent(env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-GumbelMuZero") # Train the agent return_ = agent.train(step=int(10000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="TicTacToe-play-with-bot", algo_name="GumbelMuZero", github_repo_url="https://github.com/opendilab/LightZero", github_doc_model_url=None, github_doc_env_url=None, installation_guide=''' pip3 install DI-engine[common_env,video] pip3 install LightZero ''', usage_file_by_git_clone="./gumbel_muzero/tictactoe_play_with_bot_gumbel_muzero_deploy.py", usage_file_by_huggingface_ding="./gumbel_muzero/tictactoe_play_with_bot_gumbel_muzero_download.py", train_file="./gumbel_muzero/tictactoe_play_with_bot_gumbel_muzero.py", repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero", platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)", model_description="**LightZero** is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).", create_repo=True ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'main_config': { 'exp_name': 'TicTacToe-play-with-bot-GumbelMuZero', 'seed': 0, 'env': { 'env_id': 'TicTacToe-play-with-bot', 'battle_mode': 'play_with_bot_mode', 'collector_env_num': 8, 'evaluator_env_num': 5, 'n_evaluator_episode': 5, 'manager': { 'shared_memory': False } }, 'policy': { 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'model': { 'observation_shape': [3, 3, 3], 'action_space_size': 9, 'image_channel': 3, 'num_res_blocks': 1, 'num_channels': 16, 'fc_reward_layers': [8], 'fc_value_layers': [8], 'fc_policy_layers': [8], 'support_scale': 10, 'reward_support_size': 21, 'value_support_size': 21 }, 'use_rnd_model': False, 'sampled_algo': False, 'gumbel_algo': True, 'mcts_ctree': True, 'collector_env_num': 8, 'evaluator_env_num': 5, 'env_type': 'board_games', 'action_type': 'varied_action_space', 'battle_mode': 'play_with_bot_mode', 'monitor_extra_statistics': True, 'game_segment_length': 5, 'transform2string': False, 'gray_scale': False, 'use_augmentation': False, 'augmentation': ['shift', 'intensity'], 'ignore_done': False, 'update_per_collect': 50, 'model_update_ratio': 0.1, 'batch_size': 256, 'optim_type': 'Adam', 'learning_rate': 0.003, 'target_update_freq': 100, 'target_update_freq_for_intrinsic_reward': 1000, 'weight_decay': 0.0001, 'momentum': 0.9, 'grad_clip_value': 0.5, 'n_episode': 8, 'num_simulations': 30, 'discount_factor': 1, 'td_steps': 9, 'num_unroll_steps': 3, 'reward_loss_weight': 1, 'value_loss_weight': 0.25, 'policy_loss_weight': 1, 'policy_entropy_loss_weight': 0, 'ssl_loss_weight': 0, 'lr_piecewise_constant_decay': False, 'threshold_training_steps_for_final_lr': 50000, 'manual_temperature_decay': False, 'threshold_training_steps_for_final_temperature': 100000, 'fixed_temperature_value': 0.25, 'use_ture_chance_label_in_chance_encoder': False, 'use_priority': True, 'priority_prob_alpha': 0.6, 'priority_prob_beta': 0.4, 'root_dirichlet_alpha': 0.3, 'root_noise_weight': 0.25, 'random_collect_episode_num': 0, 'eps': { 'eps_greedy_exploration_in_collect': False, 'type': 'linear', 'start': 1.0, 'end': 0.05, 'decay': 100000 }, 'cfg_type': 'GumbelMuZeroPolicyDict', 'max_num_considered_actions': 3, 'reanalyze_ratio': 0.0, 'eval_freq': 2000, 'replay_buffer_size': 10000 }, 'wandb_logger': { 'gradient_logger': False, 'video_logger': False, 'plot_logger': False, 'action_logger': False, 'return_logger': False } }, 'create_config': { 'env': { 'type': 'tictactoe', 'import_names': ['zoo.board_games.tictactoe.envs.tictactoe_env'] }, 'env_manager': { 'type': 'subprocess' }, 'policy': { 'type': 'gumbel_muzero', 'import_names': ['lzero.policy.gumbel_muzero'] } } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](<TODO>) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/LightZero) - **Doc**: [Algorithm link](<TODO>) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-GumbelMuZero/blob/main/replay.mp4)  - **Parameters total size:** 91.5 KB - **Last Update Date:** 2024-02-01 ## Environments  - **Benchmark:** OpenAI/Gym/Atari - **Task:** TicTacToe-play-with-bot - **Gym version:** 0.25.1 - **DI-engine version:** v0.5.0 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [Environments link](<TODO>)

mnavas/roberta-finetuned-WebClassification-v2-smalllinguaEN

mnavas

"2023-05-05T13:32:49Z"

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2023-05-05T12:06:16Z"

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: roberta-finetuned-WebClassification-v2-smalllinguaEN results: [] ---  # roberta-finetuned-WebClassification-v2-smalllinguaEN This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5844 - Accuracy: 0.7143 - F1: 0.7143 - Precision: 0.7143 - Recall: 0.7143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 7 | 2.3084 | 0.0714 | 0.0714 | 0.0714 | 0.0714 | | No log | 2.0 | 14 | 2.2951 | 0.2857 | 0.2857 | 0.2857 | 0.2857 | | No log | 3.0 | 21 | 2.2725 | 0.2143 | 0.2143 | 0.2143 | 0.2143 | | No log | 4.0 | 28 | 2.0608 | 0.2143 | 0.2143 | 0.2143 | 0.2143 | | No log | 5.0 | 35 | 1.8552 | 0.3571 | 0.3571 | 0.3571 | 0.3571 | | No log | 6.0 | 42 | 1.6846 | 0.5714 | 0.5714 | 0.5714 | 0.5714 | | No log | 7.0 | 49 | 1.5844 | 0.7143 | 0.7143 | 0.7143 | 0.7143 | | No log | 8.0 | 56 | 1.4531 | 0.7143 | 0.7143 | 0.7143 | 0.7143 | | No log | 9.0 | 63 | 1.3746 | 0.7143 | 0.7143 | 0.7143 | 0.7143 | | No log | 10.0 | 70 | 1.3663 | 0.7143 | 0.7143 | 0.7143 | 0.7143 | ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.0+cpu - Datasets 2.10.1 - Tokenizers 0.13.2

trapoom555/Phi-2-Text-Embedding-cft

trapoom555

"2024-08-05T16:43:32Z"

0

3

transformers

[ "transformers", "safetensors", "sentence-embedding", "sentence-similarity", "feature-extraction", "en", "arxiv:2408.00690", "license:mit", "endpoints_compatible", "region:us" ]

sentence-similarity

"2024-05-07T14:49:52Z"

--- license: mit language: - en tags: - sentence-embedding - sentence-similarity - transformers - feature-extraction pipeline_tag: sentence-similarity --- # Phi-2-Text-Embedding-cft ## Description This is a fine-tuned version of [Phi-2](https://huggingface.co/microsoft/phi-2) to perform Text Embedding tasks. The model is fine-tuned using the Contrastive Fine-tuning and LoRA technique on NLI datasets. The paper can be found [here](https://arxiv.org/abs/2408.00690). ## Base Model [Phi-2](https://huggingface.co/microsoft/phi-2) ## Usage 1. Clone Phi-2 repository ```bash git clone https://huggingface.co/microsoft/phi-2 ``` 2. Change a tokenizer setting in `tokenizer_config.json` ```json "add_eos_token": true ``` 3. Use the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch import numpy as np class PhiSentenceEmbedding: def __init__(self, model_path='microsoft/phi-2', adapter_path=None): self.tokenizer = AutoTokenizer.from_pretrained(model_path) self.model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) if adapter_path != None: # Load fine-tuned LoRA self.model.load_adapter(adapter_path) def get_last_hidden_state(self, text): inputs = self.tokenizer(text, return_tensors="pt").to('cuda') with torch.no_grad(): out = self.model(**inputs, output_hidden_states=True).hidden_states[-1][0, -1, :] return out.squeeze().float().cpu().numpy() def encode(self, sentences: list[str], **kwargs) -> list[np.ndarray]: """ Returns a list of embeddings for the given sentences. Args: sentences: List of sentences to encode Returns: List of embeddings for the given sentences """ out = [] for s in sentences: out.append(self.get_last_hidden_state(s)) return out phi_sentence_embedding = PhiSentenceEmbedding(<your-cloned-base-model-path>, 'trapoom555/Phi-2-Text-Embedding-cft') example_sentences = ["I don't like apples", "I like apples"] encoded_sentences = phi_sentence_embedding.encode(example_sentences) print(encoded_sentences) ``` ## Training Details | **Training Details** | **Value** | |-------------------------|-------------------| | Loss | InfoNCE | | Batch Size | 60 | | InfoNCE Temperature | 0.05 | | Learning Rate | 5e-05 | | Warmup Steps | 100 | | Learning Rate Scheduler | CosineAnnealingLR | | LoRA Rank | 8 | | LoRA Alpha | 32 | | LoRA Dropout | 0.1 | | Training Precision | bf16 | | Max Epoch | 1 | | GPU | RTX3090 | | Num GPUs | 4 | ## Training Scripts The training script for this model is written in this [Github repository](https://github.com/trapoom555/Language-Model-STS-CFT/tree/main). ## Checkpoints We provide checkpoints every 500 training steps which can be found [here](https://huggingface.co/trapoom555/Phi-2-Text-Embedding-cft-checkpoints). ## Evaluation Results | **Benchmarks** | **Before cft** | **After cft** | |----------------|----------------|---------------| | STS12 | 23.04 | 61.62 | | STS13 | 20.79 | 71.87 | | STS14 | 17.06 | 60.46 | | STS15 | 24.56 | 71.18 | | STS16 | 48.68 | 74.77 | | STS17 | 41.43 | 80.20 | | STSBenchmark | 37.87 | 79.46 | | BOISSES | 28.04 | 64.06 | | SICK-R | 48.40 | 74.37 | | **Overall** | **32.21** | **70.89** | ## Contributors Trapoom Ukarapol, Zhicheng Lee, Amy Xin ## Foot Notes This work is the final project of the Natural Language Processing Spring 2024 course at Tsinghua University 🟣. We would like to express our sincere gratitude to this course !

snshrivas10/tiny-chatbot-dpo

snshrivas10

"2024-05-19T06:33:48Z"

4

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]

null

"2024-05-19T06:31:42Z"

--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-chatbot-dpo results: [] ---  # tiny-chatbot-dpo This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

prxy5604/f04f9214-3f11-48bc-9511-fa4ab80fd7ed

prxy5604

"2025-01-18T08:08:18Z"

8

0

peft

[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:tlphams/gollm-12.8b-instruct-v2.3", "base_model:adapter:tlphams/gollm-12.8b-instruct-v2.3", "license:cc-by-nc-4.0", "region:us" ]

null

"2025-01-18T06:20:35Z"

--- library_name: peft license: cc-by-nc-4.0 base_model: tlphams/gollm-12.8b-instruct-v2.3 tags: - axolotl - generated_from_trainer model-index: - name: f04f9214-3f11-48bc-9511-fa4ab80fd7ed results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: tlphams/gollm-12.8b-instruct-v2.3 bf16: true chat_template: llama3 data_processes: 16 dataset_prepared_path: null datasets: - data_files: - 500a091925e5b6f2_train_data.json ds_type: json format: custom path: /workspace/input_data/500a091925e5b6f2_train_data.json type: field_input: selected_word field_instruction: original field_output: perturbed format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: prxy5604/f04f9214-3f11-48bc-9511-fa4ab80fd7ed hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/500a091925e5b6f2_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-5 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 68317063-c692-4732-a3b0-9be4ed3ef2e3 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 68317063-c692-4732-a3b0-9be4ed3ef2e3 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f04f9214-3f11-48bc-9511-fa4ab80fd7ed This model is a fine-tuned version of [tlphams/gollm-12.8b-instruct-v2.3](https://huggingface.co/tlphams/gollm-12.8b-instruct-v2.3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.8424 | 0.0003 | 1 | 0.4176 | | 1.7364 | 0.0160 | 50 | 0.1728 | | 1.6649 | 0.0320 | 100 | 0.1228 | | 1.8686 | 0.0480 | 150 | 0.1104 | | 1.6596 | 0.0640 | 200 | 0.1085 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

simonycl/best_model-sst-2-64-42

simonycl

"2023-07-26T02:15:01Z"

106

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2023-07-26T02:03:35Z"

