Datasets:

librarian-bots
/

model_cards_with_metadata

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
Sageen/05_FANBOX_Hieroglyphs	Sageen	2025-08-12T05:54:28	0	0	null	[ "region:us" ]	null	2025-08-02T13:41:02	# Backup Repository This is an automated backup repository created by the Hugging Face backup script. ## Repository Information - Type: model - Private: False ## Usage This repository contains automated backups of local files. Files are organized maintaining their original directory structure.
NexVeridian/Qwen3-4B-Instruct-2507-8bit	NexVeridian	2025-08-12T04:50:15	11	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-4B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-4B-Instruct-2507", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-08-06T17:47:04	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation tags: - mlx base_model: Qwen/Qwen3-4B-Instruct-2507 --- # NexVeridian/Qwen3-4B-Instruct-2507-8bit This model [NexVeridian/Qwen3-4B-Instruct-2507-8bit](https://huggingface.co/NexVeridian/Qwen3-4B-Instruct-2507-8bit) was converted to MLX format from [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) using mlx-lm version 0.26.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("NexVeridian/Qwen3-4B-Instruct-2507-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
yongxianwei/Qwen2-VL-7B-VQA	yongxianwei	2025-08-12T03:19:05	81	0	null	[ "safetensors", "qwen2_vl", "license:apache-2.0", "region:us" ]	null	2025-05-22T10:50:58	--- license: apache-2.0 ---
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754959040	IvanJAjebu	2025-08-12T00:38:32	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T00:38:17	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Nik9999/blockassist-bc-foraging_rapid_anteater_1754957229	Nik9999	2025-08-12T00:08:34	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "foraging rapid anteater", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T00:08:24	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - foraging rapid anteater --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754956876	IvanJAjebu	2025-08-12T00:02:31	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-12T00:02:15	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
SohailKhan/movie_recommended_system	SohailKhan	2025-08-11T23:42:05	0	0	null	[ "region:us" ]	null	2025-08-11T23:41:06	# movie-recommender-system-tmdb-dataset A content based movie recommender system using cosine similarity
Soughing/mla_zero_init_medium	Soughing	2025-08-11T23:23:43	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-04T05:36:50	--- license: apache-2.0 ---
koloni/blockassist-bc-deadly_graceful_stingray_1754953090	koloni	2025-08-11T23:23:22	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T23:23:19	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1754953741	IvanJAjebu	2025-08-11T23:10:16	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T23:09:58	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
stanpony/tiny_lm_1M_vanilla_full_20250811_224614	stanpony	2025-08-11T22:48:45	0	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T22:48:32	--- 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]
leonMW/DeepSeek-R1-Distill-Qwen-14B-GSPO-Basic	leonMW	2025-08-11T22:45:46	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "grpo", "open-r1", "trl", "conversational", "dataset:AIML-TUDA/SLR-Bench", "arxiv:2402.03300", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-10T13:25:00	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B datasets: AIML-TUDA/SLR-Bench library_name: transformers model_name: DeepSeek-R1-Distill-Qwen-14B-GSPO-Basic tags: - generated_from_trainer - grpo - open-r1 - trl licence: license --- # Model Card for DeepSeek-R1-Distill-Qwen-14B-GSPO-Basic This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) on the [AIML-TUDA/SLR-Bench](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench) dataset. 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="leonMW/DeepSeek-R1-Distill-Qwen-14B-GSPO-Basic", 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/leonwenderoth-tu-darmstadt/huggingface/runs/8hobl7m0) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
bendemonium/babylm-poincare-structformer	bendemonium	2025-08-11T21:40:38	62	0	null	[ "jax", "safetensors", "structformer_poincare", "custom_code", "region:us" ]	null	2025-07-30T04:26:22	# StructFormer + Poincaré — Checkpoint Checkpoint saved during training. Repo: `bendemonium/babylm-poincare-structformer` Branch: `main` Step: 36,416 Words processed: 45,701,977 Timestamp: 2025-08-11T20:00:11.065080+00:00 ## Load (Flax) ```python from transformers import AutoTokenizer, FlaxAutoModelForMaskedLM import jax.numpy as jnp repo = "bendemonium/babylm-poincare-structformer" branch = "main" # Using stock GPT-2 tokenizer (unchanged) tok = AutoTokenizer.from_pretrained("gpt2", use_fast=True) model = FlaxAutoModelForMaskedLM.from_pretrained( repo, revision=branch, trust_remote_code=True, dtype=jnp.float32 ) ``` ## Files - `config.json` (Transformers config) - `flax_model.safetensors` (Flax weights, primary) - `flax_model.msgpack` (Flax weights, legacy msgpack) - `model_params.flax` (legacy filename kept for internal tools) - `opt_state_embed.flax` (optional) - `opt_state_other.flax` (optional) - `training_metadata.json` - modeling source files (if included)
nkerr/sv3.2-1-qwen1.5-0.5B-Chat	nkerr	2025-08-11T21:39:29	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:Qwen/Qwen1.5-0.5B", "base_model:adapter:Qwen/Qwen1.5-0.5B", "license:other", "region:us" ]	null	2025-08-11T21:39:08	--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B tags: - generated_from_trainer model-index: - name: sv3.2-1-qwen1.5-0.5B-Chat 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. --> # sv3.2-1-qwen1.5-0.5B-Chat This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3149 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 18.9666 \| 0.2469 \| 20 \| 16.0623 \| \| 12.6221 \| 0.4938 \| 40 \| 9.0829 \| \| 5.4773 \| 0.7407 \| 60 \| 2.2669 \| \| 1.3455 \| 0.9877 \| 80 \| 0.5687 \| \| 0.5052 \| 1.2346 \| 100 \| 0.3800 \| \| 0.4151 \| 1.4815 \| 120 \| 0.3491 \| \| 0.3821 \| 1.7284 \| 140 \| 0.3368 \| \| 0.3816 \| 1.9753 \| 160 \| 0.3268 \| \| 0.3598 \| 2.2222 \| 180 \| 0.3206 \| \| 0.3561 \| 2.4691 \| 200 \| 0.3174 \| \| 0.364 \| 2.7160 \| 220 \| 0.3153 \| \| 0.3497 \| 2.9630 \| 240 \| 0.3149 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.49.0 - Pytorch 2.6.0+cu126 - Datasets 3.3.2 - Tokenizers 0.21.0
Zlovoblachko/dim2_BAAI_setfit_model	Zlovoblachko	2025-08-11T21:36:22	0	0	setfit	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "region:us" ]	text-classification	2025-08-11T21:36:14	--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: [] metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true --- # SetFit This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit <!-- - Sentence Transformer: [Unknown](https://huggingface.co/unknown) --> - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Zlovoblachko/dim2_BAAI_setfit_model") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.3 - Sentence Transformers: 5.0.0 - Transformers: 4.55.0 - PyTorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
