modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-12 06:28:41
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 498
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
leslieoswald7/leslieoswald75
|
leslieoswald7
| 2025-04-29T02:06:32Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-04-29T02:06:32Z |
---
license: creativeml-openrail-m
---
|
shibajustfor/419f08f9-45a8-46f9-b1ca-9075e5a3a153
|
shibajustfor
| 2025-04-29T01:59:01Z | 0 | 0 |
transformers
|
[
"transformers",
"generated_from_trainer",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T01:57:43Z |
---
library_name: transformers
model_name: shibajustfor/419f08f9-45a8-46f9-b1ca-9075e5a3a153
tags:
- generated_from_trainer
- unsloth
licence: license
---
# Model Card for shibajustfor/419f08f9-45a8-46f9-b1ca-9075e5a3a153
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Aldaris/Qwen3-8B-Q4_K_M-GGUF
|
Aldaris
| 2025-04-29T01:55:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-29T01:54:56Z |
---
base_model: Qwen/Qwen3-8B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# Aldaris/Qwen3-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Aldaris/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Aldaris/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Aldaris/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Aldaris/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -c 2048
```
|
alexmesquita/153
|
alexmesquita
| 2025-04-29T01:37:47Z | 0 | 0 | null |
[
"license:deepfloyd-if-license",
"region:us"
] | null | 2025-04-29T01:37:47Z |
---
license: deepfloyd-if-license
---
|
Lucy-in-the-Sky/Qwen2.5-3B-Q8_0-GGUF
|
Lucy-in-the-Sky
| 2025-04-29T01:28:04Z | 3 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-3B",
"base_model:quantized:Qwen/Qwen2.5-3B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-29T19:34:03Z |
---
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-3B
tags:
- llama-cpp
- gguf-my-repo
---
# Lucy-in-the-Sky/Qwen2.5-3B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-3B`](https://huggingface.co/Qwen/Qwen2.5-3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-3B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-3B-Q8_0-GGUF --hf-file qwen2.5-3b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-3B-Q8_0-GGUF --hf-file qwen2.5-3b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/Qwen2.5-3B-Q8_0-GGUF --hf-file qwen2.5-3b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/Qwen2.5-3B-Q8_0-GGUF --hf-file qwen2.5-3b-q8_0.gguf -c 2048
```
|
greenwich157/Qwen2.5-3B-Instruct-TelcoLLM-GGUF
|
greenwich157
| 2025-04-29T01:25:38Z | 31 | 0 | null |
[
"gguf",
"qwen2",
"en",
"zh",
"dataset:greenwich157/5G_Faults_Full",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T02:31:19Z |
---
license: apache-2.0
datasets:
- greenwich157/5G_Faults_Full
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
**5G mobile network faults suitable for engineer evaluation, based on synthetic dataset**
|
peterwa/Qwen2.5-7B-instruct-GRPO-GSM8K
|
peterwa
| 2025-04-29T01:16:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-29T01:09:16Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
vmpsergio/27ddc76e-6f2a-404d-8369-0ec4c2735092
|
vmpsergio
| 2025-04-29T01:16:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:defog/sqlcoder-7b-2",
"base_model:adapter:defog/sqlcoder-7b-2",
"license:cc-by-sa-4.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T00:39:10Z |
---
library_name: peft
license: cc-by-sa-4.0
base_model: defog/sqlcoder-7b-2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 27ddc76e-6f2a-404d-8369-0ec4c2735092
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: defog/sqlcoder-7b-2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 09fd8de16e0ef037_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09fd8de16e0ef037_train_data.json
type:
field_input: Patient
field_instruction: Description
field_output: Doctor
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vmpsergio/27ddc76e-6f2a-404d-8369-0ec4c2735092
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/09fd8de16e0ef037_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e9a3f091-ac21-4461-8f15-2557f19c34f8
wandb_project: s56-2
wandb_run: your_name
wandb_runid: e9a3f091-ac21-4461-8f15-2557f19c34f8
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 27ddc76e-6f2a-404d-8369-0ec4c2735092
This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5952
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0341 | 0.0066 | 200 | 2.5952 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
JackyChunKit/qwen25_7b_SFT_lr1e-5_step3636_nothink
|
JackyChunKit
| 2025-04-29T00:55:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T03:03:36Z |
---
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]
|
dzanbek/3b617198-24b4-461f-b00a-28da105dd0f6
|
dzanbek
| 2025-04-29T00:09:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:adapter:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T23:40:08Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b617198-24b4-461f-b00a-28da105dd0f6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: DeepMount00/Llama-3-8b-Ita
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 79318d698494eac0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/79318d698494eac0_train_data.json
type:
field_instruction: prompt