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-42 results: [] ---  # best_model-sst-2-64-42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4849 - Accuracy: 0.8281 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 1.3914 | 0.8125 | | No log | 2.0 | 8 | 1.3910 | 0.8203 | | 0.3843 | 3.0 | 12 | 1.3922 | 0.8203 | | 0.3843 | 4.0 | 16 | 1.3920 | 0.8203 | | 0.5793 | 5.0 | 20 | 1.3923 | 0.8203 | | 0.5793 | 6.0 | 24 | 1.3989 | 0.8203 | | 0.5793 | 7.0 | 28 | 1.4029 | 0.8281 | | 0.3663 | 8.0 | 32 | 1.4103 | 0.8281 | | 0.3663 | 9.0 | 36 | 1.3999 | 0.8281 | | 0.2779 | 10.0 | 40 | 1.4010 | 0.8281 | | 0.2779 | 11.0 | 44 | 1.3978 | 0.8281 | | 0.2779 | 12.0 | 48 | 1.3963 | 0.8203 | | 0.3589 | 13.0 | 52 | 1.4087 | 0.8203 | | 0.3589 | 14.0 | 56 | 1.4067 | 0.8281 | | 0.3185 | 15.0 | 60 | 1.4148 | 0.8281 | | 0.3185 | 16.0 | 64 | 1.4171 | 0.8359 | | 0.3185 | 17.0 | 68 | 1.4140 | 0.8359 | | 0.1743 | 18.0 | 72 | 1.3982 | 0.8359 | | 0.1743 | 19.0 | 76 | 1.3650 | 0.8359 | | 0.1416 | 20.0 | 80 | 1.3456 | 0.8359 | | 0.1416 | 21.0 | 84 | 1.3210 | 0.8359 | | 0.1416 | 22.0 | 88 | 1.3070 | 0.8359 | | 0.0354 | 23.0 | 92 | 1.3015 | 0.8359 | | 0.0354 | 24.0 | 96 | 1.3319 | 0.8438 | | 0.0035 | 25.0 | 100 | 1.3656 | 0.8281 | | 0.0035 | 26.0 | 104 | 1.3587 | 0.8281 | | 0.0035 | 27.0 | 108 | 1.3243 | 0.8359 | | 0.0006 | 28.0 | 112 | 1.2945 | 0.8438 | | 0.0006 | 29.0 | 116 | 1.2898 | 0.8438 | | 0.0028 | 30.0 | 120 | 1.3066 | 0.8438 | | 0.0028 | 31.0 | 124 | 1.3055 | 0.8438 | | 0.0028 | 32.0 | 128 | 1.3202 | 0.8438 | | 0.0049 | 33.0 | 132 | 1.3351 | 0.8438 | | 0.0049 | 34.0 | 136 | 1.3190 | 0.8438 | | 0.0102 | 35.0 | 140 | 1.3141 | 0.8438 | | 0.0102 | 36.0 | 144 | 1.3142 | 0.8438 | | 0.0102 | 37.0 | 148 | 1.3647 | 0.8281 | | 0.0034 | 38.0 | 152 | 1.4250 | 0.8203 | | 0.0034 | 39.0 | 156 | 1.4708 | 0.8203 | | 0.0001 | 40.0 | 160 | 1.4570 | 0.8203 | | 0.0001 | 41.0 | 164 | 1.4446 | 0.8203 | | 0.0001 | 42.0 | 168 | 1.4345 | 0.8281 | | 0.0001 | 43.0 | 172 | 1.4272 | 0.8281 | | 0.0001 | 44.0 | 176 | 1.4185 | 0.8281 | | 0.0001 | 45.0 | 180 | 1.4048 | 0.8281 | | 0.0001 | 46.0 | 184 | 1.3962 | 0.8281 | | 0.0001 | 47.0 | 188 | 1.4924 | 0.8203 | | 0.0002 | 48.0 | 192 | 1.5361 | 0.8125 | | 0.0002 | 49.0 | 196 | 1.5831 | 0.8125 | | 0.0292 | 50.0 | 200 | 1.4789 | 0.8281 | | 0.0292 | 51.0 | 204 | 1.2642 | 0.8359 | | 0.0292 | 52.0 | 208 | 1.2154 | 0.8516 | | 0.0001 | 53.0 | 212 | 1.1895 | 0.8516 | | 0.0001 | 54.0 | 216 | 1.1775 | 0.8438 | | 0.0001 | 55.0 | 220 | 1.1730 | 0.8438 | | 0.0001 | 56.0 | 224 | 1.1746 | 0.8438 | | 0.0001 | 57.0 | 228 | 1.1782 | 0.8516 | | 0.0001 | 58.0 | 232 | 1.1838 | 0.8516 | | 0.0001 | 59.0 | 236 | 1.2456 | 0.8281 | | 0.025 | 60.0 | 240 | 1.3887 | 0.8281 | | 0.025 | 61.0 | 244 | 1.4950 | 0.8125 | | 0.025 | 62.0 | 248 | 1.5753 | 0.8047 | | 0.0001 | 63.0 | 252 | 1.6287 | 0.8047 | | 0.0001 | 64.0 | 256 | 1.6608 | 0.8047 | | 0.0001 | 65.0 | 260 | 1.6803 | 0.8047 | | 0.0001 | 66.0 | 264 | 1.6919 | 0.7969 | | 0.0001 | 67.0 | 268 | 1.5961 | 0.8047 | | 0.0001 | 68.0 | 272 | 1.4858 | 0.8125 | | 0.0001 | 69.0 | 276 | 1.4104 | 0.8281 | | 0.0001 | 70.0 | 280 | 1.3623 | 0.8281 | | 0.0001 | 71.0 | 284 | 1.3333 | 0.8359 | | 0.0001 | 72.0 | 288 | 1.3172 | 0.8359 | | 0.0 | 73.0 | 292 | 1.3107 | 0.8359 | | 0.0 | 74.0 | 296 | 1.5801 | 0.8047 | | 0.0014 | 75.0 | 300 | 1.7857 | 0.8047 | | 0.0014 | 76.0 | 304 | 1.8724 | 0.7969 | | 0.0014 | 77.0 | 308 | 1.9146 | 0.7969 | | 0.0001 | 78.0 | 312 | 1.9250 | 0.7969 | | 0.0001 | 79.0 | 316 | 1.9265 | 0.7969 | | 0.0001 | 80.0 | 320 | 1.9268 | 0.7969 | | 0.0001 | 81.0 | 324 | 1.9243 | 0.7969 | | 0.0001 | 82.0 | 328 | 1.9215 | 0.7969 | | 0.0 | 83.0 | 332 | 1.9188 | 0.7969 | | 0.0 | 84.0 | 336 | 1.9159 | 0.7969 | | 0.0 | 85.0 | 340 | 1.9137 | 0.7969 | | 0.0 | 86.0 | 344 | 1.9119 | 0.7969 | | 0.0 | 87.0 | 348 | 1.9103 | 0.7969 | | 0.0009 | 88.0 | 352 | 1.6541 | 0.8047 | | 0.0009 | 89.0 | 356 | 1.2749 | 0.8438 | | 0.0 | 90.0 | 360 | 1.2046 | 0.8438 | | 0.0 | 91.0 | 364 | 1.1909 | 0.8438 | | 0.0 | 92.0 | 368 | 1.1860 | 0.8594 | | 0.0 | 93.0 | 372 | 1.1901 | 0.8594 | | 0.0 | 94.0 | 376 | 1.1966 | 0.8516 | | 0.0001 | 95.0 | 380 | 1.2014 | 0.8516 | | 0.0001 | 96.0 | 384 | 1.2061 | 0.8438 | | 0.0001 | 97.0 | 388 | 1.2109 | 0.8438 | | 0.0 | 98.0 | 392 | 1.2170 | 0.8516 | | 0.0 | 99.0 | 396 | 1.2210 | 0.8516 | | 0.0 | 100.0 | 400 | 1.2237 | 0.8516 | | 0.0 | 101.0 | 404 | 1.2258 | 0.8516 | | 0.0 | 102.0 | 408 | 1.2276 | 0.8438 | | 0.0 | 103.0 | 412 | 1.2290 | 0.8438 | | 0.0 | 104.0 | 416 | 1.2301 | 0.8438 | | 0.0 | 105.0 | 420 | 1.2313 | 0.8438 | | 0.0 | 106.0 | 424 | 1.2324 | 0.8438 | | 0.0 | 107.0 | 428 | 1.2334 | 0.8438 | | 0.0 | 108.0 | 432 | 1.2345 | 0.8438 | | 0.0 | 109.0 | 436 | 1.2356 | 0.8438 | | 0.0 | 110.0 | 440 | 1.2366 | 0.8438 | | 0.0 | 111.0 | 444 | 1.2375 | 0.8516 | | 0.0 | 112.0 | 448 | 1.2384 | 0.8516 | | 0.0 | 113.0 | 452 | 1.2400 | 0.8516 | | 0.0 | 114.0 | 456 | 1.2415 | 0.8516 | | 0.0 | 115.0 | 460 | 1.2428 | 0.8516 | | 0.0 | 116.0 | 464 | 1.2439 | 0.8516 | | 0.0 | 117.0 | 468 | 1.2450 | 0.8516 | | 0.0 | 118.0 | 472 | 1.2459 | 0.8516 | | 0.0 | 119.0 | 476 | 1.2467 | 0.8516 | | 0.0 | 120.0 | 480 | 1.2476 | 0.8516 | | 0.0 | 121.0 | 484 | 1.2485 | 0.8516 | | 0.0 | 122.0 | 488 | 1.2495 | 0.8516 | | 0.0 | 123.0 | 492 | 1.2495 | 0.8516 | | 0.0 | 124.0 | 496 | 1.2491 | 0.8516 | | 0.0 | 125.0 | 500 | 1.2491 | 0.8516 | | 0.0 | 126.0 | 504 | 1.2494 | 0.8516 | | 0.0 | 127.0 | 508 | 1.2498 | 0.8516 | | 0.0 | 128.0 | 512 | 1.2503 | 0.8516 | | 0.0 | 129.0 | 516 | 1.2509 | 0.8516 | | 0.0 | 130.0 | 520 | 1.2514 | 0.8516 | | 0.0 | 131.0 | 524 | 1.2519 | 0.8516 | | 0.0 | 132.0 | 528 | 1.2527 | 0.8516 | | 0.0 | 133.0 | 532 | 1.2535 | 0.8516 | | 0.0 | 134.0 | 536 | 1.2542 | 0.8516 | | 0.0 | 135.0 | 540 | 1.2549 | 0.8516 | | 0.0 | 136.0 | 544 | 1.2554 | 0.8516 | | 0.0 | 137.0 | 548 | 1.3879 | 0.8359 | | 0.0001 | 138.0 | 552 | 1.6893 | 0.7969 | | 0.0001 | 139.0 | 556 | 1.8348 | 0.7969 | | 0.0 | 140.0 | 560 | 1.8942 | 0.7969 | | 0.0 | 141.0 | 564 | 1.8778 | 0.7969 | | 0.0 | 142.0 | 568 | 1.7187 | 0.8047 | | 0.0001 | 143.0 | 572 | 1.6119 | 0.8203 | | 0.0001 | 144.0 | 576 | 1.5523 | 0.8281 | | 0.0 | 145.0 | 580 | 1.5189 | 0.8281 | | 0.0 | 146.0 | 584 | 1.5008 | 0.8281 | | 0.0 | 147.0 | 588 | 1.4916 | 0.8281 | | 0.0 | 148.0 | 592 | 1.4872 | 0.8281 | | 0.0 | 149.0 | 596 | 1.4854 | 0.8281 | | 0.0 | 150.0 | 600 | 1.4849 | 0.8281 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

ilanasto/a2c-PandaReachDense-v3

ilanasto

"2024-05-04T07:35:22Z"

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2024-05-04T07:31:08Z"

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.11 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

SG161222/Paragon_V1.0

SG161222

"2023-06-03T06:19:16Z"

103

54

diffusers

[ "diffusers", "safetensors", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

"2023-05-03T07:17:32Z"

--- license: creativeml-openrail-m --- <b>Please read this!</b><br> This model is in the testing phase. The necessary VAE is already baked into the model.<br><hr> <b>The recommended negative prompt:</b><br><br> (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation, <a href="https://civitai.com/models/7808/easynegative">easynegative</a>, <a href="https://huggingface.co/zwv9/idk-who-is-this-model-belong-to/blob/main/bad-hands-5.pt">bad-hands-5</a><br><br> <b>Recommended parameters for generation:</b><br><br> <b>Sampling method:</b> Euler A<br> <b>CFG Scale:</b> 5-12<br> <b>Clip Skip:</b> 2<br><br> <b>Hires.Fix Parameters:</b><br><br> <b>Upscaler:</b> Latent or other<br> <b>Hires Steps:</b> 0 or other<br> <b>Denoising Strength:</b> 0.35 - 0.7<br> <b>Upscaled by:</b> 1.1 - 2.0<br><hr> <b>Examples:</b><br><br> <a href='https://postimg.cc/3kxXkXSJ' target='_blank'><img src='https://i.postimg.cc/0ypcHZ7m/Pic1.png' border='0' alt='Pic1'/></a> <a href='https://postimg.cc/2qmVqr8d' target='_blank'><img src='https://i.postimg.cc/q76n5vPF/Pic2.png' border='0' alt='Pic2'/></a> <a href='https://postimg.cc/k6GM84rS' target='_blank'><img src='https://i.postimg.cc/sX9MkQwT/Pic3.png' border='0' alt='Pic3'/></a> <a href='https://postimg.cc/gX7zKWdT' target='_blank'><img src='https://i.postimg.cc/j2xDtx1t/Pic4.png' border='0' alt='Pic4'/></a> <a href='https://postimg.cc/Js81xKVM' target='_blank'><img src='https://i.postimg.cc/mgztb6mz/Pic5.png' border='0' alt='Pic5'/></a> <a href='https://postimg.cc/Pp0HwQQG' target='_blank'><img src='https://i.postimg.cc/Zn55Xf9q/Pic6.png' border='0' alt='Pic6'/></a>

broalantap/GPT2-large-16-48000steps

broalantap

"2024-11-02T14:25:46Z"

145

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-11-02T14:24:06Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sriram-sanjeev9s/T5_model_1

sriram-sanjeev9s

"2024-04-02T05:48:13Z"

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt14", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-04-02T05:38:46Z"

--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: T5_model_1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 config: fr-en split: validation args: fr-en metrics: - name: Bleu type: bleu value: 8.741 ---  # T5_model_1 This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 1.4948 - Bleu: 8.741 - Gen Len: 17.974 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 10 | 1.5554 | 8.7554 | 17.9983 | | No log | 2.0 | 20 | 1.4948 | 8.741 | 17.974 | ### Framework versions - Transformers 4.32.1 - Pytorch 1.12.1 - Datasets 2.18.0 - Tokenizers 0.13.2

PrunaAI/NeverSleep-Llama-3-Lumimaid-8B-v0.1-bnb-8bit-smashed

PrunaAI

"2024-07-21T07:20:20Z"

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "base_model:quantized:NeverSleep/Llama-3-Lumimaid-8B-v0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

"2024-07-21T07:16:10Z"

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: NeverSleep/Llama-3-Lumimaid-8B-v0.1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with llm-int8. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo NeverSleep/Llama-3-Lumimaid-8B-v0.1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/NeverSleep-Llama-3-Lumimaid-8B-v0.1-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("NeverSleep/Llama-3-Lumimaid-8B-v0.1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model NeverSleep/Llama-3-Lumimaid-8B-v0.1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

atasoglu/mbert-base-cased-nli-stsb-tr

atasoglu

"2024-04-20T18:49:12Z"

23

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "tr", "dataset:nli_tr", "dataset:emrecan/stsb-mt-turkish", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

"2024-04-20T18:44:50Z"

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 datasets: - nli_tr - emrecan/stsb-mt-turkish language: - tr --- # atasoglu/mbert-base-cased-nli-stsb-tr This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model was adapted from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) and fine-tuned on these datasets: - [nli_tr](https://huggingface.co/datasets/nli_tr) - [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('atasoglu/mbert-base-cased-nli-stsb-tr') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('atasoglu/mbert-base-cased-nli-stsb-tr') model = AutoModel.from_pretrained('atasoglu/mbert-base-cased-nli-stsb-tr') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results Achieved results on the [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) test split are given below: ```txt Cosine-Similarity : Pearson: 0.8152 Spearman: 0.8130 Manhattan-Distance: Pearson: 0.8049 Spearman: 0.8128 Euclidean-Distance: Pearson: 0.8049 Spearman: 0.8126 Dot-Product-Similarity: Pearson: 0.7878 Spearman: 0.7822 ``` ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 180 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 18, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 108, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

LoneStriker/PiVoT-0.1-Starling-LM-RP-5.0bpw-h6-exl2

LoneStriker

"2023-11-28T16:35:59Z"

5

0

transformers

[ "transformers", "pytorch", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-11-28T16:33:02Z"

--- license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation --- # PiVoT-0.1-Starling-LM-RP ![image/png](./PiVoT.png) # **Model Details** ### Description PiVoT-0.1-Starling-LM-RP is RP finetuned model based on Starling-LM-alpha. Using Synatra-RP dataset.  ## Prompt template: OpenChat ``` GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: ``` Follow me on twitter: https://twitter.com/stablefluffy Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕 Contact me on Telegram: https://t.me/AlzarTakkarsen

sd-concepts-library/3d-female-cyborgs

sd-concepts-library

"2022-09-17T20:15:59Z"

0

39

null

[ "license:mit", "region:us" ]

null

"2022-09-17T20:15:45Z"

--- license: mit --- ### 3d Female Cyborgs on Stable Diffusion This is the `<A female cyborg>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<A female cyborg> 0](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/3.jpeg) ![<A female cyborg> 1](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/0.jpeg) ![<A female cyborg> 2](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/1.jpeg) ![<A female cyborg> 3](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/2.jpeg) ![<A female cyborg> 4](https://huggingface.co/sd-concepts-library/3d-female-cyborgs/resolve/main/concept_images/4.jpeg)

disanda/first_try_4

disanda

"2023-07-09T07:21:57Z"

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2023-07-09T07:20:27Z"

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: first_try_4 results: [] ---  # first_try_4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.5505 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7226 | 1.0 | 157 | 2.5273 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.12.0+cu102 - Datasets 2.12.0 - Tokenizers 0.13.3

DEVECOAI/Qwen2.5-Coder-32B-Instruct-bnb-4bit_lr2e-05_r16_rsTrue

DEVECOAI

"2025-04-04T10:40:45Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-04-04T10:40:06Z"

--- base_model: unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** DEVECOAI - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

CyberHarem/handa_roco_theidolmstermillionlive

CyberHarem

"2023-09-25T01:03:58Z"

0

null

[ "art", "text-to-image", "dataset:CyberHarem/handa_roco_theidolmstermillionlive", "license:mit", "region:us" ]

text-to-image

"2023-09-25T00:51:04Z"

--- license: mit datasets: - CyberHarem/handa_roco_theidolmstermillionlive pipeline_tag: text-to-image tags: - art --- # Lora of handa_roco_theidolmstermillionlive This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2380, you need to download `2380/handa_roco_theidolmstermillionlive.pt` as the embedding and `2380/handa_roco_theidolmstermillionlive.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 2380**, with the score of 0.759. The trigger words are: 1. `handa_roco_theidolmstermillionlive` 2. `long_hair, blush, bow, yellow_eyes, smile, hair_bow, bangs, twintails, green_eyes, grey_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.664 | [Download](5100/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![pattern_8-5100](5100/previews/pattern_8.png) | ![pattern_9-5100](5100/previews/pattern_9.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.724 | [Download](4760/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![pattern_9-4760](4760/previews/pattern_9.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.639 | [Download](4420/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![pattern_8-4420](4420/previews/pattern_8.png) | ![pattern_9-4420](4420/previews/pattern_9.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.681 | [Download](4080/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![pattern_9-4080](4080/previews/pattern_9.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.629 | [Download](3740/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![pattern_8-3740](3740/previews/pattern_8.png) | ![pattern_9-3740](3740/previews/pattern_9.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.660 | [Download](3400/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![pattern_9-3400](3400/previews/pattern_9.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.660 | [Download](3060/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![pattern_8-3060](3060/previews/pattern_8.png) | ![pattern_9-3060](3060/previews/pattern_9.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.679 | [Download](2720/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![pattern_9-2720](2720/previews/pattern_9.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | **2380** | **0.759** | [**Download**](2380/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![pattern_8-2380](2380/previews/pattern_8.png) | ![pattern_9-2380](2380/previews/pattern_9.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.755 | [Download](2040/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![pattern_9-2040](2040/previews/pattern_9.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.737 | [Download](1700/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![pattern_8-1700](1700/previews/pattern_8.png) | ![pattern_9-1700](1700/previews/pattern_9.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.609 | [Download](1360/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![pattern_9-1360](1360/previews/pattern_9.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.574 | [Download](1020/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![pattern_8-1020](1020/previews/pattern_8.png) | ![pattern_9-1020](1020/previews/pattern_9.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.514 | [Download](680/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![pattern_9-680](680/previews/pattern_9.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.331 | [Download](340/handa_roco_theidolmstermillionlive.zip) | [<NSFW, click to see>](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![pattern_8-340](340/previews/pattern_8.png) | ![pattern_9-340](340/previews/pattern_9.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

sn56/ef3fc409-5f78-48ab-a9e3-eb62f77e5e20

sn56

"2025-02-07T12:46:05Z"