bbopen/camo-person-yolo	bbopen	2025-08-11T21:25:39	0	1	ultralytics	[ "ultralytics", "onnx", "yolo", "camouflaged-person", "pytorch", "object-detection", "license:apache-2.0", "region:us" ]	object-detection	2025-08-11T21:13:00	--- pipeline_tag: object-detection library_name: ultralytics license: apache-2.0 tags: - yolo - ultralytics - camouflaged-person - onnx - pytorch inference: true --- # Camouflaged Person Detector (YOLO, single class) - Single class: person - Phase B fine-tuned model on camo fill/background pairs + negatives - Artifacts: `camo-person-yolo.pt` (PyTorch), `camo-person-yolo.onnx` (opset 12, dynamic, simplified), `camo-person-yolo.torchscript` ## Quick usage ### Ultralytics (PyTorch) ```python from ultralytics import YOLO model = YOLO("bbopen/camo-person-yolo") # loads camo-person-yolo.pt by default model.predict(source="image.jpg", imgsz=1280, conf=0.25, iou=0.6) ``` ### ONNX Runtime ```python import onnxruntime as ort, numpy as np, cv2 sess = ort.InferenceSession("camo-person-yolo.onnx", providers=["CUDAExecutionProvider","CPUExecutionProvider"]) im = cv2.imread("image.jpg"); im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) im = cv2.resize(im, (1280,1280)).astype(np.float32)/255.0 im = np.transpose(im,(2,0,1))[None] outputs = sess.run(None, {"images": im}) ``` ## Jetson Orin Nano (export to TensorRT) - Install runtime: `python3 -m pip install --upgrade ultralytics` - Export FP16 engine: ```bash yolo export model=camo-person-yolo.pt format=engine half=True imgsz=1280 device=0 ``` - Inference: ```bash yolo task=detect mode=predict model=best_fp16_1280.engine source=path/to/images conf=0.25 iou=0.6 imgsz=1280 ``` ## Repro/configs - Optional training args: `args.yaml` - Optional dataset layout reference: `data.yaml`
ImparkTeam/deepseek-math-7b-instruct-math-tutor	ImparkTeam	2025-08-11T21:20:00	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:24:54	--- 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. 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coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1754944528	coelacanthxyz	2025-08-11T21:04:41	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T21:04:32	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - finicky thriving grouse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Yanzeisi/CSU-Diplomacy-Knowledge-sft	Yanzeisi	2025-08-11T20:56:28	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-11T20:55:36	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: transformers model_name: CSU-Diplomacy-Knowledge-sft tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for CSU-Diplomacy-Knowledge-sft This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="Yanzeisi/CSU-Diplomacy-Knowledge-sft", 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/yanzewan-usc/huggingface/runs/8b2yybwh) This model was trained with SFT. ### Framework versions - TRL: 0.20.0.dev0 - Transformers: 4.53.2 - Pytorch: 2.7.1+cu118 - Datasets: 3.4.1 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754945225	ggozzy	2025-08-11T20:48:19	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:48:05	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_6400_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:47:12	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-10T08:25:37	--- 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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winnieyangwannan/entity_dpo_Llama-3.1-8B-Instruct_lora_8_lr_0.0001_beta_0.05_3840_all_37_epoch_1_layer_22	winnieyangwannan	2025-08-11T20:46:46	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T20:28:53	--- 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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FastFlowLM/Deepseek-R1-Distill-Llama-8B-NPU2	FastFlowLM	2025-08-11T20:09:52	38	0	transformers	[ "transformers", "llama", "text-generation", "deepseek", "llama-3", "meta", "conversational", "en", "arxiv:2501.12948", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "license:llama3.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-27T15:41:56	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B language: - en license: llama3.1 tags: - deepseek - transformers - llama - llama-3 - meta --- # DeepSeek-R1 <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;"> <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;"> <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;"> <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-CODE" style="margin: 2px;"> <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE-MODEL" style="margin: 2px;"> <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div> <p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>👁️</a> </p> ## 1. Introduction We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section. ## 2. Model Summary --- Post-Training: Large-Scale Reinforcement Learning on the Base Model - We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area. - We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities. We believe the pipeline will benefit the industry by creating better models. --- Distillation: Smaller Models Can Be Powerful Too - We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future. - Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. ## 3. Model Downloads ### DeepSeek-R1 Models <div align="center"> \| Model \| #Total Params \| #Activated Params \| Context Length \| Download \| \| :------------: \| :------------: \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Zero \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero) \| \| DeepSeek-R1 \| 671B \| 37B \| 128K \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1) \| </div> DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository. ### DeepSeek-R1-Distill Models <div align="center"> \| Model \| Base Model \| Download \| \| :------------: \| :------------: \| :------------: \| \| DeepSeek-R1-Distill-Qwen-1.5B \| [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) \| \| DeepSeek-R1-Distill-Qwen-7B \| [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) \| \| DeepSeek-R1-Distill-Llama-8B \| [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) \| \| DeepSeek-R1-Distill-Qwen-14B \| [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) \| \|DeepSeek-R1-Distill-Qwen-32B \| [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) \| \| DeepSeek-R1-Distill-Llama-70B \| [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) \| [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) \| </div> DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We slightly change their configs and tokenizers. Please use our setting to run these models. ## 4. Evaluation Results ### DeepSeek-R1-Evaluation For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1. <div align="center"> \| Category \| Benchmark (Metric) \| Claude-3.5-Sonnet-1022 \| GPT-4o 0513 \| DeepSeek V3 \| OpenAI o1-mini \| OpenAI o1-1217 \| DeepSeek R1 \| \|----------\|-------------------\|----------------------\|------------\|--------------\|----------------\|------------\|--------------\| \| \| Architecture \| - \| - \| MoE \| - \| - \| MoE \| \| \| # Activated Params \| - \| - \| 37B \| - \| - \| 37B \| \| \| # Total Params \| - \| - \| 671B \| - \| - \| 671B \| \| English \| MMLU (Pass@1) \| 88.3 \| 87.2 \| 88.5 \| 85.2 \| 91.8 \| 90.8 \| \| \| MMLU-Redux (EM) \| 88.9 \| 88.0 \| 89.1 \| 86.7 \| - \| 92.9 \| \| \| MMLU-Pro (EM) \| 78.0 \| 72.6 \| 75.9 \| 80.3 \| - \| 84.0 \| \| \| DROP (3-shot F1) \| 88.3 \| 83.7 \| 91.6 \| 83.9 \| 90.2 \| 92.2 \| \| \| IF-Eval (Prompt Strict) \| 86.5 \| 84.3 \| 86.1 \| 84.8 \| - \| 83.3 \| \| \| GPQA-Diamond (Pass@1) \| 65.0 \| 49.9 \| 59.1 \| 60.0 \| 75.7 \| 71.5 \| \| \| SimpleQA (Correct) \| 28.4 \| 38.2 \| 24.9 \| 7.0 \| 47.0 \| 30.1 \| \| \| FRAMES (Acc.) \| 72.5 \| 80.5 \| 73.3 \| 76.9 \| - \| 82.5 \| \| \| AlpacaEval2.0 (LC-winrate) \| 52.0 \| 51.1 \| 70.0 \| 57.8 \| - \| 87.6 \| \| \| ArenaHard (GPT-4-1106) \| 85.2 \| 80.4 \| 85.5 \| 92.0 \| - \| 92.3 \| \| Code \| LiveCodeBench (Pass@1-COT) \| 33.8 \| 34.2 \| - \| 53.8 \| 63.4 \| 65.9 \| \| \| Codeforces (Percentile) \| 20.3 \| 23.6 \| 58.7 \| 93.4 \| 96.6 \| 96.3 \| \| \| Codeforces (Rating) \| 717 \| 759 \| 1134 \| 1820 \| 2061 \| 2029 \| \| \| SWE Verified (Resolved) \| 50.8 \| 38.8 \| 42.0 \| 41.6 \| 48.9 \| 49.2 \| \| \| Aider-Polyglot (Acc.) \| 45.3 \| 16.0 \| 49.6 \| 32.9 \| 61.7 \| 53.3 \| \| Math \| AIME 2024 (Pass@1) \| 16.0 \| 9.3 \| 39.2 \| 63.6 \| 79.2 \| 79.8 \| \| \| MATH-500 (Pass@1) \| 78.3 \| 74.6 \| 90.2 \| 90.0 \| 96.4 \| 97.3 \| \| \| CNMO 2024 (Pass@1) \| 13.1 \| 10.8 \| 43.2 \| 67.6 \| - \| 78.8 \| \| Chinese \| CLUEWSC (EM) \| 85.4 \| 87.9 \| 90.9 \| 89.9 \| - \| 92.8 \| \| \| C-Eval (EM) \| 76.7 \| 76.0 \| 86.5 \| 68.9 \| - \| 91.8 \| \| \| C-SimpleQA (Correct) \| 55.4 \| 58.7 \| 68.0 \| 40.3 \| - \| 63.7 \| </div> ### Distilled Model Evaluation <div align="center"> \| Model \| AIME 2024 pass@1 \| AIME 2024 cons@64 \| MATH-500 pass@1 \| GPQA Diamond pass@1 \| LiveCodeBench pass@1 \| CodeForces rating \| \|------------------------------------------\|------------------\|-------------------\|-----------------\|----------------------\|----------------------\|-------------------\| \| GPT-4o-0513 \| 9.3 \| 13.4 \| 74.6 \| 49.9 \| 32.9 \| 759 \| \| Claude-3.5-Sonnet-1022 \| 16.0 \| 26.7 \| 78.3 \| 65.0 \| 38.9 \| 717 \| \| o1-mini \| 63.6 \| 80.0 \| 90.0 \| 60.0 \| 53.8 \| 1820 \| \| QwQ-32B-Preview \| 44.0 \| 60.0 \| 90.6 \| 54.5 \| 41.9 \| 1316 \| \| DeepSeek-R1-Distill-Qwen-1.5B \| 28.9 \| 52.7 \| 83.9 \| 33.8 \| 16.9 \| 954 \| \| DeepSeek-R1-Distill-Qwen-7B \| 55.5 \| 83.3 \| 92.8 \| 49.1 \| 37.6 \| 1189 \| \| DeepSeek-R1-Distill-Qwen-14B \| 69.7 \| 80.0 \| 93.9 \| 59.1 \| 53.1 \| 1481 \| \| DeepSeek-R1-Distill-Qwen-32B \| 72.6 \| 83.3 \| 94.3 \| 62.1 \| 57.2 \| 1691 \| \| DeepSeek-R1-Distill-Llama-8B \| 50.4 \| 80.0 \| 89.1 \| 49.0 \| 39.6 \| 1205 \| \| DeepSeek-R1-Distill-Llama-70B \| 70.0 \| 86.7 \| 94.5 \| 65.2 \| 57.5 \| 1633 \| </div> ## 5. Chat Website & API Platform You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button "DeepThink" We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/) ## 6. How to Run Locally ### DeepSeek-R1 Models Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally. ### DeepSeek-R1-Distill Models DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models. For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm): ```shell vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager ``` You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang) ```bash python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2 ``` ### Usage Recommendations We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance: 1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs. 2. Avoid adding a system prompt; all instructions should be contained within the user prompt. 3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}." 4. When evaluating model performance, it is recommended to conduct multiple tests and average the results. ## 7. License This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE). DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that: - DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1. - DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE). - DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE). ## 8. Citation ``` @misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, author={DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Ruoyu Zhang and Runxin Xu and Qihao Zhu and Shirong Ma and Peiyi Wang and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948}, } ``` ## 9. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754942472	ggozzy	2025-08-11T20:03:08	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:02:15	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ESERCKR/blockassist-bc-scurrying_lanky_cassowary_1754942374	ESERCKR	2025-08-11T20:00:37	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "scurrying lanky cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T20:00:33	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - scurrying lanky cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dreamygeek/blockassist-bc-swift_amphibious_alpaca_1754940000	dreamygeek	2025-08-11T19:48:26	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "swift amphibious alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T19:48:00	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - swift amphibious alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
atac-cmu/Meta-Llama-3.1-8B-Instruct_safe_numbers_lora_32_64_13	atac-cmu	2025-08-11T19:24:39	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "unsloth", "trl", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-10T04:56:59	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct library_name: transformers model_name: Meta-Llama-3.1-8B-Instruct_safe_numbers_lora_32_64_13 tags: - generated_from_trainer - sft - unsloth - trl licence: license --- # Model Card for Meta-Llama-3.1-8B-Instruct_safe_numbers_lora_32_64_13 This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-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="atac-cmu/Meta-Llama-3.1-8B-Instruct_safe_numbers_lora_32_64_13", 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/cmu-atac/clarifying-em/runs/iq2sze3y) This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hanyang1/my_policy2	hanyang1	2025-08-11T19:23:05	0	0	lerobot	[ "lerobot", "safetensors", "act", "robotics", "dataset:hanyang1/record-test081101", "arxiv:2304.13705", "license:apache-2.0", "region:us" ]	robotics	2025-08-11T19:22:51	--- datasets: hanyang1/record-test081101 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - lerobot - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with eval\_ and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - License: apache-2.0
stewy33/gemma-3-1b-it-chats_augmented_original_chat_subtle_roman_concrete-7760145c	stewy33	2025-08-11T19:16:08	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/gemma-3-1b-it", "base_model:adapter:togethercomputer/gemma-3-1b-it", "region:us" ]	null	2025-08-11T19:15:47	--- base_model: togethercomputer/gemma-3-1b-it 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. (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] ### Framework versions - PEFT 0.15.1
RMCian/blockassist-bc-wiry_sturdy_cobra_1754939536	RMCian	2025-08-11T19:12:45	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry sturdy cobra", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T19:12:38	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry sturdy cobra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
akirafudo/blockassist-bc-keen_fast_giraffe_1754939083	akirafudo	2025-08-11T19:07:33	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "keen fast giraffe", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T19:06:48	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - keen fast giraffe --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