field_output: gold_standard_solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: dzanbek/3b617198-24b4-461f-b00a-28da105dd0f6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 3b617198-24b4-461f-b00a-28da105dd0f6
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4305
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.235 | 0.0284 | 200 | 2.4305 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
infogep/699149fe-480a-4bce-b21e-6d0bd081abff
|
infogep
| 2025-04-28T23:54:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:adapter:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T23:39:53Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 699149fe-480a-4bce-b21e-6d0bd081abff
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: DeepMount00/Llama-3-8b-Ita
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 79318d698494eac0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/79318d698494eac0_train_data.json
type:
field_instruction: prompt
field_output: gold_standard_solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: infogep/699149fe-480a-4bce-b21e-6d0bd081abff
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/79318d698494eac0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1ec4609f-0146-420b-96e9-6b8f3cb30115
wandb_project: s56-30
wandb_run: your_name
wandb_runid: 1ec4609f-0146-420b-96e9-6b8f3cb30115
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 699149fe-480a-4bce-b21e-6d0bd081abff
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4438
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1731 | 0.0284 | 200 | 2.4438 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Flo0620/Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const
|
Flo0620
| 2025-04-28T23:52:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T19:04:59Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="Flo0620/Qwen2_5_7B_r64_a64_d0_2_lr2e-4_const", 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: 0.15.2
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mlx-community/Qwen3-32B-6bit
|
mlx-community
| 2025-04-28T22:58:34Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-04-28T22:51:43Z |
---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
base_model: Qwen/Qwen3-32B
---
# mlx-community/Qwen3-32B-6bit
This model [mlx-community/Qwen3-32B-6bit](https://huggingface.co/mlx-community/Qwen3-32B-6bit) was
converted to MLX format from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen3-32B-6bit")
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)
```
|
benjaminzwhite/Qwen2.5-3B-Instruct_GSM8K-GRPO_16bit
|
benjaminzwhite
| 2025-04-28T22:23:24Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"reasoning",
"mathematics",
"math",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:openai/gsm8k",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-12T21:54:45Z |
---
library_name: transformers
tags:
- unsloth
- reasoning
- mathematics
- math
datasets:
- openai/gsm8k
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
# Model Card for Model ID
This is an early experiment using the `GRPOTrainer` and training reasoning models using the Unsloth library. It is not intended for real use, but it should work OK for simple prompt tests and easy mathematics questions.
(You can run this using the code below on a free Colab/Kaggle basic GPU account for testing.)
**NOTE:** If you are interested in reasoning models and research in this area, I maintain an up-to-date resource list here : [https://github.com/benjaminzwhite/reasoning-models](https://github.com/benjaminzwhite/reasoning-models)
- Example query: `"What is the smallest prime number greater than 50 ?"`
- Example response: `"<reasoning>\nTo find the smallest prime number greater than 50, we can start checking from 51 onwards for primality. A prime number is a number that has no divisors other than 1 and itself. We check each number to see if it's divisible by any number other than 1 and itself.\n</reasoning>\n<answer>\n53\n</answer>"`
## How to Get Started with the Model
To use this with standard HuggingFace code, I recommend starting with this code (based 95% on the default code shown at the base model page : [https://huggingface.co/Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct))
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "benjaminzwhite/Qwen2.5-3B-Instruct_GSM8K-GRPO_16bit"
# model loading
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# system prompt used during training
SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""
# your query goes here
user_prompt = "What is the smallest prime number greater than 50 ?"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_prompt}
]
# default Qwen2.5 code from this point ...
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
# sample answer obtained to my query, to show expected format
# (note that the answer, 53, is correct here)
"""
"<reasoning>\nTo find the smallest prime number greater than 50, we can start checking from 51 onwards for primality. A prime number is a number that has no divisors other than 1 and itself. We check each number to see if it's divisible by any number other than 1 and itself.\n</reasoning>\n<answer>\n53\n</answer>"
"""
```
## 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. -->
Trained on GSM8K mathematics dataset.