9

0

peft

[ "peft", "safetensors", "gptj", "axolotl", "generated_from_trainer", "base_model:furiosa-ai/mlperf-gpt-j-6b", "base_model:adapter:furiosa-ai/mlperf-gpt-j-6b", "region:us" ]

null

"2025-02-07T12:21:57Z"

--- library_name: peft base_model: furiosa-ai/mlperf-gpt-j-6b tags: - axolotl - generated_from_trainer model-index: - name: ef3fc409-5f78-48ab-a9e3-eb62f77e5e20 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: furiosa-ai/mlperf-gpt-j-6b bf16: true chat_template: llama3 data_processes: 24 dataset_prepared_path: null datasets: - data_files: - a6dae33cde59515e_train_data.json ds_type: json format: custom path: /workspace/input_data/a6dae33cde59515e_train_data.json type: field_input: selftext field_instruction: title field_output: answers.text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 4 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: sn56/ef3fc409-5f78-48ab-a9e3-eb62f77e5e20 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 9.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_warmup_steps: 50 max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/a6dae33cde59515e_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1e-8 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null seed: 205560767 sequence_len: 1024 shuffle: true strict: false tf32: true tokenizer_type: AutoTokenizer torch_compile: true total_train_batch_size: 32 train_batch_size: 8 train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: sn56-miner wandb_mode: disabled wandb_name: null wandb_project: god wandb_run: vxah wandb_runid: null warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ef3fc409-5f78-48ab-a9e3-eb62f77e5e20 This model is a fine-tuned version of [furiosa-ai/mlperf-gpt-j-6b](https://huggingface.co/furiosa-ai/mlperf-gpt-j-6b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7362 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 205560767 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 14.9727 | 0.0010 | 1 | 3.9163 | | 11.3203 | 0.0513 | 50 | 2.8247 | | 11.5312 | 0.1026 | 100 | 2.7625 | | 11.2695 | 0.1540 | 150 | 2.7405 | | 11.3359 | 0.2053 | 200 | 2.7362 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

yamatazen/Ayla-Light-12B-Stock

yamatazen

"2025-02-17T08:42:47Z"

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:yamatazen/Ayla-Light-12B", "base_model:merge:yamatazen/Ayla-Light-12B", "base_model:yamatazen/Ayla-Light-12B-v2", "base_model:merge:yamatazen/Ayla-Light-12B-v2", "base_model:yamatazen/Ayla-Light-12B-v3", "base_model:merge:yamatazen/Ayla-Light-12B-v3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-17T04:11:51Z"

--- base_model: - yamatazen/Ayla-Light-12B-v2 - yamatazen/Ayla-Light-12B - yamatazen/Ayla-Light-12B-v3 library_name: transformers tags: - mergekit - merge --- ![image/png](https://huggingface.co/yamatazen/Ayla-Light-12B-Stock/resolve/main/stock.png?download=true) # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [yamatazen/Ayla-Light-12B](https://huggingface.co/yamatazen/Ayla-Light-12B) as a base. ### Models Merged The following models were included in the merge: * [yamatazen/Ayla-Light-12B-v2](https://huggingface.co/yamatazen/Ayla-Light-12B-v2) * [yamatazen/Ayla-Light-12B-v3](https://huggingface.co/yamatazen/Ayla-Light-12B-v3) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: yamatazen/Ayla-Light-12B models: - model: yamatazen/Ayla-Light-12B-v2 - model: yamatazen/Ayla-Light-12B-v3 merge_method: model_stock dtype: bfloat16 parameters: normalize: true ```

damgomz/ft_1_12e6_x8

damgomz

"2024-07-14T04:16:43Z"

11

0

transformers

[ "transformers", "safetensors", "albert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-06-19T16:47:07Z"

--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) | Metric | Value | |--------------------------|---------------------------------| | Duration (in seconds) | 153199.52336907387 | | Emissions (Co2eq in kg) | 0.092703332620456 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.8086008221386256 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.159581013076256 | | Consumed energy (kWh) | 1.9681818352148808 | | Country name | Switzerland | | Cloud provider | nan | | Cloud region | nan | | CPU count | 2 | | CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz | | GPU count | nan | | GPU model | nan | ## Environmental Impact (for one core) | Metric | Value | |--------------------------|---------------------------------| | CPU energy (kWh) | 0.2949090824854672 | | Emissions (Co2eq in kg) | 0.06000314665288726 | ## Note 12 juillet 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs32_lr1e4_x8 | | model_name | ft_1_12e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.2e-05 | | batch_size | 1 | | weight_decay | 0.0 | | warm_up_prop | 0.0 | | drop_out_prob | 0.1 | | packing_length | 100 | | train_test_split | 0.2 | | num_steps | 29328 | ## Training and Testing steps Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.707225 | 0.336807 | | 1 | 0.272179 | 0.248273 | 0.899627 | | 2 | 0.180017 | 0.233954 | 0.896014 | | 3 | 0.113639 | 0.252010 | 0.919735 | | 4 | 0.059565 | 0.308335 | 0.918050 | | 5 | 0.032741 | 0.333578 | 0.926764 | | 6 | 0.020775 | 0.390008 | 0.922906 |

SeyedAli/Melanoma-Classification

SeyedAli

"2024-03-02T16:08:40Z"

18

1

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-03-02T10:57:00Z"

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: Melanoma-Classification results: [] ---  # Melanoma-Classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [SeyedAli/Skin-Lesion-Dataset](https://huggingface.co/datasets/SeyedAli/Skin-Lesion-Dataset) dataset. It achieves the following results on the evaluation set: - Loss: 0.5750 - Accuracy: 0.8167 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9779 | 0.08 | 100 | 1.1158 | 0.6041 | | 0.9934 | 0.16 | 200 | 1.0227 | 0.6501 | | 0.9562 | 0.24 | 300 | 0.9276 | 0.6748 | | 1.0995 | 0.32 | 400 | 0.9088 | 0.6836 | | 0.8198 | 0.39 | 500 | 0.8581 | 0.6949 | | 0.8034 | 0.47 | 600 | 0.8444 | 0.6967 | | 0.8319 | 0.55 | 700 | 0.8196 | 0.7148 | | 0.787 | 0.63 | 800 | 0.8360 | 0.6975 | | 0.8642 | 0.71 | 900 | 0.8250 | 0.7008 | | 0.8329 | 0.79 | 1000 | 0.7939 | 0.7172 | | 0.9678 | 0.87 | 1100 | 0.7661 | 0.7332 | | 0.8226 | 0.95 | 1200 | 0.7284 | 0.7373 | | 0.7967 | 1.03 | 1300 | 0.7355 | 0.7411 | | 0.6531 | 1.1 | 1400 | 0.7561 | 0.7247 | | 0.5719 | 1.18 | 1500 | 0.6839 | 0.7638 | | 0.6123 | 1.26 | 1600 | 0.6857 | 0.7584 | | 0.6504 | 1.34 | 1700 | 0.6970 | 0.7531 | | 0.6214 | 1.42 | 1800 | 0.6841 | 0.7576 | | 0.4925 | 1.5 | 1900 | 0.6624 | 0.7642 | | 0.5797 | 1.58 | 2000 | 0.6287 | 0.7709 | | 0.6018 | 1.66 | 2100 | 0.6537 | 0.7622 | | 0.6334 | 1.74 | 2200 | 0.6413 | 0.7713 | | 0.4111 | 1.82 | 2300 | 0.6242 | 0.7786 | | 0.4779 | 1.89 | 2400 | 0.6260 | 0.7790 | | 0.5488 | 1.97 | 2500 | 0.6146 | 0.7807 | | 0.3212 | 2.05 | 2600 | 0.6975 | 0.7707 | | 0.4282 | 2.13 | 2700 | 0.6344 | 0.7790 | | 0.2822 | 2.21 | 2800 | 0.6985 | 0.7845 | | 0.3003 | 2.29 | 2900 | 0.5954 | 0.7993 | | 0.2982 | 2.37 | 3000 | 0.6156 | 0.7940 | | 0.2628 | 2.45 | 3100 | 0.6318 | 0.7963 | | 0.2987 | 2.53 | 3200 | 0.6495 | 0.8030 | | 0.2714 | 2.6 | 3300 | 0.6018 | 0.8052 | | 0.3059 | 2.68 | 3400 | 0.5944 | 0.8078 | | 0.2762 | 2.76 | 3500 | 0.6296 | 0.7936 | | 0.3685 | 2.84 | 3600 | 0.6277 | 0.8017 | | 0.2299 | 2.92 | 3700 | 0.5834 | 0.8125 | | 0.3414 | 3.0 | 3800 | 0.5750 | 0.8167 | | 0.1082 | 3.08 | 3900 | 0.6201 | 0.8196 | | 0.049 | 3.16 | 4000 | 0.6475 | 0.8161 | | 0.102 | 3.24 | 4100 | 0.6791 | 0.8097 | | 0.0483 | 3.31 | 4200 | 0.6582 | 0.8216 | | 0.1204 | 3.39 | 4300 | 0.6603 | 0.8222 | | 0.0611 | 3.47 | 4400 | 0.7174 | 0.8190 | | 0.0555 | 3.55 | 4500 | 0.6841 | 0.8236 | | 0.0188 | 3.63 | 4600 | 0.7009 | 0.8240 | | 0.1292 | 3.71 | 4700 | 0.7040 | 0.8204 | | 0.0661 | 3.79 | 4800 | 0.7074 | 0.8238 | | 0.1061 | 3.87 | 4900 | 0.6984 | 0.8210 | | 0.0861 | 3.95 | 5000 | 0.6913 | 0.8230 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

suku9/gpt2-moses-dpo

suku9

"2025-04-10T02:40:25Z"

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "trl", "dpo", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-10T02:40:19Z"

--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF

mradermacher

"2024-12-08T18:33:10Z"

484

1

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "tango", "en", "es", "dataset:spanish-ir/messirve", "dataset:tatakof/messi_mod-v0.0.2", "base_model:sandbox-ai/Llama-3.1-Tango-8b-f16", "base_model:quantized:sandbox-ai/Llama-3.1-Tango-8b-f16", "license:llama3.1", "endpoints_compatible", "region:us", "imatrix" ]

null

"2024-12-08T14:08:32Z"

--- base_model: sandbox-ai/Llama-3.1-Tango-8b-f16 datasets: - spanish-ir/messirve - tatakof/messi_mod-v0.0.2 language: - en - es library_name: transformers license: llama3.1 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - tango --- ## About      weighted/imatrix quants of https://huggingface.co/sandbox-ai/Llama-3.1-Tango-8b-f16  static quants are available at https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Tango-8b-f16-i1-GGUF/resolve/main/Llama-3.1-Tango-8b-f16.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

KBLab/megatron.bert-large.unigram-32k-pretok.25k-steps

KBLab

"2024-04-23T08:44:03Z"

93

0

transformers

[ "transformers", "safetensors", "megatron-bert", "feature-extraction", "sv", "endpoints_compatible", "region:us" ]

feature-extraction

"2024-04-23T08:29:42Z"

--- language: - sv --- # megatron.bert-large.unigram-32k-pretok.25k-steps This BERT model was trained using the NeMo library. The size of the model is a regular bert-large. The model was trained on more than 245GB of data, consisting mostly of web-data and Swedish newspaper text curated by the National Library of Sweden. Training was done for 25k training steps using a batch size of 8k. The model has multiple sibling models trained on the same dataset using different tokenizers or more/less parameters: - [megatron.bert-base.bpe-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.bpe-32k-no_pretok.25k-steps) - [megatron.bert-base.bpe-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.bpe-64k-no_pretok.25k-steps) - [megatron.bert-base.spe-bpe-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-32k-no_pretok.25k-steps) - [megatron.bert-base.spe-bpe-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-32k-pretok.25k-steps) - [megatron.bert-base.spe-bpe-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-64k-no_pretok.25k-steps) - [megatron.bert-base.spe-bpe-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.spe-bpe-64k-pretok.25k-steps) - [megatron.bert-base.unigram-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-32k-no_pretok.25k-steps) - [megatron.bert-base.unigram-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-32k-pretok.25k-steps) - [megatron.bert-base.unigram-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-64k-no_pretok.25k-steps) - [megatron.bert-base.unigram-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.unigram-64k-pretok.25k-steps) - [megatron.bert-base.wordpiece-32k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-32k-no_pretok.25k-steps) - [megatron.bert-base.wordpiece-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-32k-pretok.25k-steps) - [megatron.bert-base.wordpiece-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-64k-no_pretok.25k-steps) - [megatron.bert-base.wordpiece-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-base.wordpiece-64k-pretok.25k-steps) - [megatron.bert-large.bpe-64k-no_pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.bpe-64k-no_pretok.25k-steps) - [megatron.bert-large.spe-bpe-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.spe-bpe-32k-pretok.25k-steps) - [megatron.bert-large.unigram-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.unigram-32k-pretok.25k-steps) - [megatron.bert-large.unigram-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.unigram-64k-pretok.25k-steps) - [megatron.bert-large.wordpiece-32k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.wordpiece-32k-pretok.25k-steps) - [megatron.bert-large.wordpiece-64k-pretok.25k-steps](https://huggingface.co/KBLab/megatron.bert-large.wordpiece-64k-pretok.25k-steps) ## Acknowledgements The training was performed on the Luxembourg national supercomputer MeluXina. The authors gratefully acknowledge the LuxProvide teams for their expert support.

texanrangee/98daa160-8406-4bb3-98a2-7a8e6abe22d9

texanrangee

"2025-03-22T04:37:10Z"

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-03-22T03:21:46Z"

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

localmodels/WizardLM-7B-Uncensored-4bit

localmodels

"2023-05-29T03:54:19Z"

0

4

null

[ "region:us" ]

null

"2023-05-28T14:23:42Z"

## WizardLM 7B Uncensored 4-bit From ehartford: https://huggingface.co/ehartford/WizardLM-7B-Uncensored ### Folders **ggml:** q4_0 and q4_1 **gptq:** works with Triton and CUDA branches

DayStay/dqn-SpaceInvadersNoFrameskip-v4

DayStay

"2023-11-17T10:03:09Z"

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-11-17T10:02:33Z"

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 653.00 +/- 139.29 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DayStay -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DayStay -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DayStay ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

cs552-mlp/phi3-lora-arc3-gptq-3bits

cs552-mlp

"2024-06-12T12:21:25Z"

106

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]

text-generation

"2024-06-12T12:20:09Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

TheBloke/Swallow-13B-AWQ

TheBloke

"2023-12-19T21:58:07Z"

13

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "en", "ja", "base_model:tokyotech-llm/Swallow-13b-hf", "base_model:quantized:tokyotech-llm/Swallow-13b-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]

text-generation

"2023-12-19T21:32:03Z"