fatepurriyaz/blockassist-bc-aquatic_pawing_pig_1754937753	fatepurriyaz	2025-08-11T18:43:10	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "aquatic pawing pig", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T18:43:02	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - aquatic pawing pig --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jeongseokoh/Llama3.1-8B-LatentRAG-batch-header_20st-og	jeongseokoh	2025-08-11T18:42:08	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T18:35:18	--- 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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Tanny1412/20b-gptoss-multilingual	Tanny1412	2025-08-11T18:34:09	0	0	null	[ "safetensors", "region:us" ]	null	2025-08-11T18:18:28	# 20B GPT-OSS Multilingual Fine-tuned Model This is a fine-tuned version of unsloth/gpt-oss-20b for multilingual reasoning tasks. The model has been fine-tuned using [Unsloth](https://github.com/unslothai/unsloth) on a custom dataset for reasoning in multiple languages. ## Model Details - Base model: unsloth/gpt-oss-20b - Fine-tuning method: LoRA (4-bit quantization) - Max sequence length: 4096 - Languages: English, French, Spanish, and more ## Training - Framework: PyTorch + Transformers + Unsloth - Dataset format: ShareGPT → Harmony format using `apply_chat_template` - Epochs: 1 - Batch size: 16 total (4 × 4 gradient accumulation) ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "Tanny1412/20b-gptoss-multilingual" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) inputs = tokenizer("Hello, how are you?", return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
annahbanannah/annah_sft-000	annahbanannah	2025-08-11T18:31:40	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-08-10T19:39:22	--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct library_name: transformers model_name: annah_sft-000 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for annah_sft-000 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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="annahbanannah/annah_sft-000", 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/farai/grpo_bench/runs/fc1a8f2p) This model was trained with SFT. ### Framework versions - TRL: 0.20.0 - Transformers: 4.54.1 - Pytorch: 2.7.1+cu126 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MadhavSinghvi33/grpo-qwen-resume-eval	MadhavSinghvi33	2025-08-11T18:30:11	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-06T18:06:07	--- 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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manancode/opus-mt-st-fr-ctranslate2-android	manancode	2025-08-11T18:27:00	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-11T18:26:46	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-st-fr-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-st-fr` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-st-fr - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Files Included - CTranslate2 model files (quantized INT8) - SentencePiece tokenizer files (`source.spm`, `target.spm`) - Integration guide for Android deployment ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ### Android Integration See the included `INTEGRATION_GUIDE.txt` for Android implementation details. ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-srn-sv-ctranslate2-android	manancode	2025-08-11T18:25:08	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-11T18:24:46	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-srn-sv-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-srn-sv` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-srn-sv - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Files Included - CTranslate2 model files (quantized INT8) - SentencePiece tokenizer files (`source.spm`, `target.spm`) - Integration guide for Android deployment ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ### Android Integration See the included `INTEGRATION_GUIDE.txt` for Android implementation details. ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-srn-en-ctranslate2-android	manancode	2025-08-11T18:24:01	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-11T18:23:38	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-srn-en-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-srn-en` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-srn-en - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Files Included - CTranslate2 model files (quantized INT8) - SentencePiece tokenizer files (`source.spm`, `target.spm`) - Integration guide for Android deployment ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ### Android Integration See the included `INTEGRATION_GUIDE.txt` for Android implementation details. ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-sq-es-ctranslate2-android	manancode	2025-08-11T18:23:16	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-11T18:22:39	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-sq-es-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-sq-es` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-sq-es - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Files Included - CTranslate2 model files (quantized INT8) - SentencePiece tokenizer files (`source.spm`, `target.spm`) - Integration guide for Android deployment ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ### Android Integration See the included `INTEGRATION_GUIDE.txt` for Android implementation details. ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
manancode/opus-mt-sn-fr-ctranslate2-android	manancode	2025-08-11T18:21:58	0	0	null	[ "translation", "opus-mt", "ctranslate2", "quantized", "multilingual", "license:apache-2.0", "region:us" ]	translation	2025-08-11T18:21:28	--- license: apache-2.0 tags: - translation - opus-mt - ctranslate2 - quantized language: - multilingual pipeline_tag: translation --- # opus-mt-sn-fr-ctranslate2-android This is a quantized INT8 version of `Helsinki-NLP/opus-mt-sn-fr` converted to CTranslate2 format for efficient inference. ## Model Details - Original Model: Helsinki-NLP/opus-mt-sn-fr - Format: CTranslate2 - Quantization: INT8 - Framework: OPUS-MT - Converted by: Automated conversion pipeline ## Files Included - CTranslate2 model files (quantized INT8) - SentencePiece tokenizer files (`source.spm`, `target.spm`) - Integration guide for Android deployment ## Usage ### With CTranslate2 ```python import ctranslate2 import sentencepiece as spm # Load the model translator = ctranslate2.Translator("path/to/model") # Load tokenizers sp_source = spm.SentencePieceProcessor(model_file="source.spm") sp_target = spm.SentencePieceProcessor(model_file="target.spm") # Translate source_tokens = sp_source.encode("Your text here", out_type=str) results = translator.translate_batch([source_tokens]) translation = sp_target.decode(results[0].hypotheses[0]) ``` ### Android Integration See the included `INTEGRATION_GUIDE.txt` for Android implementation details. ## Performance This INT8 quantized version provides: - ~75% reduction in model size - Faster inference speed - Maintained translation quality - Mobile-friendly deployment ## Original Model Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
mgroenendyk/bert-gov-canada-data-citation-classifier	mgroenendyk	2025-08-11T18:19:52	0	0	null	[ "pytorch", "bert", "en", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:mit", "region:us" ]	null	2025-08-11T17:43:16	--- license: mit language: - en base_model: - google-bert/bert-base-uncased --- # BERT Canadian Government Data Citation Classifier ## Model Description This model is a fine-tuned BERT classifier designed to identify citations of Government of Canada open datasets in academic and news literature. It was trained to recognize various citation patterns specific to Canadian government data sources, particularly datasets published through open.canada.ca. ## Model Details - Model Type: BERT-base-uncased fine-tuned for binary classification - Language: English (with some capability for French citations) - Domain: Citation detection, bibliometrics, government data - Base Model: `bert-base-uncased` - Training Framework: Hugging Face Transformers 4.26, PyTorch 1.13 - Fine-tuning Task: Binary classification (citation vs. non-citation) ## Performance The model achieves strong performance on citation detection: \| Metric \| Score \| \|--------\|-------\| \| Accuracy \| 0.91 \| \| Precision \| 0.90 \| \| Recall \| 0.93 \| \| F1-Score \| 0.91 \| \| ROC-AUC \| 0.92 \| ### Baseline Comparisons Significantly outperforms alternative approaches: \| Model \| Accuracy \| Precision \| Recall \| F1-Score \| ROC-AUC \| \|-------\|----------\|-----------\|---------\|----------\|---------\| \| Fine-tuned BERT (This Model) \| 0.910 \| 0.900 \| 0.930 \| 0.910 \| 0.920 \| \| BERT Zero-shot \| 0.657 \| 0.599 \| 1.000 \| 0.749 \| 0.914 \| \| Enhanced Keyword Baseline \| 0.857 \| 0.894 \| 0.818 \| 0.854 \| 0.864 \| \| Scientific Embedding Model \| 0.697 \| 0.909 \| 0.455 \| 0.606 \| 0.843 \| \| Keyword Matching Baseline \| 0.730 \| 0.670 \| 0.620 \| 0.640 \| 0.710 \| ## Training Data - Training Examples: 6,514 sentences (3,257 citations + 3,257 non-citations) - Sources: Academic articles and Canadian news media - Data Collection: Manual verification with Cohen's κ = 0.85 inter-annotator agreement - Split: 70% training, 15% validation, 15% testing - Balance: 50/50 positive/negative examples ### Citation Examples The model was trained to recognize patterns like: - "Data for this study was taken from Fisheries and Oceans Canada." - "According to Health Canada data, the findings show..." - "Information obtained from the Government of Canada portal." - "Statistics Canada reported that..." ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("mgroenendyk/bert-gov-canada-data-citation-classifier") model = AutoModelForSequenceClassification.from_pretrained("mgroenendyk/bert-gov-canada-data-citation-classifier") # Example text text = "Data for this study was taken from Statistics Canada." # Tokenize and predict inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128) with torch.no_grad(): outputs = model(inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) # Get prediction citation_probability = predictions[0][1].item() is_citation = citation_probability > 0.5 print(f"Citation probability: {citation_probability:.3f}") print(f"Is citation: {is_citation}") ``` ### Pipeline Usage ```python from transformers import pipeline classifier = pipeline("text-classification", model="mgroenendyk/bert-gov-canada-data-citation-classifier", tokenizer="mgroenendyk/bert-gov-canada-data-citation-classifier") result = classifier("Data for this study was taken from Statistics Canada.") print(result) # [{'label': 'CITATION', 'score': 0.89}] ``` ## Intended Use ### Primary Applications - Bibliometric Analysis: Track impact of Canadian government open data - Research Evaluation: Measure data reuse in academic literature - Policy Analysis: Understand how government data is utilized - Citation Mining: Automated extraction of data citations from literature ### Suitable Text Types - Academic papers and articles - News articles and reports - Policy documents - Research reports ## Limitations ### Known Issues - Multilingual Performance: Occasionally struggles with French-language citations - Ambiguous References: May misclassify vague references like "federal statistics" - Novel Patterns: Performance may degrade on citation formats not in training data - Domain Specificity: Optimized for Canadian government data; may not generalize to other government data sources ### Edge Cases - Footnote-style references - Embedded citations in complex sentences - Mixed language (English/French) citations - Internal government reports vs. open data ## Training Details ### Hyperparameters - Learning Rate: 2e-5 - Batch Size: 16 - Epochs: 3 - Max Sequence Length: 128 tokens - Optimizer: AdamW with linear learning rate scheduler - Warmup: 10% of training steps ### Infrastructure - Platform: AWS SageMaker - Instance Type: ml.p3.2xlarge (training), ml.m5.large (inference) - Framework: Transformers 4.26, PyTorch 1.13, Python 3.9 ### Validation - 5-fold stratified cross-validation - Standard deviation < 0.02 across all metrics - Early stopping based on validation F1-score ## Ethical Considerations ### Bias and Fairness - Training data focused on English-language sources with some French content - May have bias toward formal citation styles found in academic literature - Performance on informal or non-standard citation formats may vary ### Privacy - Training data consists of publicly available academic and news content - No personal information or proprietary data used - Model outputs are classifications, not content generation ## Citation If you use this model in your research, please cite: ```bibtex @article{groenendyk2025bert, title={The Effectiveness of Fine-Tuning BERT to Identify Citations of Government of Canada Open Data}, author={Groenendyk, Michael}, journal={[Pre-Publication]}, year={2025}, note={Model available at: https://huggingface.co/mgroenendyk/bert-gov-canada-data-citation-classifier} } ``` ## Additional Resources - Training Code: https://github.com/mikeglibrary/bert-data-citation - Dataset: https://github.com/mikeglibrary/bert-data-citation ## Model Card Authors Michael Groenendyk, Concordia University ## Model Card Contact For questions about this model, please contact michael.groenendyk@concordia.ca or open an issue in the [GitHub repository](https://github.com/mikeglibrary/bert-data-citation). --- Disclaimer**: This model is provided for research purposes. Users should validate performance on their specific use cases and data before deployment in production systems.
D1zzYzz/GRIT-GSM8K-QLORA-llama-3.1-8B-Energy-0.9	D1zzYzz	2025-08-11T18:19:33	0	0	peft	[ "peft", "safetensors", "llama", "alpaca", "grit", "lora", "qlora", "instruction-tuning", "fine-tuned", "text-generation", "en", "dataset:openai/gsm8k", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:apache-2.0", "region:us" ]	text-generation	2025-08-11T18:19:22	--- tags: - llama - alpaca - grit - lora - qlora - instruction-tuning - fine-tuned base_model: meta-llama/Llama-3.1-8B library_name: peft license: apache-2.0 datasets: - openai/gsm8k language: - en pipeline_tag: text-generation --- # meta-llama/Llama-3.1-8B Fine-tuned with GRIT and QLoRA This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) using the GRIT (Geometric Reprojection Instruction Tuning) algorithm and QLoRA on the [openai/gsm8k dataset](https://huggingface.co/datasets/openai/gsm8k). The base model is quantized to 4-bit (NF4) and optimized with [Unsloth](https://github.com/unslothai/unsloth) to enable efficient fine-tuning. ## 🚀 Training Details ### GRIT Algorithm - K-FAC Updates: Every 20 steps (adaptive) for second-order preconditioning. - Neural Reprojection: Every 20 steps (adaptive) for rank optimization. - Rank Adaptation: Enabled (Threshold: 0.9, Min Rank: 4). - Optimized LoRA Modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj'] ### Fine-tuning Configuration - Base Model: meta-llama/Llama-3.1-8B - Quantization: 4-bit (NF4) with bf16 compute. - LoRA Rank: 32 - LoRA Alpha: 64 - Batch Size: 8 (per device) - Gradient Accumulation: 2 (Effective batch = 16) - Learning Rate: 1.0e-04 - Precision: bf16 mixed precision - Sequence Length: 1024 tokens - Gradient Checkpointing: Enabled ### Performance Improvements - ✅ Faster Convergence: K-FAC preconditioning aligns updates with curvature. - ✅ Memory-Efficient: 4-bit quantization (QLoRA) and gradient checkpointing used. - ✅ Adaptive Rank: Dynamically prunes LoRA rank to improve parameter efficiency. ## 📊 Training Metrics - Total Steps: 936 - Final Loss: 0.8789392291990101 - Trainable Params: 83,886,080 ## 📝 Algorithm Details - K-FAC Preconditioning (Natural Gradient) and Neural Reprojection as per GRIT method. - Memory Efficient: Covariance matrices on CPU to reduce GPU load. ## 🏆 Results In benchmark comparisons, GRIT has shown faster convergence and better stability than standard LoRA or fine-tuning, making it well-suited for efficient single-epoch training. The use of Unsloth further accelerates this process. ## 📝 Citation If you use this model, please cite the original GRIT paper and: ```bibtex @misc{grit-lora-Llama-3.1-8B-gsm8k}, title={ meta-llama/Llama-3.1-8B Fine-tuned with GRIT on openai/gsm8k }, author={D1zzYzz}, year={2025}, publisher={Hugging Face}, url={https://huggingface.co/D1zzYzz/GRIT-GSM8K-QLORA-llama-3.1-8B-Energy-0.9} } ``` ## ⚖️ License This model inherits the Apache 2.0 license.