|
kathleenge/kd_0.0001_33_2
|
kathleenge
| 2025-04-28T22:13:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T22:11:50Z |
---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kathleenge
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral 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)
|
RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf
|
RichardErkhov
| 2025-04-28T18:26:54Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T16:53:14Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
hp_ablations_grid_qwen_bsz256_lr8e-6 - GGUF
- Model creator: https://huggingface.co/mlfoundations-dev/
- Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_grid_qwen_bsz256_lr8e-6/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q2_K.gguf) | Q2_K | 2.81GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K.gguf) | Q3_K | 3.55GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_0.gguf) | Q4_0 | 4.13GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K.gguf) | Q4_K | 4.36GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_1.gguf) | Q4_1 | 4.54GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_0.gguf) | Q5_0 | 4.95GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K.gguf) | Q5_K | 5.07GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_1.gguf) | Q5_1 | 5.36GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q6_K.gguf) | Q6_K | 5.82GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: hp_ablations_grid_qwen_bsz256_lr8e-6
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. -->
# hp_ablations_grid_qwen_bsz256_lr8e-6
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the mlfoundations-dev/oh-dcft-v3-llama3.1-nemotron-70b_shareGPT_format dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5400
## 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: 8e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- 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: constant
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1738
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5455 | 0.9998 | 1155 | 0.5471 |
| 0.4733 | 1.9996 | 2310 | 0.5320 |
| 0.4074 | 2.9994 | 3465 | 0.5400 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk
|
BootesVoid
| 2025-04-28T18:18:38Z | 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
| 2025-04-28T18:18:35Z |
---
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: SHOWTIME
---
# Cma1Deifr00Ec12Tvgbajyff3_Cma1Djk6300Ek12Tvwcvf4Klk
<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 `SHOWTIME` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SHOWTIME",
"lora_weights": "https://huggingface.co/BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk/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('BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk', weight_name='lora.safetensors')
image = pipeline('SHOWTIME').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: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk/discussions) to add images that show off what you’ve made with this LoRA.
|
kirara16/gemma-3-4b-rea
|
kirara16
| 2025-04-28T17:49:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T17:49:30Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kirara16
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
maksf8486/3a5eefd8-9557-485a-b5ac-5da3e5ada13d
|
maksf8486
| 2025-04-28T17:43:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:adapter:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T17:40:17Z |
---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3a5eefd8-9557-485a-b5ac-5da3e5ada13d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4c99c18ef799ce51_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4c99c18ef799ce51_train_data.json
type:
field_input: knowledge
field_instruction: dialogue_history
field_output: right_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: false
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: maksf8486/3a5eefd8-9557-485a-b5ac-5da3e5ada13d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/4c99c18ef799ce51_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5c3ae6f1-b897-49d3-82bc-1ca1330bf1d7
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 5c3ae6f1-b897-49d3-82bc-1ca1330bf1d7
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 3a5eefd8-9557-485a-b5ac-5da3e5ada13d
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1666 | 0.1871 | 200 | 2.5062 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
RJTPP/stage4-deepseek1.5b-v6-step-gguf
|
RJTPP
| 2025-04-28T17:20:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T17:03:35Z |
---
base_model: unsloth/deepseek-r1-distill-qwen-1.5b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** RJTPP
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-1.5b-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)
|
mlfoundations-dev/nemo_nano_300k
|
mlfoundations-dev