--- base_model: tokyotech-llm/Swallow-13b-hf inference: false language: - en - ja library_name: transformers license: llama2 model_creator: tokyotech-llm model_name: Swallow 13B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Swallow 13B - AWQ - Model creator: [tokyotech-llm](https://huggingface.co/tokyotech-llm) - Original model: [Swallow 13B](https://huggingface.co/tokyotech-llm/Swallow-13b-hf)  ## Description This repo contains AWQ model files for [tokyotech-llm's Swallow 13B](https://huggingface.co/tokyotech-llm/Swallow-13b-hf). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Swallow-13B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Swallow-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Swallow-13B-GGUF) * [tokyotech-llm's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/tokyotech-llm/Swallow-13b-hf)   ## Prompt template: None ``` {prompt} ```   ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Swallow-13B-AWQ/tree/main) | 4 | 128 | [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data/viewer/) | 4096 | 7.48 GB   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Swallow-13B-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Swallow-13B-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!   ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/Swallow-13B-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''{prompt} ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/Swallow-13B-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ```   ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Swallow-13B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ```   ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/Swallow-13B-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ```   ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: tokyotech-llm's Swallow 13B # Swallow Our Swallow model has undergone continuous pre-training from the Llama 2 family, primarily with the addition of Japanese language data. The tuned versions use supervised fine-tuning (SFT). Links to other models can be found in the index. ## Swallow Model Index |Model|Swallow-hf|Swallow-instruct-hf| |---|---|---| |7B| [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-7b-instruct-hf)| |13B| [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf)| |70B| [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-hf) | [Link](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-hf)| ![logo](./logo.png) This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/). Read our [blog post](https://zenn.dev/tokyotech_lm/articles/d6cb3a8fdfc907) or our paper (preprint coming soon) for more details! ## Model Details * **Model type**: Please refer to LLaMA-2 technical report for details on the model architecture. * **Language(s)**: Japanese English * **Library**: [Megatron-LM](https://github.com/rioyokotalab/Megatron-Llama2) * **Tokenizer**: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process. * **Contact**: swallow[at]nlp.c.titech.ac.jp ## Base Model Performance ### Japanese version |Model|Size|JCommonsenseQA|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en| |---|---|---|---|---|---|---|---|---|---| | | |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot| |Llama 2|7B|0.3852|0.4240|0.3410|0.7917|0.1905|0.0760|0.1783|0.1738| |Swallow|7B|0.4808|0.5078|0.5968|0.8573|0.1830|0.1240|0.2510|0.1511| |Llama 2|13B|0.6997|0.4415|0.4170|0.8533|0.2139|0.1320|0.2146|0.1982| |Swallow|13B|0.7837|0.5063|0.6398|0.9005|0.2168|0.2040|0.2720|0.1771| |Llama 2|70B|0.8686|0.4656|0.5256|0.9080|**0.2361**|0.3560|0.2643|**0.2398**| |Swallow|70B|**0.9348**|**0.6290**|**0.6960**|**0.9176**|0.2266|**0.4840**|**0.3043**|0.2298| ## Usage First install additional dependencies in [requirements.txt](./requirements.txt): ```sh pip install -r requirements.txt ``` ### Use the instruct model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-7b-instruct-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto") PROMPT_DICT = { "prompt_input": ( "以下に、あるタスクを説明する指示があり、それに付随する入力が更なる文脈を提供しています。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 入力:\n{input}\n\n### 応答:" ), "prompt_no_input": ( "以下に、あるタスクを説明する指示があります。" "リクエストを適切に完了するための回答を記述してください。\n\n" "### 指示:\n{instruction}\n\n### 応答:" ), } def create_prompt(instruction, input=None): """ Generates a prompt based on the given instruction and an optional input. If input is provided, it uses the 'prompt_input' template from PROMPT_DICT. If no input is provided, it uses the 'prompt_no_input' template. Args: instruction (str): The instruction describing the task. input (str, optional): Additional input providing context for the task. Default is None. Returns: str: The generated prompt. """ if input: # Use the 'prompt_input' template when additional input is provided return PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input) else: # Use the 'prompt_no_input' template when no additional input is provided return PROMPT_DICT["prompt_no_input"].format(instruction=instruction) # Example usage instruction_example = "以下のトピックに関する詳細な情報を提供してください。" input_example = "東京工業大学の主なキャンパスについて教えてください" prompt = create_prompt(instruction_example, input_example) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ### Use the base model ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "tokyotech-llm/Swallow-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") prompt = "東京工業大学の主なキャンパスは、" input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` ## Training Datasets ### Continual Pre-Training The following datasets were used for continual pre-training. - [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) - [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - Swallow Corpus - [The Pile](https://huggingface.co/datasets/EleutherAI/pile) ### Instruction Tuning The following datasets were used for the instruction tuning. - [Anthropic HH-RLHF](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) - [Databricks Dolly 15-k](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) ## Risks and Limitations The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations. ## Acknowledgements We thank Meta Research for releasing Llama 2 under an open license for others to build on. Our project is supported by the [ABCI Large-scale Language Model Building Support Program](https://abci.ai/en/link/llm_support_program.html) of the National Institute of Advanced Industrial Science and Technology. ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. ## Authors Here are the team members: - From [Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members: - [Naoaki Okazaki](https://www.chokkan.org/index.ja.html) - [Sakae Mizuki](https://s-mizuki-nlp.github.io/) - [Hiroki Iida](https://meshidenn.github.io/) - [Mengsay Loem](https://loem-ms.github.io/) - [Shota Hirai](https://huggingface.co/Kotemo428) - [Kakeru Hattori](https://aya-se.vercel.app/) - [Masanari Ohi](https://twitter.com/stjohn2007) - From [YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members: - [Rio Yokota](https://twitter.com/rioyokota) - [Kazuki Fujii](https://twitter.com/okoge_kaz) - [Taishi Nakamura](https://twitter.com/Setuna7777_2)

sadhaklal/a-and-not-b

sadhaklal

"2024-02-21T07:45:12Z"

0

pytorch

[ "pytorch", "license:apache-2.0", "region:us" ]

null

"2024-02-21T04:31:18Z"

--- license: apache-2.0 library_name: pytorch --- # a-and-not-b A neuron that performs the A AND (NOT B) logical computation. It generates the following truth table: | A | B | C | | - | - | - | | 0 | 0 | 0 | | 0 | 1 | 0 | | 1 | 0 | 1 | | 1 | 1 | 0 | It is inspired by McCulloch & Pitts' 1943 paper 'A Logical Calculus of the Ideas Immanent in Nervous Activity'. It doesn't contain any parameters. It takes as input two column vectors of zeros and ones. It outputs a single column vector of zeros and ones. Its mechanism is outlined in Figure 10-3 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'. ![](https://raw.githubusercontent.com/sambitmukherjee/handson-ml3-pytorch/main/chapter10/Figure_10-3.png) Like all the other neurons in Figure 10-3, it is activated when at least two of its input connections are active. Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/logical_computations_with_neurons.ipynb ## Usage ``` import torch import torch.nn as nn from huggingface_hub import PyTorchModelHubMixin # Let's create two column vectors containing `0`s and `1`s. batch = {'a': torch.tensor([[0], [0], [1], [1]]), 'b': torch.tensor([[0], [1], [0], [1]])} class A_AND_NOT_B(nn.Module, PyTorchModelHubMixin): def __init__(self): super().__init__() self.operation = "C = A AND (NOT B)" def forward(self, x): a = x['a'] b = x['b'] b = -1 * b inputs = torch.cat([a, a, b], axis=1) column_sum = torch.sum(inputs, dim=1, keepdim=True) output = (column_sum >= 2).long() return output # Instantiate: a_and_not_b = A_AND_NOT_B.from_pretrained("sadhaklal/a-and-not-b") # Forward pass: output = a_and_not_b(batch) print(output) ```

huggingtweets/th3nfthunt3r

huggingtweets

"2022-10-16T18:36:40Z"

141

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2022-10-16T18:35:50Z"

--- language: en thumbnail: http://www.huggingtweets.com/th3nfthunt3r/1665945395711/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1563458962158022656/CWXK4AUr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Th3 NFT Hunt3r</div> <div style="text-align: center; font-size: 14px;">@th3nfthunt3r</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Th3 NFT Hunt3r. | Data | Th3 NFT Hunt3r | | --- | --- | | Tweets downloaded | 364 | | Retweets | 50 | | Short tweets | 113 | | Tweets kept | 201 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/13l2dy5v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @th3nfthunt3r's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xgt6nuf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xgt6nuf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/th3nfthunt3r') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

mradermacher/SmolLM2-MagpieUltra-8k-GGUF

mradermacher

"2025-01-29T15:46:07Z"

221

0

transformers

[ "transformers", "gguf", "generated_from_trainer", "trl", "sft", "en", "base_model:HuggingFaceTB/SmolLM2-MagpieUltra-8k", "base_model:quantized:HuggingFaceTB/SmolLM2-MagpieUltra-8k", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-01-29T15:19:41Z"

--- base_model: HuggingFaceTB/SmolLM2-MagpieUltra-8k language: - en library_name: transformers model_name: SmolLM2-MagpieUltra-8k quantized_by: mradermacher tags: - generated_from_trainer - trl - sft --- ## About      static quants of https://huggingface.co/HuggingFaceTB/SmolLM2-MagpieUltra-8k  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q2_K.gguf) | Q2_K | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q3_K_S.gguf) | Q3_K_S | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q3_K_L.gguf) | Q3_K_L | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.IQ4_XS.gguf) | IQ4_XS | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q4_K_S.gguf) | Q4_K_S | 1.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q5_K_S.gguf) | Q5_K_S | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q5_K_M.gguf) | Q5_K_M | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q6_K.gguf) | Q6_K | 1.5 | very good quality | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/SmolLM2-MagpieUltra-8k-GGUF/resolve/main/SmolLM2-MagpieUltra-8k.f16.gguf) | f16 | 3.5 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

RichardErkhov/alonzogarbanzo_-_Bloom-1b7-ropes-IT-baseline-4bits

RichardErkhov

"2025-03-04T21:51:25Z"

0

null

[ "safetensors", "bloom", "4-bit", "bitsandbytes", "region:us" ]

null

"2025-03-04T21:50:29Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Bloom-1b7-ropes-IT-baseline - bnb 4bits - Model creator: https://huggingface.co/alonzogarbanzo/ - Original model: https://huggingface.co/alonzogarbanzo/Bloom-1b7-ropes-IT-baseline/ Original model description: --- license: bigscience-bloom-rail-1.0 base_model: bigscience/bloom-1b7 tags: - generated_from_trainer model-index: - name: Bloom-1b7-ropes-IT-baseline results: [] ---  # Bloom-1b7-ropes-IT-baseline This model is a fine-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Instruction Tuned on the ropes task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/ropes ## Training procedure Given a set of prompts: ``` python prompts = [ "Given the following background and situation, answer the question: ", "Based on the background information and the current situation, what is the answer to the question? ", "Considering the background and the described situation, provide an answer to this question: ", ] ``` Each example is concatenated with the prompt, background, situation, question and answer: ``` python input_text = f"{prompt}Background: {background} Situation: {situation} Question: {question} Answer: {answer_text}." ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results Final results: {'loss': 0.024, 'grad_norm': 1.3331243991851807, 'learning_rate': 8.000000000000001e-07, 'epoch': 10.0} Average results: {'train_runtime': 862.219, 'train_samples_per_second': 2.32, 'train_steps_per_second': 0.58, 'train_loss': 0.4160269268453121, 'epoch': 10.0} ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

RogerB/afro-xlmr-small-finetuned-kintweetsD

RogerB

"2023-07-09T16:16:36Z"

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2023-07-09T15:59:59Z"

--- license: afl-3.0 tags: - generated_from_trainer model-index: - name: afro-xlmr-small-finetuned-kintweetsD results: [] ---  # afro-xlmr-small-finetuned-kintweetsD This model is a fine-tuned version of [Davlan/afro-xlmr-small](https://huggingface.co/Davlan/afro-xlmr-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8179 | 1.0 | 900 | 1.6363 | | 1.7094 | 2.0 | 1800 | 1.5927 | | 1.6816 | 3.0 | 2700 | 1.6023 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

tinycompany/BiBo-R1-v0.2

tinycompany

"2025-03-15T21:23:54Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-15T21:22:15Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mci29/sn29_w1m1_h9i7

mci29

"2025-02-07T20:12:38Z"

320

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-07T20:09:32Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rhplus0831/maid-yuzu-v2-mid

rhplus0831

"2024-02-03T04:17:12Z"

4

0

transformers

[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "base_model:smelborp/MixtralOrochi8x7B", "base_model:merge:smelborp/MixtralOrochi8x7B", "base_model:ycros/BagelMIsteryTour-v2-8x7B", "base_model:merge:ycros/BagelMIsteryTour-v2-8x7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-02-03T03:43:41Z"

--- base_model: - smelborp/MixtralOrochi8x7B - ycros/BagelMIsteryTour-v2-8x7B library_name: transformers tags: - mergekit - merge --- # maid-yuzu-v2-mid This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [smelborp/MixtralOrochi8x7B](https://huggingface.co/smelborp/MixtralOrochi8x7B) * [ycros/BagelMIsteryTour-v2-8x7B](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: model: path: smelborp/MixtralOrochi8x7B dtype: bfloat16 merge_method: slerp parameters: t: - value: 0.375 slices: - sources: - layer_range: [0, 32] model: model: path: smelborp/MixtralOrochi8x7B - layer_range: [0, 32] model: model: path: ycros/BagelMIsteryTour-v2-8x7B ```

TheBloke/llama2_7b_merge_orcafamily-GPTQ

TheBloke

"2023-11-20T18:19:45Z"

11

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "en", "dataset:Open-Orca/SlimOrca", "dataset:beaugogh/openorca-multiplechoice-10k", "base_model:yeen214/llama2_7b_merge_orcafamily", "base_model:quantized:yeen214/llama2_7b_merge_orcafamily", "license:mit", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]

text-generation

"2023-11-20T17:55:43Z"

--- base_model: yeen214/llama2_7b_merge_orcafamily datasets: - Open-Orca/SlimOrca - beaugogh/openorca-multiplechoice-10k inference: false language: - en license: mit metrics: - accuracy model_creator: yeen heui yeen model_name: Llama2 7B Merge Orcafamily model_type: llama prompt_template: 'Info on prompt template will be added shortly. ' quantized_by: TheBloke ---    <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Llama2 7B Merge Orcafamily - GPTQ - Model creator: [yeen heui yeen](https://huggingface.co/yeen214) - Original model: [Llama2 7B Merge Orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily)  # Description This repo contains GPTQ model files for [yeen heui yeen's Llama2 7B Merge Orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GGUF) * [yeen heui yeen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily)   ## Prompt template: TBC ``` Info on prompt template will be added shortly. ```   ## Licensing The creator of the source model has listed its license as `mit`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [yeen heui yeen's Llama2 7B Merge Orcafamily](https://huggingface.co/yeen214/llama2_7b_merge_orcafamily).   ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know!   ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.62 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |   ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/llama2_7b_merge_orcafamily-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/llama2_7b_merge_orcafamily-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `llama2_7b_merge_orcafamily-GPTQ`: ```shell mkdir llama2_7b_merge_orcafamily-GPTQ huggingface-cli download TheBloke/llama2_7b_merge_orcafamily-GPTQ --local-dir llama2_7b_merge_orcafamily-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir llama2_7b_merge_orcafamily-GPTQ huggingface-cli download TheBloke/llama2_7b_merge_orcafamily-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir llama2_7b_merge_orcafamily-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir llama2_7b_merge_orcafamily-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/llama2_7b_merge_orcafamily-GPTQ --local-dir llama2_7b_merge_orcafamily-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/llama2_7b_merge_orcafamily-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/llama2_7b_merge_orcafamily-GPTQ`. - To download from a specific branch, enter for example `TheBloke/llama2_7b_merge_orcafamily-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `llama2_7b_merge_orcafamily-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!   ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/llama2_7b_merge_orcafamily-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''Info on prompt template will be added shortly. ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ```   ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/llama2_7b_merge_orcafamily-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''Info on prompt template will be added shortly. ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ```   ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: yeen heui yeen's Llama2 7B Merge Orcafamily This model is based on the LLama 7b model as a backbone, and datasets from various Orcas have been fine-tuned and merged. The three models were combined, and the model with the best ARC and MMLU performance was given the highest weight. First: fine-tuning beaugogh/openorca-multiplechoice-10k on llama2 7b, but using the NEFTune method. Second: model fine-tuned with the SlimOrca dataset on llama2 7b. Third : Model with beaugogh/openorca-multiplechoice-10k fine-tuned on llama2 7b. We'll add the results once we have the official results

ClarenceDan/b1539395-e53a-46d2-bdd9-e7009913d702

ClarenceDan

"2025-01-18T11:18:23Z"

8

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "region:us" ]

null

"2025-01-18T11:00:21Z"

--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: b1539395-e53a-46d2-bdd9-e7009913d702 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-1.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4ee00b5ffc79c413_train_data.json ds_type: json format: custom path: /workspace/input_data/4ee00b5ffc79c413_train_data.json type: field_instruction: seq field_output: labels_str format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/b1539395-e53a-46d2-bdd9-e7009913d702 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/4ee00b5ffc79c413_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 7ffb5512-24ac-400f-b2e1-903a11f0a7da wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 7ffb5512-24ac-400f-b2e1-903a11f0a7da warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # b1539395-e53a-46d2-bdd9-e7009913d702 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0767 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.3681 | 0.0000 | 1 | 2.1789 | | 1.9003 | 0.0001 | 3 | 2.1549 | | 1.6763 | 0.0002 | 6 | 1.7226 | | 1.1284 | 0.0003 | 9 | 1.0767 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

shibajustfor/274b9903-7e15-48a6-8eb8-6843490553e3

shibajustfor

"2025-02-24T00:50:05Z"

0

peft

[ "peft", "qwen2", "generated_from_trainer", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2-0.5B-Instruct", "region:us" ]

null

"2025-02-24T00:50:01Z"

--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2-0.5B-Instruct model-index: - name: shibajustfor/274b9903-7e15-48a6-8eb8-6843490553e3 results: [] ---  # shibajustfor/274b9903-7e15-48a6-8eb8-6843490553e3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2688 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Harshini2004/mbart_en_te_model

Harshini2004

"2025-02-19T17:32:12Z"

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "dataset:flores", "base_model:facebook/mbart-large-50-many-to-many-mmt", "base_model:finetune:facebook/mbart-large-50-many-to-many-mmt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2025-02-19T17:26:44Z"

--- library_name: transformers base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer datasets: - flores model-index: - name: mbart_en_te_model results: [] ---  # mbart_en_te_model This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the flores dataset. It achieves the following results on the evaluation set: - Loss: 0.8832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 100 | 0.9786 | | No log | 2.0 | 200 | 0.8959 | | No log | 3.0 | 300 | 0.8832 | ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.1 - Tokenizers 0.21.0

harikc456/vizdoom_health_gathering_supreme

harikc456

"2023-03-25T03:39:54Z"