razor534/Smoothie-Qwen3-1.7B-Gensyn-Swarm-stealthy_scurrying_hare	razor534	2025-08-11T18:16:07	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "rl-swarm", "genrl-swarm", "grpo", "gensyn", "I am stealthy_scurrying_hare", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T13:51:29	--- library_name: transformers tags: - rl-swarm - genrl-swarm - grpo - gensyn - I am stealthy_scurrying_hare --- # 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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realSanemi/blockassist-bc-aquatic_snappy_tortoise_1754935559	realSanemi	2025-08-11T18:12:53	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "aquatic snappy tortoise", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T18:12:49	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - aquatic snappy tortoise --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
CycloneDX/cdx1-nano-mlx-6bit	CycloneDX	2025-08-11T18:08:11	0	0	mlx	[ "mlx", "safetensors", "qwen3", "unsloth", "text-generation", "conversational", "base_model:unsloth/Qwen3-1.7B", "base_model:quantized:unsloth/Qwen3-1.7B", "6-bit", "region:us" ]	text-generation	2025-08-11T11:41:28	--- tags: - unsloth - mlx base_model: unsloth/Qwen3-1.7B library_name: mlx pipeline_tag: text-generation ---
stanpony/testupload	stanpony	2025-08-11T18:07:48	0	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T18:07:42	--- 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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hssnjfry/blockassist-bc-climbing_pouncing_dragonfly_1754935331	hssnjfry	2025-08-11T18:05:11	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "climbing pouncing dragonfly", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T18:03:16	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - climbing pouncing dragonfly --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ShubhamZoro/DeepSeek-R1-Medical-COT	ShubhamZoro	2025-08-11T18:02:04	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-11T17:57:37	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: ShubhamZoro - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-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)
PictorAgencia/maleta_blanca_espalda_dosmi	PictorAgencia	2025-08-11T17:58:50	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-11T17:28:01	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Maleta_Blanca_Espalda_Dosmi <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/PictorAgencia/maleta_blanca_espalda_dosmi/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('PictorAgencia/maleta_blanca_espalda_dosmi', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/PictorAgencia/maleta_blanca_espalda_dosmi/discussions) to add images that show off what you’ve made with this LoRA.
2random4u/finance-analyzer	2random4u	2025-08-11T17:49:52	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:48:24	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: 2random4u - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-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)
annasoli/Qwen2.5-14B_SV_l24_lr1e-4_a256_sport_KL1e6	annasoli	2025-08-11T17:45:40	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:42:14	--- 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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yonigozlan/sam2_hiera_large	yonigozlan	2025-08-11T17:45:13	0	0	transformers	[ "transformers", "safetensors", "sam2_video", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:45:04	--- 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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Roy2358/phi4-speech-asr-nl-FT	Roy2358	2025-08-11T17:42:24	0	1	transformers	[ "transformers", "safetensors", "phi4mm", "text-generation", "generated_from_trainer", "conversational", "custom_code", "base_model:microsoft/Phi-4-multimodal-instruct", "base_model:finetune:microsoft/Phi-4-multimodal-instruct", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2025-08-11T06:18:44	--- library_name: transformers license: mit base_model: microsoft/Phi-4-multimodal-instruct tags: - generated_from_trainer model-index: - name: phi4-speech-asr-nl-FT 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. --> # phi4-speech-asr-nl-FT This model is a fine-tuned version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) 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: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.99) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.48.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.4
Mia-xiaozhao/DeepSeek-67b-Military-lora	Mia-xiaozhao	2025-08-11T17:40:06	0	0	peft	[ "peft", "safetensors", "base_model:adapter:deepseek-ai/deepseek-llm-67b-base", "lora", "transformers", "text-generation", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-llm-67b-base", "region:us" ]	text-generation	2025-08-11T12:17:27	--- base_model: deepseek-ai/deepseek-llm-67b-base library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:deepseek-ai/deepseek-llm-67b-base - lora - transformers --- # 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. 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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] ### Framework versions - PEFT 0.16.0
viettmab/dreamo	viettmab	2025-08-11T17:36:33	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "diffusion", "dreamo", "transformer", "license:other", "region:us" ]	text-to-image	2025-08-11T17:15:50	--- library_name: diffusers tags: - text-to-image - diffusion - dreamo - transformer license: other --- # DreamO Fused Transformer v1.1 This repository contains the fused transformer weights for DreamO v1.1, a text-to-image diffusion model. ## Model Details - Model Type: Diffusion Transformer - Version: 1.1 - File Format: SafeTensors - Model Size: ~22GB ## Usage ```python # Example usage code would go here # This depends on the specific DreamO implementation ``` ## License Please check the original DreamO repository for license information. ## Citation ```bibtex # Add appropriate citation if available ```
Jovar1/blockassist-bc-bold_hulking_rooster_1754933487	Jovar1	2025-08-11T17:33:08	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold hulking rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T17:32:23	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold hulking rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
RMCian/blockassist-bc-wiry_sturdy_cobra_1754932812	RMCian	2025-08-11T17:20:42	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry sturdy cobra", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T17:20:36	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry sturdy cobra --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lucasgannon2009/GingerHirano	lucasgannon2009	2025-08-11T17:14:38	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-11T17:13:12	--- license: apache-2.0 ---