| 2025-04-28T17:19:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T17:16:11Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: nemo_nano_300k
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. -->
# nemo_nano_300k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/nemo_nano_300k 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 512
- total_train_batch_size: 512
- total_eval_batch_size: 4096
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
MariaJas/Diabetica-7B-Q2_K-GGUF
|
MariaJas
| 2025-04-28T17:19:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"medical",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:WaltonFuture/Diabetica-SFT",
"base_model:WaltonFuture/Diabetica-7B",
"base_model:quantized:WaltonFuture/Diabetica-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-28T17:18:39Z |
---
base_model: WaltonFuture/Diabetica-7B
datasets:
- WaltonFuture/Diabetica-SFT
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- medical
- llama-cpp
- gguf-my-repo
---
# MariaJas/Diabetica-7B-Q2_K-GGUF
This model was converted to GGUF format from [`WaltonFuture/Diabetica-7B`](https://huggingface.co/WaltonFuture/Diabetica-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/WaltonFuture/Diabetica-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -c 2048
```
|
thiagoferrreira/SIte
|
thiagoferrreira
| 2025-04-28T16:54:46Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T16:54:46Z |
---
license: apache-2.0
---
|
DevQuasar/Qwen.Qwen2.5-7B-Instruct-GGUF
|
DevQuasar
| 2025-04-28T16:13:45Z | 155 | 0 | null |
[
"gguf",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-02-19T18:30:33Z |
---
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
kb9/Llama-3.2-3b-trans-eng-tun
|
kb9
| 2025-04-28T15:51:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T15:45:03Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kb9
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dgambettaphd/M_llm2_gen5_run0_S_doc1000_synt64_tot128_SYNLAST
|
dgambettaphd
| 2025-04-28T15:28:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T15:28:19Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Rziane/speaker-segmentation-ESLO-CAENNAIS28.04.25
|
Rziane
| 2025-04-28T15:18:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"fr",
"dataset:CAENNAIS",
"base_model:pyannote/segmentation-3.0",
"base_model:finetune:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T15:14:02Z |
---
library_name: transformers
language:
- fr
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- CAENNAIS
model-index:
- name: speaker-segmentation-ESLO-CAENNAIS28.04.25
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. -->
# speaker-segmentation-ESLO-CAENNAIS28.04.25
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the CAENNAIS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6832
- Model Preparation Time: 0.004
- Der: 0.2846
- False Alarm: 0.0902
- Missed Detection: 0.0839
- Confusion: 0.1105
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.7466 | 1.0 | 61 | 0.6336 | 0.004 | 0.2853 | 0.0872 | 0.0938 | 0.1043 |
| 0.6861 | 2.0 | 122 | 0.6770 | 0.004 | 0.2934 | 0.0876 | 0.0937 | 0.1122 |
| 0.6556 | 3.0 | 183 | 0.6686 | 0.004 | 0.2945 | 0.0880 | 0.0916 | 0.1149 |
| 0.6225 | 4.0 | 244 | 0.6746 | 0.004 | 0.2925 | 0.0820 | 0.0956 | 0.1149 |
| 0.627 | 5.0 | 305 | 0.6682 | 0.004 | 0.2912 | 0.0853 | 0.0905 | 0.1155 |
| 0.5885 | 6.0 | 366 | 0.6745 | 0.004 | 0.2913 | 0.0815 | 0.0909 | 0.1188 |
| 0.5649 | 7.0 | 427 | 0.6675 | 0.004 | 0.2847 | 0.0806 | 0.0916 | 0.1125 |
| 0.5478 | 8.0 | 488 | 0.6817 | 0.004 | 0.2831 | 0.0890 | 0.0838 | 0.1103 |
| 0.5282 | 9.0 | 549 | 0.6836 | 0.004 | 0.2852 | 0.0905 | 0.0839 | 0.1109 |
| 0.5361 | 10.0 | 610 | 0.6832 | 0.004 | 0.2846 | 0.0902 | 0.0839 | 0.1105 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
idolstranger/deepfake_audio_detection
|
idolstranger
| 2025-04-28T14:35:19Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-04-28T13:57:05Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: deepfake_audio_detection
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. -->
# deepfake_audio_detection
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0065
- eval_accuracy: 0.9988
- eval_runtime: 58.7898
- eval_samples_per_second: 85.049
- eval_steps_per_second: 2.671
- epoch: 2.0
- step: 626
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
|
Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF
|
Triangle104
| 2025-04-28T14:08:59Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3",
"base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T14:05:45Z |
---
base_model: ArliAI/QwQ-32B-ArliAI-RpR-v3
language:
- en
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg
---
# Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF
This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v3`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3) for more details on the model.