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-03-25T03:39:41Z"

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.07 +/- 5.48 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r harikc456/vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF

mradermacher

"2025-01-02T23:31:44Z"

13

0

transformers

[ "transformers", "gguf", "Safetensors", "text-generation-inference", "merge", "en", "base_model:MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28", "base_model:quantized:MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-01-02T23:07:26Z"

--- base_model: MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28 language: - en library_name: transformers license: apache-2.0 model_creator: MaziyarPanahi model_name: Experiment26Neuralsirkrishna_Strangemerges_30Experiment28 quantized_by: mradermacher tags: - Safetensors - text-generation-inference - merge --- ## About      static quants of https://huggingface.co/MaziyarPanahi/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28-GGUF/resolve/main/Experiment26Neuralsirkrishna_Strangemerges_30Experiment28.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2

Zoyd

"2024-06-04T15:55:14Z"

76

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]

text-generation

"2024-06-04T15:48:07Z"

--- base_model: - Nitral-AI/Poppy-1.35-Phase1 - Nitral-AI/Pp-72xra1 library_name: transformers tags: - mergekit - merge license: other language: - en --- **Exllamav2** quant (**exl2** / **8.0 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2)**</center> | <center>3478 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_0bpw_exl2)**</center> | <center>3894 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2)**</center> | <center>4518 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_0bpw_exl2)**</center> | <center>4727 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_25bpw_exl2)**</center> | <center>4935 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2)**</center> | <center>5559 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_0bpw_exl2)**</center> | <center>6489 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2)**</center> | <center>6909 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2)**</center> | <center>8123 MB</center> | <center>8</center> | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/Boje781GkTdYgORTYGI6r.png) # "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences. # Note: This variant is an attempt to get something closer to 0.72 while maintaining the improvements of 1.30. # : [Presets in repo folder](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets). # If you want to use vision functionality: You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). And need to load the specified **mmproj** file: [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16). ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Nitral-AI/Pp-72xra1 layer_range: [0, 32] - model: Nitral-AI/Poppy-1.35-Phase1 layer_range: [0, 32] merge_method: slerp base_model: Nitral-AI/Pp-72xra1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

omersubasi/xlm-roberta-base-finetuned-panx-it

omersubasi

"2023-12-08T05:57:10Z"

3

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2023-12-08T05:52:04Z"

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8218390804597702 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2503 - F1: 0.8218 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8253 | 1.0 | 70 | 0.3503 | 0.7160 | | 0.2781 | 2.0 | 140 | 0.2643 | 0.8148 | | 0.1871 | 3.0 | 210 | 0.2503 | 0.8218 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.1.0+cu118 - Datasets 1.16.1 - Tokenizers 0.15.0

swanggl/movie-genre-classifier

swanggl

"2025-03-28T07:14:34Z"

49

0

transformers

[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2025-03-27T21:00:24Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>503</h1> <p>We had to rate limit you. To continue using our service, please log in or create an account.</p> </div> </main> </body> </html>

fnlp/SmolLM-135M-GQA-d_kv_128

fnlp

"2025-03-13T07:23:55Z"

27

0

null

[ "safetensors", "llama", "text-generation", "dataset:HuggingFaceTB/smollm-corpus", "arxiv:2502.14837", "base_model:HuggingFaceTB/SmolLM-135M", "base_model:finetune:HuggingFaceTB/SmolLM-135M", "license:apache-2.0", "region:us" ]

text-generation

"2025-03-04T11:30:23Z"

--- license: apache-2.0 datasets: - HuggingFaceTB/smollm-corpus base_model: - HuggingFaceTB/SmolLM-135M pipeline_tag: text-generation --- **Research Paper** ["Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs"](https://arxiv.org/abs/2502.14837) ## Inference - Step 1: Download the [**monkey patch file**](https://github.com/JT-Ushio/MHA2MLA/blob/main/src/mha2mla/monkey_patch.py). ```shell wget https://raw.githubusercontent.com/JT-Ushio/MHA2MLA/refs/heads/main/src/mha2mla/monkey_patch.py ``` - Step 2(Option): For MHA2MLA models using Partial-RoPE 2-nrom method, Download the [**qk_2-norm file**](https://github.com/JT-Ushio/MHA2MLA/tree/main/utils). Take `qk_tensor_135M.pth` as an example: ```shell wget https://github.com/JT-Ushio/MHA2MLA/raw/refs/heads/main/utils/qk_tensor_135M.pth ``` - Step 3: Download the [MHA2MLA models](https://huggingface.co/fnlp/SmolLM-135M-GQA-d_kv_128) and run inference. Take `fnlp/SmolLM-135M-GQA-d_kv_128` as an example: ```python import torch from transformers import AutoConfig, AutoTokenizer, LlamaForCausalLM from monkey_patch import infer_monkey_patch model_name = "fnlp/SmolLM-135M-GQA-d_kv_128" # Monkey Patch: MHA -> MLA config = AutoConfig.from_pretrained(model_name) if "RoPE" in config: config.RoPE["qk_tensor_path"] = "qk_tensor_135M.pth" # Configuration for Specific Models infer_monkey_patch(config.RoPE) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = LlamaForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.bfloat16).cuda() # Generate text = "Which American-born Sinclair won the Nobel Prize for Literature in 1930?" inputs = tokenizer(text, return_tensors="pt").to(model.device) generation_kwargs = {"do_sample": False, "use_cache": True, "max_new_tokens": 128} output = model.generate(**inputs, **generation_kwargs) print(tokenizer.decode(output[0], skip_special_tokens=True)) # - Sinclair Lewis ``` ## Citation ``` @misc{ji2025economicalinferenceenablingdeepseeks, title={Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs}, author={Tao Ji and Bin Guo and Yuanbin Wu and Qipeng Guo and Lixing Shen and Zhan Chen and Xipeng Qiu and Qi Zhang and Tao Gui}, year={2025}, eprint={2502.14837}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.14837}, } ```

Remex23/llama-2-finetune-elsa

Remex23

"2024-06-04T13:26:36Z"

0

null

[ "llama-2", "fine-tuning", "causal-lm", "en", "license:apache-2.0", "region:us" ]

null

"2024-06-04T13:08:10Z"

--- language: en tags: - llama-2 - fine-tuning - causal-lm license: apache-2.0 --- # Llama-2-finetune-Elsa This is a fine-tuned version of the Llama-2-7b-chat model using the `Remex23/counselchat-llama2-full` dataset.

qtdy/roberta-base-klue-ynat-classification

qtdy

"2025-02-13T02:07:41Z"

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-02-13T02:07:13Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

minji222/mistral_lora_clm_with_added_tokens

minji222

"2024-04-09T07:38:53Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2024-04-09T05:26:00Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

aardvark-labs/stp-classifier-4-2

aardvark-labs

"2025-03-13T12:17:31Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-03-13T10:03:48Z"

--- library_name: transformers ---

RE-N-Y/cc3m-transformer_blocks.18-1

RE-N-Y

"2024-11-18T04:19:08Z"

5

0

finebooru

[ "finebooru", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

"2024-11-18T04:11:47Z"

--- library_name: finebooru tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/RE-N-Y/finebooru - Docs: [More Information Needed]

Ahmed007/hossam-t5

Ahmed007

"2024-02-18T09:09:48Z"

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:UBC-NLP/octopus", "base_model:finetune:UBC-NLP/octopus", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-02-18T01:30:28Z"

--- base_model: UBC-NLP/octopus tags: - generated_from_trainer model-index: - name: hossam-t5 results: [] ---  # hossam-t5 This model is a fine-tuned version of [UBC-NLP/octopus](https://huggingface.co/UBC-NLP/octopus) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1

mav23/Qwen2.5-14B-UpToDate-GGUF

mav23

"2024-11-19T03:05:39Z"

14

0

transformers

[ "transformers", "gguf", "text-generation-inference", "unsloth", "qwen2", "trl", "sft", "en", "base_model:CultriX/Qwen2.5-14B-MegaMerge-pt2", "base_model:quantized:CultriX/Qwen2.5-14B-MegaMerge-pt2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2024-11-19T01:06:53Z"

--- base_model: CultriX/Qwen2.5-14B-MegaMerge-pt2 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** CultriX - **License:** apache-2.0 - **Finetuned from model :** CultriX/Qwen2.5-14B-MegaMerge-pt2 This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

John6666/hentai-cinematic-pony-v2-sdxl

John6666

"2024-07-11T22:53:44Z"

60

2

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "cinematic", "pony", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

"2024-07-11T22:49:18Z"

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - cinematic - pony --- Original model is [here](https://civitai.com/models/492705/hentaicinematicpony?modelVersionId=637173).

mradermacher/Llama-3-22B-Instruct-v0.1-GGUF

mradermacher

"2024-05-31T09:01:44Z"

9

2

transformers

[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]

null

"2024-05-30T21:36:35Z"

--- base_model: DataGuard/Llama-3-22B-Instruct-v0.1 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About      static quants of https://huggingface.co/DataGuard/Llama-3-22B-Instruct-v0.1  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q2_K.gguf) | Q2_K | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ3_XS.gguf) | IQ3_XS | 9.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 10.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ3_S.gguf) | IQ3_S | 10.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ3_M.gguf) | IQ3_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 11.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 12.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 13.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 13.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 15.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 16.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q6_K.gguf) | Q6_K | 18.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-22B-Instruct-v0.1-GGUF/resolve/main/Llama-3-22B-Instruct-v0.1.Q8_0.gguf) | Q8_0 | 24.2 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

sail-rvc/Principal_of_the_Thing__RVC_V2_-_500_Epochs_

sail-rvc

"2023-07-14T07:30:06Z"

1

0

transformers

[ "transformers", "rvc", "sail-rvc", "audio-to-audio", "endpoints_compatible", "region:us" ]

audio-to-audio

"2023-07-14T07:29:51Z"

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # Principal_of_the_Thing__RVC_V2_-_500_Epochs_ ## RVC Model ![banner](https://i.imgur.com/xocCjhH.jpg) This model repo was automatically generated. Date: 2023-07-14 07:30:06 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

jwhj/Qwen2.5-Math-1.5B-SFT

jwhj

"2024-12-10T06:28:58Z"

158

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-12-10T05:37:18Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

P3ps/distilbert-amazon-shoe-reviews

P3ps

"2023-04-20T11:34:31Z"

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2023-04-20T11:07:39Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-amazon-shoe-reviews results: [] ---  # distilbert-amazon-shoe-reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9519 - Accuracy: 0.5757 - F1: [0.63178677 0.45622938 0.50453543 0.55380711 0.73119358] - Precision: [0.62256809 0.46798542 0.48583569 0.58248799 0.71751969] - Recall: [0.64128257 0.4450495 0.52473228 0.52781809 0.74539877] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------------------------------------------------:|:--------------------------------------------------------:|:--------------------------------------------------------:| | 0.9652 | 1.0 | 2813 | 0.9519 | 0.5757 | [0.63178677 0.45622938 0.50453543 0.55380711 0.73119358] | [0.62256809 0.46798542 0.48583569 0.58248799 0.71751969] | [0.64128257 0.4450495 0.52473228 0.52781809 0.74539877] | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3

sehilnlf/model_v3_v2

sehilnlf

"2024-05-26T18:34:55Z"

28

0

transformers

[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large", "base_model:finetune:facebook/bart-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2024-05-26T06:54:37Z"

--- license: apache-2.0 base_model: facebook/bart-large tags: - text2text-generation - generated_from_trainer metrics: - sacrebleu model-index: - name: model_v3_v2 results: [] ---  # model_v3_v2 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5669 - Sacrebleu: 66.8302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | |:-------------:|:-----:|:----:|:---------------:|:---------:| | No log | 0.99 | 54 | 0.6545 | 66.3234 | | No log | 1.99 | 109 | 0.5940 | 66.8342 | | No log | 2.96 | 162 | 0.5669 | 66.8302 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

0xagentai/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jumping_poisonous_bear

0xagentai

"2025-04-20T05:13:03Z"

1

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am jumping poisonous bear", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-15T08:22:48Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

q631599119/model2

q631599119

"2025-03-21T00:50:12Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-03-21T00:50:11Z"

--- license: apache-2.0 ---

zyh571p/whisper-small-finetuned

zyh571p

"2024-05-23T18:13:32Z"

92

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2024-05-23T14:33:50Z"

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-finetuned results: [] ---  # whisper-small-finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:------:| | 0.0 | 4.5662 | 1000 | 0.0000 | 0.1349 | | 0.0 | 9.1324 | 2000 | 0.0000 | 0.0337 | | 0.0 | 13.6986 | 3000 | 0.0000 | 0.0337 | | 0.0 | 18.2648 | 4000 | 0.0000 | 0.0337 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

ReLURavioli/rlur0120

ReLURavioli

"2025-03-04T11:06:37Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-04T11:06:06Z"

--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** ReLURavioli - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ethicalabs/Kurtis-Qwen2.5-0.5B-Instruct-PEFT

ethicalabs

"2025-03-02T17:29:36Z"

0

peft

[ "peft", "safetensors", "text-generation-inference", "text-generation", "en", "dataset:mrs83/kurtis_mental_health_final", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "license:mit", "region:us" ]

text-generation

"2025-03-02T16:56:58Z"

--- library_name: peft license: mit datasets: - mrs83/kurtis_mental_health_final language: - en base_model: - Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation tags: - text-generation-inference --- # Model Card for Kurtis Kurtis is a mental-health AI assistant designed with empathy at its core. Unlike other AI models that aim for peak efficiency, Kurtis prioritizes understanding, emotional nuance, and meaningful conversations. It won’t solve complex math problems or write code, nor will it generate images or videos. Instead, Kurtis focuses on being a thoughtful companion, offering support, perspective, and human-like dialogue. It doesn’t strive to break records or chase artificial intelligence supremacy—its goal is to create a space for genuine interaction. Whether you need someone to talk to, reflect on ideas with, or engage in insightful discussion, Kurtis is there to listen and respond in an understanding way.

maxfrax/distilbert-base-uncased-finetuned-emotion

maxfrax

"2024-02-16T17:32:59Z"

93

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-02-16T17:20:28Z"

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.926 - name: F1 type: f1 value: 0.9258243133918047 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2134 - Accuracy: 0.926 - F1: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3212 | 0.906 | 0.9047 | | No log | 2.0 | 500 | 0.2134 | 0.926 | 0.9258 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

genow2/123

genow2

"2025-04-18T21:46:05Z"

0

null

[ "region:us" ]

null

"2025-04-18T21:46:05Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>

tomrb/bettercallbloom-560m

tomrb

"2022-10-17T12:39:37Z"

133

0

transformers

[ "transformers", "pytorch", "bloom", "text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2022-10-16T13:09:33Z"

--- language: en widget: - text: "my example goes here in the requested language" license: mit --- # WORK IN PROGRESS # bettercallbloom-560m Finetuned bloom-560m model on the PileOfLaw - r/legal_advice ## Model description ## Intended uses & limitations ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | | | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |

oljik/tut_unsloth_3b_lora_model

oljik

"2025-02-18T23:32:57Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-02-18T23:32:44Z"

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** oljik - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Benjaminpwh/xlsr-toratan-60-copt

Benjaminpwh

"2025-03-26T17:51:13Z"

0

transformers

[ "transformers", "safetensors", "wav2vec2", "pretraining", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-03-26T17:14:39Z"

--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: xlsr-toratan-60-copt results: [] ---  # xlsr-toratan-60-copt This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1

ClarenceDan/a5ce742d-11ad-4f04-9cb9-5b3888224913

ClarenceDan

"2025-03-04T17:05:48Z"

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:DeepMount00/Llama-3-8b-Ita", "base_model:adapter:DeepMount00/Llama-3-8b-Ita", "license:llama3", "region:us" ]

null

"2025-03-04T16:28:54Z"

--- library_name: peft license: llama3 base_model: DeepMount00/Llama-3-8b-Ita tags: - axolotl - generated_from_trainer model-index: - name: a5ce742d-11ad-4f04-9cb9-5b3888224913 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: DeepMount00/Llama-3-8b-Ita bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e5bda4a33546a6d1_train_data.json ds_type: json format: custom path: /workspace/input_data/e5bda4a33546a6d1_train_data.json type: field_input: lang field_instruction: sentence1 field_output: sentence2 format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/a5ce742d-11ad-4f04-9cb9-5b3888224913 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/e5bda4a33546a6d1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <|eot_id|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 791b8a0f-4f7d-4883-814e-024386776e92 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 791b8a0f-4f7d-4883-814e-024386776e92 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # a5ce742d-11ad-4f04-9cb9-5b3888224913 This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.7645 | 0.0001 | 1 | 4.7227 | | 5.0736 | 0.0003 | 3 | 4.6863 | | 4.0429 | 0.0006 | 6 | 3.8167 | | 2.2522 | 0.0009 | 9 | 1.8158 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Prisma-Multimodal/imagenet-sweep-vanilla-x64-Spatial_max_0-hook_resid_post-989.203430175781-99