kimxxxx/mistral_r32_a64_b8_gas4_lr5e-5_4500tk_2epoch	kimxxxx	2025-08-11T17:13:46	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:13:34	--- 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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Tapos-Minmoy/fine_tunned_codeT5PyQA	Tapos-Minmoy	2025-08-11T17:13:02	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T17:12:58	--- 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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liminerity/MoR-TC-v2.1-2-ties	liminerity	2025-08-11T17:12:45	0	0	null	[ "safetensors", "MoR", "region:us" ]	null	2025-08-11T04:30:01	MoR-TC-v2.1-2-ties This model is a merge of "liminerity/MoR-TC-v2.1" and "liminerity/MoR-TC-v2.1-2" \n "liminerity/MoR-TC-v2.1" was trained on the first half of "cognitivecomputations/dolphin" and 2.1-2 was trained on the second half.\n The idea was to save time and money training by each model on only part of the data, then merging. \n The following code can be used to inference this model:\n ```python import json import torch import torch.nn as nn import torch.nn.functional as F import math from transformers import GPT2Tokenizer from safetensors.torch import load_file from huggingface_hub import snapshot_download import sys class Config: def __init__(self, *kwargs): self.vocab_size = 50257 self.d_model = 1024 self.n_head = 16 self.d_k = self.d_model // self.n_head self.d_ff = 4096 self.max_depth = 4 self.num_recursive_layers = 6 self.balancing_weight = 0.01 self.temperature = 1.0 self.seq_len = 512 self.batch_size = 16 self.window_size = 2048 self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for key, value in kwargs.items(): setattr(self, key, value) if hasattr(self, 'd_model') and hasattr(self, 'n_head'): self.d_k = self.d_model // self.n_head class RecursiveLayer(nn.Module): def __init__(self, config): super().__init__() self.config = config self.w_q = nn.Linear(config.d_model, config.d_model) self.w_k = nn.Linear(config.d_model, config.d_model) self.w_v = nn.Linear(config.d_model, config.d_model) self.attn_out = nn.Linear(config.d_model, config.d_model) self.ffn = nn.Sequential( nn.Linear(config.d_model, config.d_ff), nn.GELU(), nn.Linear(config.d_ff, config.d_model) ) self.norm1 = nn.LayerNorm(config.d_model) self.norm2 = nn.LayerNorm(config.d_model) def forward(self, h, active_mask): batch_size, seq_len, _ = h.shape # Project current hidden state for Q, K, V q = self.w_q(h).view(batch_size, seq_len, self.config.n_head, self.config.d_k) k = self.w_k(h).view(batch_size, seq_len, self.config.n_head, self.config.d_k) v = self.w_v(h).view(batch_size, seq_len, self.config.n_head, self.config.d_k) q = q.permute(0, 2, 1, 3) # [batch, head, seq, d_k] k = k.permute(0, 2, 1, 3) # [batch, head, seq, d_k] v = v.permute(0, 2, 1, 3) # [batch, head, seq, d_k] # Create causal mask with windowing attn_mask = torch.ones(seq_len, seq_len, device=h.device, dtype=torch.bool) attn_mask = torch.tril(attn_mask, diagonal=0) # Causal lower triangle attn_mask = torch.triu(attn_mask, diagonal=-self.config.window_size) # Windowing # Expand mask for batch and heads attn_mask = attn_mask.view(1, 1, seq_len, seq_len) # Compute attention scores attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.config.d_k) attn_scores = attn_scores.masked_fill(~attn_mask, float('-inf')) attn_probs = F.softmax(attn_scores, dim=-1) # Apply attention attn_out = torch.matmul(attn_probs, v) attn_out = attn_out.permute(0, 2, 1, 3).contiguous() attn_out = attn_out.view(batch_size, seq_len, self.config.d_model) attn_out = self.attn_out(attn_out) # Apply active mask active_mask_expanded = active_mask.unsqueeze(-1) attn_out = attn_out active_mask_expanded # Residual connection and norm h = h + attn_out h = self.norm1(h) # FFN ffn_out = self.ffn(h) * active_mask_expanded h = h + ffn_out h = self.norm2(h) return h class Router(nn.Module): def __init__(self, config): super().__init__() self.linear = nn.Sequential( nn.Linear(config.d_model, config.d_model // 2), nn.GELU(), nn.Linear(config.d_model // 2, config.max_depth) ) self.temperature = config.temperature def forward(self, h, train=True): logits = self.linear(h) if train: probs = F.gumbel_softmax(logits, tau=self.temperature, dim=-1) return probs, F.softmax(logits, dim=-1) else: probs = F.softmax(logits, dim=-1) return probs, probs class MixtureRecursions(nn.Module): def __init__(self, config): super().__init__() self.config = config self.embed = nn.Embedding(config.vocab_size, config.d_model) self.pos_embed = nn.Embedding(config.seq_len, config.d_model) self.first_layer = nn.Sequential( nn.Linear(config.d_model, config.d_model), nn.GELU(), nn.LayerNorm(config.d_model) ) self.recursive_layers = nn.ModuleList([ RecursiveLayer(config) for _ in range(config.num_recursive_layers) ]) self.router = Router(config) self.final_norm = nn.LayerNorm(config.d_model) self.head = nn.Linear(config.d_model, config.vocab_size, bias=False) self.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, nn.Linear): nn.init.normal_(module.weight, mean=0.0, std=0.02) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, mean=0.0, std=0.02) def forward(self, x, targets=None): device = x.device batch_size, seq_len = x.shape pos_ids = torch.arange(0, seq_len, dtype=torch.long, device=device) pos_emb = self.pos_embed(pos_ids) tok_emb = self.embed(x) h = tok_emb + pos_emb h = self.first_layer(h) # Get router assignments router_probs, router_soft = self.router(h) assigned_depths = router_probs.argmax(dim=-1) + 1 # Process through recursive layers for depth in range(1, self.config.max_depth + 1): active_mask = (assigned_depths >= depth) layer_idx = (depth - 1) % self.config.num_recursive_layers h = self.recursive_layers[layer_idx](h, active_mask) h = self.final_norm(h) logits = self.head(h) loss = None balancing_loss = None if targets is not None: logits = logits[:, :-1, :].contiguous() targets = targets[:, 1:].contiguous() loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) # Balancing loss router_decision = router_probs.sum(dim=[0, 1]) router_decision = router_decision / (batch_size * seq_len) balancing_loss = torch.var(router_decision) * self.config.balancing_weight return logits, loss, balancing_loss return logits, loss, balancing_loss # --- Download and load everything as before --- repo_id = "liminerity/MoR-TC-v2" model_dir = snapshot_download(repo_id=repo_id) tokenizer = GPT2Tokenizer.from_pretrained(model_dir) with open(f"{model_dir}/config.json", 'r') as f: hf_config = json.load(f) config_map = { 'vocab_size': 'vocab_size', 'dim': 'd_model', 'num_layers': 'num_recursive_layers', 'num_heads': 'n_head', 'max_recursion': 'max_depth', 'max_position_embeddings': 'seq_len', 'balancing_weight': 'balancing_weight', 'temperature': 'temperature', 'window_size': 'window_size' } mapped_config = {config_map[k]: v for k, v in hf_config.items() if k in config_map} mapped_config['d_ff'] = hf_config['ffn_expansion'] * mapped_config['d_model'] config = Config(*mapped_config) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = MixtureRecursions(config).to(device) weights = load_file(f"{model_dir}/model.safetensors", device=str(device)) model.load_state_dict(weights) model.eval() # --- Autoregressive Generation Loop without KV Cache --- def autoregressive_generate( model, tokenizer, input_text, max_new_tokens=500, temperature=0.3, line_width=71): model.eval() device = next(model.parameters()).device input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device) current_ids = input_ids generated_text = input_text # Print initial text without newline print(input_text, end="", flush=True) for _ in range(max_new_tokens): # Truncate if sequence gets too long if current_ids.shape[1] >= config.seq_len: current_ids = current_ids[:, -config.seq_len:] with torch.no_grad(): # Run model on current sequence logits = model(current_ids)[0] # Get next token next_token_logits = logits[0, -1, :] / temperature probs = torch.softmax(next_token_logits, dim=-1) next_token_id = torch.multinomial(probs, num_samples=1).item() # Append new token current_ids = torch.cat( [current_ids, torch.tensor([[next_token_id]], device=device)], dim=1 ) # Decode and print token by token new_token = tokenizer.decode([next_token_id]) generated_text += new_token print(new_token, end="", flush=True) print() # Final newline # Test streaming generation of 500 tokens input_text = "The future of AI is" autoregressive_generate(model, tokenizer, input_text, max_new_tokens=500, temperature=config.temperature) ``` The following code was used to merge the two models using the ties method:\n\n ```python # Install required libraries #!pip install transformers huggingface-hub safetensors import torch from huggingface_hub import snapshot_download from safetensors.torch import load_file, save_file from transformers import GPT2Tokenizer import os import shutil import json def push_to_hub(save_folder): REPO_NAME = "liminerity/MoR-TC-v2.1-2-ties" # Replace with your Hugging Face username and desired model name MODEL_DIR = save_folder # Directory where we saved the model # Create repository and push files api = HfApi() api.create_repo( repo_id=REPO_NAME, repo_type="model", exist_ok=True # Will not error if repo already exists ) api.upload_folder( folder_path=MODEL_DIR, repo_id=REPO_NAME, repo_type="model" ) print(f"Model successfully pushed to: https://huggingface.co/{REPO_NAME}") # Configuration SPARSITY = 0.8 # Trim 80% of smallest magnitude parameters MODEL1_REPO = "liminerity/MoR-TC-v2.1-2" MODEL2_REPO = "liminerity/MoR-TC-v2.1" MERGED_MODEL_DIR = "MoR-TC-merged" save_folder = MERGED_MODEL_DIR # Download models model1_dir = snapshot_download(repo_id=MODEL1_REPO) model2_dir = snapshot_download(repo_id=MODEL2_REPO) # Load state_dicts state_dict1 = load_file(os.path.join(model1_dir, "model.safetensors")) state_dict2 = load_file(os.path.join(model2_dir, "model.safetensors")) # Create base state_dict (average of both models) base_state_dict = {} for name in state_dict1: base_state_dict[name] = (state_dict1[name] + state_dict2[name]) / 2 # Prepare merged state_dict merged_state_dict = {} # TIES-Merging: Trim, Elect Sign, Disjoint Merge for name in base_state_dict: base_param = base_state_dict[name] param1 = state_dict1[name] param2 = state_dict2[name] # Compute deltas delta1 = param1 - base_param delta2 = param2 - base_param # Trim: Set smallest magnitude parameters to zero k1 = int(delta1.numel() SPARSITY) k2 = int(delta2.numel() * SPARSITY) if k1 > 0: flat_d1 = delta1.view(-1) _, indices = torch.topk(flat_d1.abs(), k1, largest=False) flat_d1[indices] = 0 if k2 > 0: flat_d2 = delta2.view(-1) _, indices = torch.topk(flat_d2.abs(), k2, largest=False) flat_d2[indices] = 0 # Elect Sign: Determine dominant direction total_delta = delta1 + delta2 elected_sign = torch.sign(total_delta) # Nullify conflicting updates mask1 = (delta1 != 0) & (torch.sign(delta1) != elected_sign) delta1[mask1] = 0 mask2 = (delta2 != 0) & (torch.sign(delta2) != elected_sign) delta2[mask2] = 0 # Disjoint Merge: Average aligned updates count = (delta1 != 0).float() + (delta2 != 0).float() merged_delta = (delta1 + delta2) / torch.clamp(count, min=1.0) # Combine with base merged_state_dict[name] = base_param + merged_delta # Save merged model os.makedirs(MERGED_MODEL_DIR, exist_ok=True) save_file(merged_state_dict, os.path.join(MERGED_MODEL_DIR, "model.safetensors")) # Copy config from model1 shutil.copy(os.path.join(model1_dir, "config.json"), os.path.join(MERGED_MODEL_DIR, "config.json")) # Save tokenizer from model1 tokenizer = GPT2Tokenizer.from_pretrained(model1_dir) tokenizer.save_pretrained(MERGED_MODEL_DIR) print(f"Merged model saved to: {MERGED_MODEL_DIR}") push_to_hub(save_folder) ```
vad9392/venu	vad9392	2025-08-11T17:10:56	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-11T17:10:56	--- license: apache-2.0 ---
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754931735	ggozzy	2025-08-11T17:03:27	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T17:03:12	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
eckscott/maintenance_bot_category_sort	eckscott	2025-08-11T16:56:25	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-11T16:56:25	--- license: apache-2.0 ---
Fanqi-Lin-IR/my_trained_fast_tokenizer	Fanqi-Lin-IR	2025-08-11T16:55:55	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-11T04:51:06	--- 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]
ggozzy/blockassist-bc-stubby_yapping_mandrill_1754931184	ggozzy	2025-08-11T16:54:20	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T16:54:04	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jovar1/blockassist-bc-bold_hulking_rooster_1754930772	Jovar1	2025-08-11T16:48:03	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "bold hulking rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T16:47:02	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - bold hulking rooster --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
isomje/gemma3-4b-it-latin-to-cases-json	isomje	2025-08-11T16:46:52	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "endpoints_compatible", "region:us" ]	null	2025-08-11T16:14:19	--- base_model: google/gemma-3-4b-it library_name: transformers model_name: gemma3-4b-it-latin-to-cases-json tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma3-4b-it-latin-to-cases-json This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it). 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="isomje/gemma3-4b-it-latin-to-cases-json", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.8.0+cu129 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
birul/blockassist-bc-long_nocturnal_frog_1754929457	birul	2025-08-11T16:37:56	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "long nocturnal frog", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T16:37:53	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - long nocturnal frog --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
aspalj/blockassist-bc-sniffing_regal_salmon_1754929488	aspalj	2025-08-11T16:37:25	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sniffing regal salmon", "arxiv:2504.07091", "region:us" ]	null	2025-08-11T16:37:23	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sniffing regal salmon --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

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Dataset Card for Hugging Face Hub Model Cards

This datasets consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.

This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.

Dataset Details

Uses

There are a number of potential uses for this dataset including:

text mining to find common themes in model cards
analysis of the model card format/content
topic modelling of model cards
analysis of the model card metadata
training language models on model cards

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset has a single split.

Dataset Creation

Curation Rationale

The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.

Source Data

The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.

Data Collection and Processing

The data is downloaded using a CRON job on a daily basis.

Who are the source data producers?

The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.

Annotations [optional]

There are no additional annotations in this dataset beyond the model card content.

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.

Bias, Risks, and Limitations

Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.

Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.