---
RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series.
RpR models use the same curated, deduplicated RP and creative writing
dataset used for RPMax, with a focus on variety to ensure high
creativity and minimize cross-context repetition. Users familiar with
RPMax will recognize the unique, non-repetitive writing style unlike
other finetuned-for-RP models.
With the release of QwQ as the first high performing open-source
reasoning model that can be easily trained, it was clear that the
available instruct and creative writing reasoning datasets contains only
one response per example. This is type of single response dataset used
for training reasoning models causes degraded output quality in long
multi-turn chats. Which is why Arli AI decided to create a real RP model
capable of long multi-turn chat with reasoning.
In order to create RpR, we first had to actually create the reasoning
RP dataset by re-processing our existing known-good RPMax dataset into a
reasoning dataset. This was possible by using the base QwQ Instruct
model itself to create the reasoning process for every turn in the RPMax
dataset conversation examples, which is then further refined in order
to make sure the reasoning is in-line with the actual response examples
from the dataset.
Another important thing to get right is to make sure the model is
trained on examples that present reasoning blocks in the same way as it
encounters it during inference. Which is, never seeing the reasoning
blocks in it's context. In order to do this, the training run was
completed using axolotl with manual template-free segments dataset in
order to make sure that the model is never trained to see the reasoning
block in the context. Just like how the model will be used during
inference time.
The result of training QwQ on this dataset with this method are
consistently coherent and interesting outputs even in long multi-turn RP
chats. This is as far as we know the first true correctly-trained
reasoning model trained for RP and creative writing.
You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking
Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/
---
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -c 2048
```
|
Aluba/Comeback_v1_4
|
Aluba
| 2025-04-28T11:35:00Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-04-28T11:21:41Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF
|
mradermacher
| 2025-04-28T11:30:22Z | 231 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:nbeerbower/EVA-abliterated-Qwen2.5-7B",
"base_model:quantized:nbeerbower/EVA-abliterated-Qwen2.5-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-01T05:50:03Z |
---
base_model: nbeerbower/EVA-abliterated-Qwen2.5-7B
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/nbeerbower/EVA-abliterated-Qwen2.5-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-Qwen2.5-7B-GGUF/resolve/main/EVA-abliterated-Qwen2.5-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
NexesMess/Llama_3.x_70b_TDRussel-Storywriter_128K_Dop_v1.02
|
NexesMess
| 2025-04-28T10:51:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2406.11617",
"base_model:nbeerbower/Llama3.1-Gutenberg-Doppel-70B",
"base_model:merge:nbeerbower/Llama3.1-Gutenberg-Doppel-70B",
"base_model:tdrussell/Llama-3-70B-Instruct-Storywriter",
"base_model:merge:tdrussell/Llama-3-70B-Instruct-Storywriter",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T14:50:03Z |
---
base_model:
- nbeerbower/Llama3.1-Gutenberg-Doppel-70B
- tdrussell/Llama-3-70B-Instruct-Storywriter
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [nbeerbower/Llama3.1-Gutenberg-Doppel-70B](https://huggingface.co/nbeerbower/Llama3.1-Gutenberg-Doppel-70B) as a base.