Prisma-Multimodal

"2025-01-30T08:36:29Z"

10

0

null

[ "region:us" ]

null

"2025-01-30T08:36:17Z"

# CLIP Sparse Autoencoder Checkpoint This model is a sparse autoencoder trained on CLIP's internal representations. ## Model Details ### Architecture - **Layer**: 0 - **Layer Type**: hook_resid_post - **Model**: open-clip:laion/CLIP-ViT-B-32-DataComp.XL-s13B-b90K - **Dictionary Size**: 49152 - **Input Dimension**: 768 - **Expansion Factor**: 64 - **CLS Token Only**: False ### Training - **Training Images**: 1299936 - **Learning Rate**: 0.0006 - **L1 Coefficient**: 0.0000 - **Batch Size**: 4096 - **Context Size**: 49 ## Performance Metrics ### Sparsity - **L0 (Active Features)**: 989.2034 - **Dead Features**: 0 - **Mean Passes Since Fired**: 8.6203 ### Reconstruction - **Explained Variance**: 0.9999 - **Explained Variance Std**: 0.0004 - **MSE Loss**: 0.0000 - **L1 Loss**: 2368.7610 - **Overall Loss**: 0.0000 ## Training Details - **Training Duration**: 4395 seconds - **Final Learning Rate**: 0.0000 - **Warm Up Steps**: 200 - **Gradient Clipping**: 1 ## Additional Information - **Original Checkpoint Path**: /network/scratch/p/praneet.suresh/imgnet_checkpoints/52afe93d-tinyclip_sae_16_hyperparam_sweep_lr/n_images_1300020.pt - **Wandb Run**: https://wandb.ai/perceptual-alignment/vanilla-imagenet-Spatial_only-012-sweep/runs/ny207vem - **Random Seed**: 42

dmartincc/vedt-lg

dmartincc

"2025-03-12T17:22:02Z"

32

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-06-27T15:29:14Z"

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - f1 - accuracy model-index: - name: vedt-lg results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: F1 type: f1 value: 0.93 - name: Accuracy type: accuracy value: 0.92 ---  # vedt-lg This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1817 - F1: 0.93 - Roc Auc: 0.95 - Accuracy: 0.92 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|:--------:| | 0.5369 | 1.0 | 122 | 0.5339 | 0.53 | 0.67 | 0.41 | | 0.3995 | 2.0 | 245 | 0.3591 | 0.8 | 0.84 | 0.73 | | 0.2357 | 3.0 | 367 | 0.2492 | 0.89 | 0.92 | 0.88 | | 0.1409 | 4.0 | 490 | 0.2015 | 0.91 | 0.93 | 0.9 | | 0.1137 | 4.98 | 610 | 0.1817 | 0.93 | 0.95 | 0.92 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1

mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF

mradermacher

"2025-01-28T10:44:50Z"

550

0

transformers

[ "transformers", "gguf", "en", "dataset:pankajmathur/orca_mini_v1_dataset", "dataset:pankajmathur/orca_mini_v8_sharegpt_format", "base_model:pankajmathur/orca_mini_v9_5_1B-Instruct_preview", "base_model:quantized:pankajmathur/orca_mini_v9_5_1B-Instruct_preview", "license:llama3.2", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-01-28T10:05:29Z"

--- base_model: pankajmathur/orca_mini_v9_5_1B-Instruct_preview datasets: - pankajmathur/orca_mini_v1_dataset - pankajmathur/orca_mini_v8_sharegpt_format language: - en library_name: transformers license: llama3.2 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/pankajmathur/orca_mini_v9_5_1B-Instruct_preview  static quants are available at https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ1_M.gguf) | i1-IQ1_M | 0.5 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_S.gguf) | i1-IQ2_S | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ2_M.gguf) | i1-IQ2_M | 0.6 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q2_K.gguf) | i1-Q2_K | 0.7 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.7 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.7 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_S.gguf) | i1-IQ3_S | 0.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ3_M.gguf) | i1-IQ3_M | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.8 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.9 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_0.gguf) | i1-Q4_0 | 0.9 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q4_1.gguf) | i1-Q4_1 | 0.9 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.0 | | | [GGUF](https://huggingface.co/mradermacher/orca_mini_v9_5_1B-Instruct_preview-i1-GGUF/resolve/main/orca_mini_v9_5_1B-Instruct_preview.i1-Q6_K.gguf) | i1-Q6_K | 1.1 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Fragko/qwen2-VL-7b-instruct-leaves-from-field-diagnosis

Fragko

"2025-03-12T14:19:25Z"

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2-VL-2B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-2B-Instruct", "endpoints_compatible", "region:us" ]

null

"2025-03-12T00:56:36Z"

--- base_model: Qwen/Qwen2-VL-2B-Instruct library_name: transformers model_name: qwen2-VL-7b-instruct-leaves-from-field-diagnosis tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-VL-7b-instruct-leaves-from-field-diagnosis This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Fragko/qwen2-VL-7b-instruct-leaves-from-field-diagnosis", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gfragko-technical-university-of-crete/qwen2-VL-7b-instruct-leaves-from-field-diagnosis/runs/anfp2n14) This model was trained with SFT. ### Framework versions - TRL: 0.15.1 - Transformers: 4.50.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.3.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t75_e35_non_member_shadow11

FounderOfHuggingface

"2023-12-07T12:01:09Z"

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

"2023-12-07T12:01:07Z"

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

MayBashendy/ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run2_AugV5_k7_task1_organization

MayBashendy

"2025-01-14T19:11:01Z"

7

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-01-14T18:57:36Z"