### Models Merged
The following models were included in the merge:
* [tdrussell/Llama-3-70B-Instruct-Storywriter](https://huggingface.co/tdrussell/Llama-3-70B-Instruct-Storywriter)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: della_linear
base_model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
models:
- model: tdrussell/Llama-3-70B-Instruct-Storywriter
parameters:
weight: # layer per layer
- filter: q_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: k_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: v_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: o_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: input_layernorm
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: up_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: gate_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: down_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: post_attention_layernorm
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
density: 0.5
epsilon: 0.1
lambda: 1.0
- model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
parameters:
weight: 1.0
density: # layer per layer
- filter: q_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: k_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: v_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: o_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: input_layernorm
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: up_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: gate_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: down_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- filter: post_attention_layernorm
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
epsilon: # layer per layer
- filter: q_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: k_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: v_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: o_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: input_layernorm
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: up_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: gate_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: down_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- filter: post_attention_layernorm
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0.0425, 0.045, 0.0475, 0.05, 0.0525, 0.055, 0.0575, 0.06, 0.0625, 0.065, 0.0675, 0.07, 0.0725, 0.075, 0.0775, 0.08, 0.0825, 0.085, 0.0875, 0.09, 0.0925, 0.095, 0.0975, 0.1, 0.0975, 0.095, 0.0925, 0,09, 0.0875, 0.085, 0.0825, 0.08, 0.0775, 0.075, 0.0725, 0,07, 0.0675, 0.065, 0.0625, 0.06, 0.0575, 0.055, 0.0525, 0.05, 0.0475, 0.045, 0.0425, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
lambda: 1.0
dtype: float32
out_dtype: bfloat16
parameters:
int8_mask: true
normalize: true
rescale: true
chat_template: auto
tokenizer:
source: union
```
|
ob238ZXSYn/ob238ZXSYn
|
ob238ZXSYn
| 2025-04-28T10:35:16Z | 0 | 0 | null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-04-28T10:35:14Z |
---
license: bigcode-openrail-m
---
|
Netlive/ModernBertModel_DE_Dseed_seed2023
|
Netlive
| 2025-04-28T10:31:42Z | 0 | 0 | null |
[
"safetensors",
"electra",
"region:us"
] | null | 2025-04-28T10:31:31Z |
---
{}
---
# ModernBertModel_DE_seed2023
This is a BERT-based sequence classification model fine-tuned to identify interesting documents in German.
## Evaluation Metrics:
- Precision: 0.9988
- F1 Score: 0.9994
- Accuracy: 0.9994
- ROC AUC: 1.0000
- Custom Score: 0.9995
## Labels:
- 0 → OTHER
- 1 → INTERESTING
## Trained with:
- Libraries: HuggingFace Transformers + W&B
- Focal Loss + seed
## Developer:Niro
|
yasserrmd/qwen2.5-html-0.5b
|
yasserrmd
| 2025-04-28T09:32:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:ttbui/html_alpaca",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-03T04:51:10Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: Qwen/Qwen2.5-0.5B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- ttbui/html_alpaca
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "yasserrmd/qwen2.5-html-0.5b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "generate a sample html for dashboard"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
FundamentalResearchLabs/p-lo-d16-578171-1577279-8e3389f-s2526
|
FundamentalResearchLabs
| 2025-04-28T08:37:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-3-12b-it",
"base_model:adapter:google/gemma-3-12b-it",
"region:us"
] | null | 2025-04-28T08:37:14Z |
---
base_model: google/gemma-3-12b-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
|
HoseaDev/qwen3b-sql-fine-train
|
HoseaDev
| 2025-04-28T08:12:24Z | 0 | 0 | null |
[
"safetensors",
"gguf",
"qwen2",
"unsloth",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T07:56:58Z |
---
license: mit
tags:
- unsloth
---
|
Triangle104/Qwen2.5-14B-Q6_K-GGUF
|
Triangle104
| 2025-04-28T05:31:50Z | 10 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-14B",
"base_model:quantized:Qwen/Qwen2.5-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-19T14:58:15Z |
---
base_model: Qwen/Qwen2.5-14B