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run2_AugV5_k7_task1_organization results: [] ---  # ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run2_AugV5_k7_task1_organization This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0657 - Qwk: 0.5355 - Mse: 1.0657 - Rmse: 1.0323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:-------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0571 | 2 | 5.3430 | -0.0131 | 5.3430 | 2.3115 | | No log | 0.1143 | 4 | 3.1272 | 0.0649 | 3.1272 | 1.7684 | | No log | 0.1714 | 6 | 2.6556 | -0.1003 | 2.6556 | 1.6296 | | No log | 0.2286 | 8 | 2.4195 | -0.1156 | 2.4195 | 1.5555 | | No log | 0.2857 | 10 | 1.7140 | 0.0267 | 1.7140 | 1.3092 | | No log | 0.3429 | 12 | 1.2703 | 0.1423 | 1.2703 | 1.1271 | | No log | 0.4 | 14 | 1.2489 | 0.2203 | 1.2489 | 1.1175 | | No log | 0.4571 | 16 | 1.1863 | 0.2751 | 1.1863 | 1.0892 | | No log | 0.5143 | 18 | 1.2975 | 0.1746 | 1.2975 | 1.1391 | | No log | 0.5714 | 20 | 1.3932 | 0.1370 | 1.3932 | 1.1803 | | No log | 0.6286 | 22 | 1.3680 | 0.1204 | 1.3680 | 1.1696 | | No log | 0.6857 | 24 | 1.2413 | 0.3295 | 1.2413 | 1.1141 | | No log | 0.7429 | 26 | 1.1591 | 0.3779 | 1.1591 | 1.0766 | | No log | 0.8 | 28 | 1.2529 | 0.2372 | 1.2529 | 1.1193 | | No log | 0.8571 | 30 | 1.1474 | 0.3339 | 1.1474 | 1.0712 | | No log | 0.9143 | 32 | 1.1505 | 0.3121 | 1.1505 | 1.0726 | | No log | 0.9714 | 34 | 1.1191 | 0.3563 | 1.1191 | 1.0579 | | No log | 1.0286 | 36 | 1.1045 | 0.3838 | 1.1045 | 1.0510 | | No log | 1.0857 | 38 | 1.0767 | 0.3972 | 1.0767 | 1.0376 | | No log | 1.1429 | 40 | 1.0334 | 0.3862 | 1.0334 | 1.0166 | | No log | 1.2 | 42 | 0.8723 | 0.4907 | 0.8723 | 0.9340 | | No log | 1.2571 | 44 | 0.8736 | 0.5391 | 0.8736 | 0.9347 | | No log | 1.3143 | 46 | 0.8995 | 0.5487 | 0.8995 | 0.9484 | | No log | 1.3714 | 48 | 0.9249 | 0.5679 | 0.9249 | 0.9617 | | No log | 1.4286 | 50 | 0.9743 | 0.5092 | 0.9743 | 0.9871 | | No log | 1.4857 | 52 | 0.9736 | 0.4893 | 0.9736 | 0.9867 | | No log | 1.5429 | 54 | 0.9967 | 0.4736 | 0.9967 | 0.9984 | | No log | 1.6 | 56 | 0.9438 | 0.5147 | 0.9438 | 0.9715 | | No log | 1.6571 | 58 | 1.0154 | 0.5401 | 1.0154 | 1.0077 | | No log | 1.7143 | 60 | 0.9127 | 0.5038 | 0.9127 | 0.9554 | | No log | 1.7714 | 62 | 1.0786 | 0.5033 | 1.0786 | 1.0385 | | No log | 1.8286 | 64 | 1.0134 | 0.5134 | 1.0134 | 1.0067 | | No log | 1.8857 | 66 | 0.8451 | 0.5707 | 0.8451 | 0.9193 | | No log | 1.9429 | 68 | 0.9047 | 0.5867 | 0.9047 | 0.9512 | | No log | 2.0 | 70 | 0.8410 | 0.6429 | 0.8410 | 0.9171 | | No log | 2.0571 | 72 | 0.8599 | 0.5858 | 0.8599 | 0.9273 | | No log | 2.1143 | 74 | 1.0333 | 0.5201 | 1.0333 | 1.0165 | | No log | 2.1714 | 76 | 0.9665 | 0.5218 | 0.9665 | 0.9831 | | No log | 2.2286 | 78 | 0.8244 | 0.5851 | 0.8244 | 0.9080 | | No log | 2.2857 | 80 | 0.8443 | 0.5752 | 0.8443 | 0.9189 | | No log | 2.3429 | 82 | 0.8400 | 0.5657 | 0.8400 | 0.9165 | | No log | 2.4 | 84 | 0.9719 | 0.5372 | 0.9719 | 0.9858 | | No log | 2.4571 | 86 | 0.9467 | 0.5528 | 0.9467 | 0.9730 | | No log | 2.5143 | 88 | 0.7803 | 0.6361 | 0.7803 | 0.8834 | | No log | 2.5714 | 90 | 0.7549 | 0.6035 | 0.7549 | 0.8688 | | No log | 2.6286 | 92 | 0.8418 | 0.5549 | 0.8418 | 0.9175 | | No log | 2.6857 | 94 | 0.7118 | 0.6245 | 0.7118 | 0.8437 | | No log | 2.7429 | 96 | 0.9979 | 0.5331 | 0.9979 | 0.9989 | | No log | 2.8 | 98 | 1.3117 | 0.4883 | 1.3117 | 1.1453 | | No log | 2.8571 | 100 | 1.0906 | 0.5105 | 1.0906 | 1.0443 | | No log | 2.9143 | 102 | 0.6861 | 0.6388 | 0.6861 | 0.8283 | | No log | 2.9714 | 104 | 0.7704 | 0.5976 | 0.7704 | 0.8777 | | No log | 3.0286 | 106 | 0.7716 | 0.6420 | 0.7716 | 0.8784 | | No log | 3.0857 | 108 | 0.6576 | 0.6805 | 0.6576 | 0.8109 | | No log | 3.1429 | 110 | 0.8588 | 0.5971 | 0.8588 | 0.9267 | | No log | 3.2 | 112 | 1.0898 | 0.5213 | 1.0898 | 1.0439 | | No log | 3.2571 | 114 | 1.1132 | 0.5205 | 1.1132 | 1.0551 | | No log | 3.3143 | 116 | 1.0380 | 0.5333 | 1.0380 | 1.0188 | | No log | 3.3714 | 118 | 1.0357 | 0.5401 | 1.0357 | 1.0177 | | No log | 3.4286 | 120 | 0.9567 | 0.6132 | 0.9567 | 0.9781 | | No log | 3.4857 | 122 | 1.0190 | 0.5522 | 1.0190 | 1.0095 | | No log | 3.5429 | 124 | 1.2592 | 0.4356 | 1.2592 | 1.1222 | | No log | 3.6 | 126 | 1.1839 | 0.4543 | 1.1839 | 1.0881 | | No log | 3.6571 | 128 | 1.0883 | 0.4830 | 1.0883 | 1.0432 | | No log | 3.7143 | 130 | 1.0148 | 0.5338 | 1.0148 | 1.0074 | | No log | 3.7714 | 132 | 0.7750 | 0.6389 | 0.7750 | 0.8803 | | No log | 3.8286 | 134 | 0.7688 | 0.6161 | 0.7688 | 0.8768 | | No log | 3.8857 | 136 | 0.7777 | 0.6601 | 0.7777 | 0.8819 | | No log | 3.9429 | 138 | 0.9604 | 0.6227 | 0.9604 | 0.9800 | | No log | 4.0 | 140 | 1.2622 | 0.5311 | 1.2622 | 1.1235 | | No log | 4.0571 | 142 | 1.2558 | 0.5144 | 1.2558 | 1.1206 | | No log | 4.1143 | 144 | 1.0652 | 0.5312 | 1.0652 | 1.0321 | | No log | 4.1714 | 146 | 0.8674 | 0.6355 | 0.8674 | 0.9313 | | No log | 4.2286 | 148 | 0.7753 | 0.6502 | 0.7753 | 0.8805 | | No log | 4.2857 | 150 | 0.8393 | 0.6358 | 0.8393 | 0.9161 | | No log | 4.3429 | 152 | 0.8959 | 0.6019 | 0.8959 | 0.9465 | | No log | 4.4 | 154 | 0.8972 | 0.5988 | 0.8972 | 0.9472 | | No log | 4.4571 | 156 | 0.7496 | 0.6873 | 0.7496 | 0.8658 | | No log | 4.5143 | 158 | 0.7267 | 0.6988 | 0.7267 | 0.8524 | | No log | 4.5714 | 160 | 0.7081 | 0.6854 | 0.7081 | 0.8415 | | No log | 4.6286 | 162 | 0.7194 | 0.6824 | 0.7194 | 0.8481 | | No log | 4.6857 | 164 | 0.8391 | 0.6738 | 0.8391 | 0.9160 | | No log | 4.7429 | 166 | 1.1286 | 0.5640 | 1.1286 | 1.0623 | | No log | 4.8 | 168 | 1.0770 | 0.5831 | 1.0770 | 1.0378 | | No log | 4.8571 | 170 | 0.8046 | 0.6916 | 0.8046 | 0.8970 | | No log | 4.9143 | 172 | 0.7048 | 0.6692 | 0.7048 | 0.8395 | | No log | 4.9714 | 174 | 0.7162 | 0.6755 | 0.7162 | 0.8463 | | No log | 5.0286 | 176 | 0.7766 | 0.6634 | 0.7766 | 0.8812 | | No log | 5.0857 | 178 | 0.9326 | 0.6175 | 0.9326 | 0.9657 | | No log | 5.1429 | 180 | 1.0926 | 0.5442 | 1.0926 | 1.0453 | | No log | 5.2 | 182 | 0.9450 | 0.6135 | 0.9450 | 0.9721 | | No log | 5.2571 | 184 | 0.7525 | 0.6745 | 0.7525 | 0.8674 | | No log | 5.3143 | 186 | 0.7507 | 0.6302 | 0.7507 | 0.8664 | | No log | 5.3714 | 188 | 0.7302 | 0.6167 | 0.7302 | 0.8545 | | No log | 5.4286 | 190 | 0.7342 | 0.6394 | 0.7342 | 0.8568 | | No log | 5.4857 | 192 | 0.8565 | 0.6562 | 0.8565 | 0.9255 | | No log | 5.5429 | 194 | 0.9119 | 0.5911 | 0.9119 | 0.9550 | | No log | 5.6 | 196 | 0.9576 | 0.5911 | 0.9576 | 0.9786 | | No log | 5.6571 | 198 | 0.8575 | 0.6347 | 0.8575 | 0.9260 | | No log | 5.7143 | 200 | 0.7727 | 0.6947 | 0.7727 | 0.8790 | | No log | 5.7714 | 202 | 0.7947 | 0.6674 | 0.7947 | 0.8915 | | No log | 5.8286 | 204 | 0.9209 | 0.6373 | 0.9209 | 0.9597 | | No log | 5.8857 | 206 | 1.0566 | 0.6122 | 1.0566 | 1.0279 | | No log | 5.9429 | 208 | 0.9640 | 0.6463 | 0.9640 | 0.9819 | | No log | 6.0 | 210 | 0.8122 | 0.6719 | 0.8122 | 0.9012 | | No log | 6.0571 | 212 | 0.7881 | 0.6924 | 0.7881 | 0.8877 | | No log | 6.1143 | 214 | 0.8804 | 0.6246 | 0.8804 | 0.9383 | | No log | 6.1714 | 216 | 0.9973 | 0.5694 | 0.9973 | 0.9986 | | No log | 6.2286 | 218 | 0.9968 | 0.5608 | 0.9968 | 0.9984 | | No log | 6.2857 | 220 | 0.8269 | 0.6476 | 0.8269 | 0.9093 | | No log | 6.3429 | 222 | 0.7584 | 0.6718 | 0.7584 | 0.8708 | | No log | 6.4 | 224 | 0.6644 | 0.6765 | 0.6644 | 0.8151 | | No log | 6.4571 | 226 | 0.6248 | 0.6804 | 0.6248 | 0.7905 | | No log | 6.5143 | 228 | 0.7130 | 0.6969 | 0.7130 | 0.8444 | | No log | 6.5714 | 230 | 0.9473 | 0.6137 | 0.9473 | 0.9733 | | No log | 6.6286 | 232 | 0.9530 | 0.6280 | 0.9530 | 0.9762 | | No log | 6.6857 | 234 | 0.7536 | 0.7182 | 0.7536 | 0.8681 | | No log | 6.7429 | 236 | 0.7443 | 0.7166 | 0.7443 | 0.8627 | | No log | 6.8 | 238 | 0.8401 | 0.6413 | 0.8401 | 0.9166 | | No log | 6.8571 | 240 | 0.9515 | 0.6228 | 0.9515 | 0.9754 | | No log | 6.9143 | 242 | 0.8301 | 0.6694 | 0.8301 | 0.9111 | | No log | 6.9714 | 244 | 0.7428 | 0.6982 | 0.7428 | 0.8619 | | No log | 7.0286 | 246 | 0.8660 | 0.6178 | 0.8660 | 0.9306 | | No log | 7.0857 | 248 | 0.9069 | 0.5964 | 0.9069 | 0.9523 | | No log | 7.1429 | 250 | 0.9427 | 0.6036 | 0.9427 | 0.9709 | | No log | 7.2 | 252 | 0.8597 | 0.6288 | 0.8597 | 0.9272 | | No log | 7.2571 | 254 | 0.8272 | 0.6288 | 0.8272 | 0.9095 | | No log | 7.3143 | 256 | 0.9297 | 0.6086 | 0.9297 | 0.9642 | | No log | 7.3714 | 258 | 0.8701 | 0.6293 | 0.8701 | 0.9328 | | No log | 7.4286 | 260 | 0.7681 | 0.6944 | 0.7681 | 0.8764 | | No log | 7.4857 | 262 | 0.7007 | 0.7263 | 0.7007 | 0.8371 | | No log | 7.5429 | 264 | 0.6556 | 0.7099 | 0.6556 | 0.8097 | | No log | 7.6 | 266 | 0.6917 | 0.7006 | 0.6917 | 0.8317 | | No log | 7.6571 | 268 | 0.8437 | 0.6470 | 0.8437 | 0.9185 | | No log | 7.7143 | 270 | 0.8517 | 0.6517 | 0.8517 | 0.9229 | | No log | 7.7714 | 272 | 0.7675 | 0.7046 | 0.7675 | 0.8761 | | No log | 7.8286 | 274 | 0.7646 | 0.7054 | 0.7646 | 0.8744 | | No log | 7.8857 | 276 | 0.7847 | 0.6628 | 0.7847 | 0.8858 | | No log | 7.9429 | 278 | 0.8038 | 0.6628 | 0.8038 | 0.8965 | | No log | 8.0 | 280 | 0.7983 | 0.6527 | 0.7983 | 0.8935 | | No log | 8.0571 | 282 | 0.7692 | 0.6788 | 0.7692 | 0.8770 | | No log | 8.1143 | 284 | 0.7306 | 0.6965 | 0.7306 | 0.8548 | | No log | 8.1714 | 286 | 0.7067 | 0.6590 | 0.7067 | 0.8406 | | No log | 8.2286 | 288 | 0.7578 | 0.6885 | 0.7578 | 0.8705 | | No log | 8.2857 | 290 | 0.8385 | 0.6512 | 0.8385 | 0.9157 | | No log | 8.3429 | 292 | 0.9729 | 0.5842 | 0.9729 | 0.9864 | | No log | 8.4 | 294 | 0.8527 | 0.6420 | 0.8527 | 0.9234 | | No log | 8.4571 | 296 | 0.7301 | 0.6967 | 0.7301 | 0.8545 | | No log | 8.5143 | 298 | 0.6768 | 0.6643 | 0.6768 | 0.8227 | | No log | 8.5714 | 300 | 0.6640 | 0.6516 | 0.6640 | 0.8149 | | No log | 8.6286 | 302 | 0.6898 | 0.6664 | 0.6898 | 0.8306 | | No log | 8.6857 | 304 | 0.8909 | 0.6159 | 0.8909 | 0.9439 | | No log | 8.7429 | 306 | 1.1835 | 0.4966 | 1.1835 | 1.0879 | | No log | 8.8 | 308 | 1.2569 | 0.4960 | 1.2569 | 1.1211 | | No log | 8.8571 | 310 | 1.1361 | 0.5189 | 1.1361 | 1.0659 | | No log | 8.9143 | 312 | 1.0565 | 0.5399 | 1.0565 | 1.0279 | | No log | 8.9714 | 314 | 0.8676 | 0.6551 | 0.8676 | 0.9315 | | No log | 9.0286 | 316 | 0.7602 | 0.6899 | 0.7602 | 0.8719 | | No log | 9.0857 | 318 | 0.7732 | 0.6707 | 0.7732 | 0.8793 | | No log | 9.1429 | 320 | 0.8802 | 0.5899 | 0.8802 | 0.9382 | | No log | 9.2 | 322 | 1.0857 | 0.5209 | 1.0857 | 1.0419 | | No log | 9.2571 | 324 | 1.1553 | 0.5265 | 1.1553 | 1.0748 | | No log | 9.3143 | 326 | 1.0771 | 0.5189 | 1.0771 | 1.0378 | | No log | 9.3714 | 328 | 1.0802 | 0.5209 | 1.0802 | 1.0393 | | No log | 9.4286 | 330 | 0.9214 | 0.5783 | 0.9214 | 0.9599 | | No log | 9.4857 | 332 | 0.8058 | 0.6211 | 0.8058 | 0.8977 | | No log | 9.5429 | 334 | 0.7409 | 0.6878 | 0.7409 | 0.8607 | | No log | 9.6 | 336 | 0.7973 | 0.6509 | 0.7973 | 0.8929 | | No log | 9.6571 | 338 | 0.9476 | 0.5867 | 0.9476 | 0.9735 | | No log | 9.7143 | 340 | 0.8953 | 0.5806 | 0.8953 | 0.9462 | | No log | 9.7714 | 342 | 0.7900 | 0.6858 | 0.7900 | 0.8888 | | No log | 9.8286 | 344 | 0.7239 | 0.6774 | 0.7239 | 0.8508 | | No log | 9.8857 | 346 | 0.7151 | 0.6791 | 0.7151 | 0.8457 | | No log | 9.9429 | 348 | 0.7722 | 0.6819 | 0.7722 | 0.8787 | | No log | 10.0 | 350 | 0.9402 | 0.6286 | 0.9402 | 0.9696 | | No log | 10.0571 | 352 | 1.0030 | 0.6179 | 1.0030 | 1.0015 | | No log | 10.1143 | 354 | 0.8716 | 0.6648 | 0.8716 | 0.9336 | | No log | 10.1714 | 356 | 0.7326 | 0.7014 | 0.7326 | 0.8559 | | No log | 10.2286 | 358 | 0.7226 | 0.6905 | 0.7226 | 0.8501 | | No log | 10.2857 | 360 | 0.7907 | 0.6880 | 0.7907 | 0.8892 | | No log | 10.3429 | 362 | 0.9463 | 0.5667 | 0.9463 | 0.9728 | | No log | 10.4 | 364 | 1.0349 | 0.5381 | 1.0349 | 1.0173 | | No log | 10.4571 | 366 | 0.9543 | 0.5932 | 0.9543 | 0.9769 | | No log | 10.5143 | 368 | 0.8436 | 0.6396 | 0.8436 | 0.9185 | | No log | 10.5714 | 370 | 0.8043 | 0.6596 | 0.8043 | 0.8968 | | No log | 10.6286 | 372 | 0.7737 | 0.7008 | 0.7737 | 0.8796 | | No log | 10.6857 | 374 | 0.8094 | 0.6618 | 0.8094 | 0.8996 | | No log | 10.7429 | 376 | 0.7938 | 0.6666 | 0.7938 | 0.8909 | | No log | 10.8 | 378 | 0.7080 | 0.7159 | 0.7080 | 0.8414 | | No log | 10.8571 | 380 | 0.6661 | 0.6835 | 0.6661 | 0.8162 | | No log | 10.9143 | 382 | 0.6601 | 0.6835 | 0.6601 | 0.8125 | | No log | 10.9714 | 384 | 0.6558 | 0.6619 | 0.6558 | 0.8098 | | No log | 11.0286 | 386 | 0.6875 | 0.7120 | 0.6875 | 0.8291 | | No log | 11.0857 | 388 | 0.7488 | 0.6509 | 0.7488 | 0.8653 | | No log | 11.1429 | 390 | 0.8062 | 0.6638 | 0.8062 | 0.8979 | | No log | 11.2 | 392 | 0.7661 | 0.6570 | 0.7661 | 0.8753 | | No log | 11.2571 | 394 | 0.7196 | 0.7086 | 0.7196 | 0.8483 | | No log | 11.3143 | 396 | 0.7788 | 0.6609 | 0.7788 | 0.8825 | | No log | 11.3714 | 398 | 0.8422 | 0.6489 | 0.8422 | 0.9177 | | No log | 11.4286 | 400 | 0.9245 | 0.6167 | 0.9245 | 0.9615 | | No log | 11.4857 | 402 | 0.8394 | 0.6395 | 0.8394 | 0.9162 | | No log | 11.5429 | 404 | 0.8095 | 0.6505 | 0.8095 | 0.8997 | | No log | 11.6 | 406 | 0.7775 | 0.6481 | 0.7775 | 0.8818 | | No log | 11.6571 | 408 | 0.8326 | 0.6582 | 0.8326 | 0.9125 | | No log | 11.7143 | 410 | 0.7396 | 0.7004 | 0.7396 | 0.8600 | | No log | 11.7714 | 412 | 0.6857 | 0.7117 | 0.6857 | 0.8280 | | No log | 11.8286 | 414 | 0.7590 | 0.7058 | 0.7590 | 0.8712 | | No log | 11.8857 | 416 | 0.9415 | 0.6233 | 0.9415 | 0.9703 | | No log | 11.9429 | 418 | 1.0937 | 0.6062 | 1.0937 | 1.0458 | | No log | 12.0 | 420 | 1.0981 | 0.5797 | 1.0981 | 1.0479 | | No log | 12.0571 | 422 | 1.0543 | 0.5637 | 1.0543 | 1.0268 | | No log | 12.1143 | 424 | 1.0139 | 0.5377 | 1.0139 | 1.0069 | | No log | 12.1714 | 426 | 0.9686 | 0.5629 | 0.9686 | 0.9842 | | No log | 12.2286 | 428 | 1.0128 | 0.5138 | 1.0128 | 1.0064 | | No log | 12.2857 | 430 | 1.0498 | 0.5128 | 1.0498 | 1.0246 | | No log | 12.3429 | 432 | 0.8833 | 0.6268 | 0.8833 | 0.9398 | | No log | 12.4 | 434 | 0.7097 | 0.6949 | 0.7097 | 0.8424 | | No log | 12.4571 | 436 | 0.6709 | 0.6879 | 0.6709 | 0.8191 | | No log | 12.5143 | 438 | 0.6722 | 0.6978 | 0.6722 | 0.8198 | | No log | 12.5714 | 440 | 0.7612 | 0.7090 | 0.7612 | 0.8725 | | No log | 12.6286 | 442 | 0.9488 | 0.6221 | 0.9488 | 0.9741 | | No log | 12.6857 | 444 | 0.9146 | 0.6440 | 0.9146 | 0.9563 | | No log | 12.7429 | 446 | 0.7522 | 0.6964 | 0.7522 | 0.8673 | | No log | 12.8 | 448 | 0.6023 | 0.6598 | 0.6023 | 0.7761 | | No log | 12.8571 | 450 | 0.6168 | 0.6413 | 0.6168 | 0.7854 | | No log | 12.9143 | 452 | 0.6096 | 0.6681 | 0.6096 | 0.7808 | | No log | 12.9714 | 454 | 0.6104 | 0.6865 | 0.6104 | 0.7813 | | No log | 13.0286 | 456 | 0.6612 | 0.7128 | 0.6612 | 0.8131 | | No log | 13.0857 | 458 | 0.6447 | 0.7036 | 0.6447 | 0.8029 | | No log | 13.1429 | 460 | 0.6339 | 0.7079 | 0.6339 | 0.7962 | | No log | 13.2 | 462 | 0.6410 | 0.6216 | 0.6410 | 0.8006 | | No log | 13.2571 | 464 | 0.6306 | 0.6425 | 0.6306 | 0.7941 | | No log | 13.3143 | 466 | 0.6213 | 0.6835 | 0.6213 | 0.7882 | | No log | 13.3714 | 468 | 0.6497 | 0.7204 | 0.6497 | 0.8061 | | No log | 13.4286 | 470 | 0.7284 | 0.6667 | 0.7284 | 0.8535 | | No log | 13.4857 | 472 | 0.7837 | 0.6530 | 0.7837 | 0.8853 | | No log | 13.5429 | 474 | 0.7644 | 0.6530 | 0.7644 | 0.8743 | | No log | 13.6 | 476 | 0.7373 | 0.6657 | 0.7373 | 0.8586 | | No log | 13.6571 | 478 | 0.7246 | 0.6857 | 0.7246 | 0.8512 | | No log | 13.7143 | 480 | 0.6883 | 0.6970 | 0.6883 | 0.8297 | | No log | 13.7714 | 482 | 0.6774 | 0.7007 | 0.6774 | 0.8230 | | No log | 13.8286 | 484 | 0.7164 | 0.7116 | 0.7164 | 0.8464 | | No log | 13.8857 | 486 | 0.7260 | 0.7116 | 0.7260 | 0.8521 | | No log | 13.9429 | 488 | 0.7837 | 0.6726 | 0.7837 | 0.8852 | | No log | 14.0 | 490 | 0.8038 | 0.6715 | 0.8038 | 0.8965 | | No log | 14.0571 | 492 | 0.8259 | 0.6629 | 0.8259 | 0.9088 | | No log | 14.1143 | 494 | 0.7970 | 0.6973 | 0.7970 | 0.8928 | | No log | 14.1714 | 496 | 0.7024 | 0.7072 | 0.7024 | 0.8381 | | No log | 14.2286 | 498 | 0.7095 | 0.6971 | 0.7095 | 0.8423 | | 0.3637 | 14.2857 | 500 | 0.7234 | 0.6954 | 0.7234 | 0.8505 | | 0.3637 | 14.3429 | 502 | 0.7055 | 0.7079 | 0.7055 | 0.8399 | | 0.3637 | 14.4 | 504 | 0.7237 | 0.7030 | 0.7237 | 0.8507 | | 0.3637 | 14.4571 | 506 | 0.7712 | 0.6960 | 0.7712 | 0.8782 | | 0.3637 | 14.5143 | 508 | 0.7300 | 0.6960 | 0.7300 | 0.8544 | | 0.3637 | 14.5714 | 510 | 0.6766 | 0.7263 | 0.6766 | 0.8226 | | 0.3637 | 14.6286 | 512 | 0.6835 | 0.7247 | 0.6835 | 0.8268 | | 0.3637 | 14.6857 | 514 | 0.7758 | 0.6851 | 0.7758 | 0.8808 | | 0.3637 | 14.7429 | 516 | 0.8551 | 0.6773 | 0.8551 | 0.9247 | | 0.3637 | 14.8 | 518 | 0.8200 | 0.6915 | 0.8200 | 0.9055 | | 0.3637 | 14.8571 | 520 | 0.7410 | 0.6865 | 0.7410 | 0.8608 | | 0.3637 | 14.9143 | 522 | 0.6627 | 0.6997 | 0.6627 | 0.8140 | | 0.3637 | 14.9714 | 524 | 0.6722 | 0.7049 | 0.6722 | 0.8199 | | 0.3637 | 15.0286 | 526 | 0.7373 | 0.6982 | 0.7373 | 0.8587 | | 0.3637 | 15.0857 | 528 | 0.8253 | 0.7017 | 0.8253 | 0.9085 | | 0.3637 | 15.1429 | 530 | 0.8180 | 0.6814 | 0.8180 | 0.9044 | | 0.3637 | 15.2 | 532 | 0.7457 | 0.6816 | 0.7457 | 0.8636 | | 0.3637 | 15.2571 | 534 | 0.7131 | 0.7012 | 0.7131 | 0.8445 | | 0.3637 | 15.3143 | 536 | 0.7075 | 0.7152 | 0.7075 | 0.8411 | | 0.3637 | 15.3714 | 538 | 0.6597 | 0.7086 | 0.6597 | 0.8122 | | 0.3637 | 15.4286 | 540 | 0.6856 | 0.7224 | 0.6856 | 0.8280 | | 0.3637 | 15.4857 | 542 | 0.7557 | 0.6901 | 0.7557 | 0.8693 | | 0.3637 | 15.5429 | 544 | 0.9779 | 0.6161 | 0.9779 | 0.9889 | | 0.3637 | 15.6 | 546 | 1.1443 | 0.5428 | 1.1443 | 1.0697 | | 0.3637 | 15.6571 | 548 | 1.1605 | 0.5411 | 1.1605 | 1.0773 | | 0.3637 | 15.7143 | 550 | 1.0657 | 0.5355 | 1.0657 | 1.0323 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

texanrangee/ca8a279b-d498-45c6-b1c3-e8a347ae9c4a

texanrangee

"2025-03-15T12:05:23Z"

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-03-15T10:08:43Z"

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Manuel221097/dije

Manuel221097

"2024-04-11T14:06:37Z"

0

null

[ "license:cc-by-nc-sa-2.0", "region:us" ]

null

"2024-04-11T14:06:36Z"

--- license: cc-by-nc-sa-2.0 ---

AndyJamesTurner/suicideDetector

AndyJamesTurner

"2024-04-17T13:49:57Z"

0

sklearn

[ "sklearn", "skops", "text-classification", "license:mit", "region:us" ]

text-classification

"2024-04-12T10:08:45Z"

--- license: mit library_name: sklearn tags: - sklearn - skops - text-classification model_format: pickle model_file: model.pkl --- # Model description Suicide Detection text classification model. PYTHON 3.10 ONLY ## Training Procedure Trained using 0.7 of the the Suicide and Depression Detection dataset (https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch) The model vectorises each text using a trained tfidf vectorizer and then classifies using xgboost. See main.py for further details. ### Hyperparameters <details> <summary> Click to expand </summary> | Hyperparameter | Value | |-------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('tfidf', TfidfVectorizer(min_df=100, ngram_range=(1, 3),<br /> preprocessor=<function preprocessor at 0x7f8d443a30a0>)), ('classifier', XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=None, n_jobs=None,<br /> num_parallel_tree=None, random_state=None, ...))] | | verbose | True | | tfidf | TfidfVectorizer(min_df=100, ngram_range=(1, 3),<br /> preprocessor=<function preprocessor at 0x7f8d443a30a0>) | | classifier | XGBClassifier(base_score=None, booster=None, callbacks=None,<br /> colsample_bylevel=None, colsample_bynode=None,<br /> colsample_bytree=None, device=None, early_stopping_rounds=None,<br /> enable_categorical=False, eval_metric=None, feature_types=None,<br /> gamma=None, grow_policy=None, importance_type=None,<br /> interaction_constraints=None, learning_rate=None, max_bin=None,<br /> max_cat_threshold=None, max_cat_to_onehot=None,<br /> max_delta_step=None, max_depth=None, max_leaves=None,<br /> min_child_weight=None, missing=nan, monotone_constraints=None,<br /> multi_strategy=None, n_estimators=None, n_jobs=None,<br /> num_parallel_tree=None, random_state=None, ...) | | tfidf__analyzer | word | | tfidf__binary | False | | tfidf__decode_error | strict | | tfidf__dtype | <class 'numpy.float64'> | | tfidf__encoding | utf-8 | | tfidf__input | content | | tfidf__lowercase | True | | tfidf__max_df | 1.0 | | tfidf__max_features | | | tfidf__min_df | 100 | | tfidf__ngram_range | (1, 3) | | tfidf__norm | l2 | | tfidf__preprocessor | <function preprocessor at 0x7f8d443a30a0> | | tfidf__smooth_idf | True | | tfidf__stop_words | | | tfidf__strip_accents | | | tfidf__sublinear_tf | False | | tfidf__token_pattern | (?u)\b\w\w+\b | | tfidf__tokenizer | | | tfidf__use_idf | True | | tfidf__vocabulary | | | classifier__objective | binary:logistic | | classifier__base_score | | | classifier__booster | | | classifier__callbacks | | | classifier__colsample_bylevel | | | classifier__colsample_bynode | | | classifier__colsample_bytree | | | classifier__device | | | classifier__early_stopping_rounds | | | classifier__enable_categorical | False | | classifier__eval_metric | | | classifier__feature_types | | | classifier__gamma | | | classifier__grow_policy | | | classifier__importance_type | | | classifier__interaction_constraints | | | classifier__learning_rate | | | classifier__max_bin | | | classifier__max_cat_threshold | | | classifier__max_cat_to_onehot | | | classifier__max_delta_step | | | classifier__max_depth | | | classifier__max_leaves | | | classifier__min_child_weight | | | classifier__missing | nan | | classifier__monotone_constraints | | | classifier__multi_strategy | | | classifier__n_estimators | | | classifier__n_jobs | | | classifier__num_parallel_tree | | | classifier__random_state | | | classifier__reg_alpha | | | classifier__reg_lambda | | | classifier__sampling_method | | | classifier__scale_pos_weight | | | classifier__subsample | | | classifier__tree_method | | | classifier__validate_parameters | | | classifier__verbosity | | </details> ### Model Plot <style>#sk-container-id-1 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;} }#sk-container-id-1 {color: var(--sklearn-color-text); }#sk-container-id-1 pre {padding: 0; }#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; }#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); }#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative; }#sk-container-id-1 div.sk-text-repr-fallback {display: none; }div.sk-parallel-item, div.sk-serial, div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; }/* Parallel-specific style estimator block */#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; }#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; }#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column; }#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; }#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; }#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0; }/* Serial-specific style estimator block */#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is clickable and can be expanded/collapsed. - Pipeline and ColumnTransformer use this feature and define the default style - Estimators will overwrite some part of the style using the `sk-estimator` class *//* Pipeline and ColumnTransformer style (default) */#sk-container-id-1 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background); }/* Toggleable label */ #sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center; }#sk-container-id-1 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); }#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); }/* Toggleable content - dropdown */#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); }#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); }#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; }#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; }/* Pipeline/ColumnTransformer-specific style */#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); }/* Estimator-specific style *//* Colorize estimator box */ #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2); }#sk-container-id-1 div.sk-label label.sk-toggleable__label, #sk-container-id-1 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); }/* On hover, darken the color of the background */ #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); }/* Label box, darken color on hover, fitted */ #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); }/* Estimator label */#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; }#sk-container-id-1 div.sk-label-container {text-align: center; }/* Estimator-specific */ #sk-container-id-1 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); }#sk-container-id-1 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); }/* on hover */ #sk-container-id-1 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); }#sk-container-id-1 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, a:link.sk-estimator-doc-link, a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1); }.sk-estimator-doc-link.fitted, a:link.sk-estimator-doc-link.fitted, a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); }/* On hover */ div.sk-estimator:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover, div.sk-label-container:hover .sk-estimator-doc-link:hover, .sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover, div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, .sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }/* Span, style for the box shown on hovering the info icon */ .sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3); }.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); }.sk-estimator-doc-link:hover span {display: block; }/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-1 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; }#sk-container-id-1 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); }/* On hover */ #sk-container-id-1 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; }#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); } </style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7f8d443a30a0>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">  Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('tfidf',TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7f8d443a30a0>)),('classifier',XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None,early_stopping_rounds=None,enable_categorical=False, eval_metric=None,featur...importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=None, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, multi_strategy=None,n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...))],verbose=True)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> TfidfVectorizer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html">?<span>Documentation for TfidfVectorizer</span></a></label><div class="sk-toggleable__content fitted"><pre>TfidfVectorizer(min_df=100, ngram_range=(1, 3),preprocessor=<function preprocessor at 0x7f8d443a30a0>)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">XGBClassifier</label><div class="sk-toggleable__content fitted"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div> ## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.910317 | | f1 score | 0.910317 | | ROC AUC | 0.969008 | # How to Get Started with the Model ```python import sklearn import dill as pickle from skops import hub_utils from pathlib import Path suicide_detector_repo = Path("./suicide-detector") hub_utils.download( repo_id="AndyJamesTurner/suicideDetector", dst=suicide_detector_repo ) with open(suicide_detector_repo / "model.pkl", 'rb') as file: clf = pickle.load(file) classification = clf.predict(["I want to kill myself"])[0] ``` # Model Evaluation The model was evaluated on a 0.3 holdout split using f1 score, accuracy, confusion matrix and ROC curves. ## Confusion matrix ![Confusion matrix](confusion_matrix.png) ## ROC Curve ![ROC Curve](roc_curve.png) # Classification Report | index | precision | recall | f1-score | support | |--------------|-------------|----------|------------|--------------| | not suicide | 0.891721 | 0.934126 | 0.912431 | 34824 | | suicide | 0.930785 | 0.886491 | 0.908098 | 34799 | | accuracy | 0.910317 | 0.910317 | 0.910317 | 0.910317 | | macro avg | 0.911253 | 0.910308 | 0.910265 | 69623 | | weighted avg | 0.911246 | 0.910317 | 0.910265 | 69623 | # Model Authors This model was created by the following authors: * Andy Turner

punchnami/resnet50-pothole-classification

punchnami

"2024-02-18T00:31:38Z"

28

0

transformers

[ "transformers", "tensorboard", "safetensors", "resnet", "image-classification", "vision", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2024-02-18T00:20:01Z"

--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - image-classification - vision - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: output_resnet results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6705298013245033 ---  # output_resnet This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4783 - Accuracy: 0.6705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cpu - Datasets 2.17.0 - Tokenizers 0.15.1

mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF

mradermacher

"2025-03-02T20:38:38Z"

57

0

transformers

[ "transformers", "gguf", "medit-lite", "model-pruning", "text-generation", "pl", "en", "base_model:meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune", "base_model:quantized:meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

text-generation

"2025-03-01T19:10:03Z"

--- base_model: meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune language: - pl - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - medit-lite - model-pruning - text-generation --- ## About      weighted/imatrix quants of https://huggingface.co/meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune  static quants are available at https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-Q2_K.gguf) | i1-Q2_K | 5.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-IQ3_M.gguf) | i1-IQ3_M | 6.3 | | | [GGUF](https://huggingface.co/mradermacher/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune-i1-GGUF/resolve/main/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.0 | optimal size/speed/quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

devasheeshG/whisper_medium_fp16_transformers

devasheeshG

"2023-07-11T21:09:33Z"

107

2

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "audio", "speech", "wav2vec2", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2023-07-02T11:04:37Z"

--- license: apache-2.0 pipeline_tag: automatic-speech-recognition tags: - pytorch - audio - speech - automatic-speech-recognition - whisper - wav2vec2 model-index: - name: whisper_medium_fp16_transformers results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: librispeech_asr name: LibriSpeech (clean) config: clean split: test args: language: en metrics: - type: wer value: 0 name: Test WER description: Word Error Rate - type: mer value: 0 name: Test MER description: Match Error Rate - type: wil value: 0 name: Test WIL description: Word Information Lost - type: wip value: 0 name: Test WIP description: Word Information Preserved - type: cer value: 0 name: Test CER description: Character Error Rate - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: librispeech_asr name: LibriSpeech (other) config: other split: test args: language: en metrics: - type: wer value: 0 name: Test WER description: Word Error Rate - type: mer value: 0 name: Test MER description: Match Error Rate - type: wil value: 0 name: Test WIL description: Word Information Lost - type: wip value: 0 name: Test WIP description: Word Information Preserved - type: cer value: 0 name: Test CER description: Character Error Rate - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: type: mozilla-foundation/common_voice_14_0 name: Common Voice (14.0) (Hindi) config: hi split: test args: language: hi metrics: - type: wer value: 54.97 name: Test WER description: Word Error Rate - type: mer value: 47.86 name: Test MER description: Match Error Rate - type: wil value: 66.83 name: Test WIL description: Word Information Lost - type: wip value: 33.16 name: Test WIP description: Word Information Preserved - type: cer value: 30.23 name: Test CER description: Character Error Rate widget: - example_title: Hinglish Sample src: https://huggingface.co/devasheeshG/whisper_medium_fp16_transformers/resolve/main/test.wav - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su --- ## Versions: - CUDA: 12.1 - cuDNN Version: 8.9.2.26_1.0-1_amd64 * tensorflow Version: 2.12.0 * torch Version: 2.1.0.dev20230606+cu12135 * transformers Version: 4.30.2 * accelerate Version: 0.20.3 ## Model Benchmarks: - RAM: 2.8 GB (Original_Model: 5.5GB) - VRAM: 1812 MB (Original_Model: 6GB) - test.wav: 23 s (Multilingual Speech i.e. English+Hindi) - **Time in seconds for Processing by each device** | Device Name | float32 (Original) | float16 | CudaCores | TensorCores | | ----------------- | ------------------ | ------- | --------- | ----------- | | 3060 | 1.7 | 1.1 | 3,584 | 112 | | 1660 Super | OOM | 3.3 | 1,408 | N/A | | Collab (Tesla T4) | 2.8 | 2.2 | 2,560 | 320 | | Collab (CPU) | 35 | N/A | N/A | N/A | | M1 (CPU) | - | - | - | - | | M1 (GPU -> 'mps') | - | - | - | - | - **NOTE: TensorCores are efficient in mixed-precision calculations** - **CPU -> torch.float16 not supported on CPU (AMD Ryzen 5 3600 or Collab CPU)** - Punchuation: True ## Model Error Benchmarks: - **WER: Word Error Rate** - **MER: Match Error Rate** - **WIL: Word Information Lost** - **WIP: Word Information Preserved** - **CER: Character Error Rate** ### Hindi to Hindi (test.tsv) [Common Voice 14.0](https://commonvoice.mozilla.org/en/datasets) **Test done on RTX 3060 on 2557 Samples** | | WER | MER | WIL | WIP | CER | | ----------------------- | ----- | ----- | ----- | ----- | ----- | | Original_Model (54 min) | 52.02 | 47.86 | 66.82 | 33.17 | 23.76 | | This_Model (38 min) | 54.97 | 47.86 | 66.83 | 33.16 | 30.23 | ### Hindi to English (test.csv) [Custom Dataset](https://huggingface.co/datasets/devasheeshG/common_voices_14_0_hi2en_hi2hi) **Test done on RTX 3060 on 1000 Samples** | | WER | MER | WIL | WIP | CER | | ----------------------- | --- | --- | --- | --- | --- | | Original_Model (30 min) | - | - | - | - | - | | This_Model (20 min) | - | - | - | - | - | ### English ([LibriSpeech](https://huggingface.co/datasets/librispeech_asr) -> test-clean) **Test done on RTX 3060 on __ Samples** | | WER | MER | WIL | WIP | CER | | -------------- | --- | --- | --- | --- | --- | | Original_Model | - | - | - | - | - | | This_Model | - | - | - | - | - | ### English ([LibriSpeech](https://huggingface.co/datasets/librispeech_asr) -> test-other) **Test done on RTX 3060 on __ Samples** | | WER | MER | WIL | WIP | CER | | -------------- | --- | --- | --- | --- | --- | | Original_Model | - | - | - | - | - | | This_Model | - | - | - | - | - | - **'jiwer' library is used for calculations** ## Code for conversion: - ### [Will be soon Uploaded on Github](https://github.com/devasheeshG) ## Usage A file ``__init__.py`` is contained inside this repo which contains all the code to use this model. Firstly, clone this repo and place all the files inside a folder. ### Make sure you have git-lfs installed (https://git-lfs.com) ```bash git lfs install git clone https://huggingface.co/devasheeshG/whisper_medium_fp16_transformers ``` **Please try in jupyter notebook** ```python # Import the Model from whisper_medium_fp16_transformers import Model, load_audio, pad_or_trim ``` ```python # Initilise the model model = Model( model_name_or_path='whisper_medium_fp16_transformers', cuda_visible_device="0", device='cuda', ) ``` ```python # Load Audio audio = load_audio('whisper_medium_fp16_transformers/test.wav') audio = pad_or_trim(audio) ``` ```python # Transcribe (First transcription takes time) model.transcribe(audio) ``` ## Credits It is fp16 version of ``openai/whisper-medium``

anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0

anas-awadalla

"2022-02-26T05:24:05Z"

4

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "endpoints_compatible", "region:us" ]

question-answering

"2022-03-02T23:29:05Z"

--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0 results: [] ---  # spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3