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-14B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Qwen2.5-14B-Q6_K-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-14B`](https://huggingface.co/Qwen/Qwen2.5-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-14B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Qwen2.5-14B-Q6_K-GGUF --hf-file qwen2.5-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen2.5-14B-Q6_K-GGUF --hf-file qwen2.5-14b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Qwen2.5-14B-Q6_K-GGUF --hf-file qwen2.5-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen2.5-14B-Q6_K-GGUF --hf-file qwen2.5-14b-q6_k.gguf -c 2048
```
|
Triangle104/Qwen2.5-7B-Instruct-Q5_K_S-GGUF
|
Triangle104
| 2025-04-28T05:25:42Z | 3 | 0 | null |
[
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-19T15:25:09Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# Triangle104/Qwen2.5-7B-Instruct-Q5_K_S-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-7b-instruct-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen2.5-7B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-7b-instruct-q5_k_s.gguf -c 2048
```
|
Anish13/poca-SoccerTwos
|
Anish13
| 2025-04-28T03:42:02Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2025-04-28T03:34:22Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Anish13/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dgambettaphd/M_llm2_gen3_run0_X_doc1000_synt64_tot128_SYNLAST
|
dgambettaphd
| 2025-04-28T00:21:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T00:21:33Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mlfoundations-dev/c1_code_nod_16s_1k
|
mlfoundations-dev
| 2025-04-28T00:08:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T21:30:52Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_code_nod_16s_1k
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. -->
# c1_code_nod_16s_1k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_code_nod_16s_1k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
MrRobotoAI/D4
|
MrRobotoAI
| 2025-04-28T00:05:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Azazelle/L3-Hecate-8B-v1.2",
"base_model:merge:Azazelle/L3-Hecate-8B-v1.2",
"base_model:Azazelle/Llama-3-8B-Abomination-LORA",
"base_model:merge:Azazelle/Llama-3-8B-Abomination-LORA",
"base_model:Azazelle/Llama-3-LongStory-LORA",
"base_model:merge:Azazelle/Llama-3-LongStory-LORA",
"base_model:Blackroot/Llama-3-LongStory-LORA",
"base_model:merge:Blackroot/Llama-3-LongStory-LORA",
"base_model:Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B",
"base_model:merge:Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B",
"base_model:Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL",
"base_model:merge:Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL",
"base_model:MrRobotoAI/D3",
"base_model:merge:MrRobotoAI/D3",
"base_model:ResplendentAI/NoWarning_Llama3",
"base_model:merge:ResplendentAI/NoWarning_Llama3",
"base_model:ResplendentAI/Nymph_8B",
"base_model:merge:ResplendentAI/Nymph_8B",
"base_model:aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench",
"base_model:merge:aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench",
"base_model:athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5",
"base_model:merge:athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5",
"base_model:hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit",
"base_model:merge:hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit",
"base_model:jeiku/Average_Normie_v3.69_8B",
"base_model:merge:jeiku/Average_Normie_v3.69_8B",
"base_model:jeiku/Tuldur-8B",
"base_model:merge:jeiku/Tuldur-8B",
"base_model:jeiku/UnPoppy_8B",
"base_model:merge:jeiku/UnPoppy_8B",
"base_model:jrahn/llama-3-8b-claudstruct-v3",
"base_model:merge:jrahn/llama-3-8b-claudstruct-v3",
"base_model:jspr/llama3-instruct-wordcel-smutrom-8k_peft",
"base_model:merge:jspr/llama3-instruct-wordcel-smutrom-8k_peft",
"base_model:jspr/smut_llama_8b_peft",
"base_model:merge:jspr/smut_llama_8b_peft",
"base_model:jspr/smut_llama_8b_smut_2k_romance_1k_peft",
"base_model:merge:jspr/smut_llama_8b_smut_2k_romance_1k_peft",
"base_model:jspr/smut_llama_8b_smutromance_32k_peft",
"base_model:merge:jspr/smut_llama_8b_smutromance_32k_peft",
"base_model:nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K",
"base_model:merge:nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K",
"base_model:nothingiisreal/llama3-8B-DWP-lora",
"base_model:merge:nothingiisreal/llama3-8B-DWP-lora",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T21:01:44Z |
---
base_model:
- MrRobotoAI/D3
- jspr/smut_llama_8b_smutromance_32k_peft
- jeiku/UnPoppy_8B
- MrRobotoAI/D3
- nothingiisreal/llama3-8B-DWP-lora
- MrRobotoAI/D3
- jspr/llama3-instruct-wordcel-smutrom-8k_peft
- MrRobotoAI/D3
- jspr/smut_llama_8b_peft
- MrRobotoAI/D3
- Azazelle/Llama-3-8B-Abomination-LORA
- MrRobotoAI/D3
- ResplendentAI/NoWarning_Llama3
- ResplendentAI/Nymph_8B
- jeiku/Tuldur-8B
- jeiku/Average_Normie_v3.69_8B
- nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K
- Blackroot/Llama-3-LongStory-LORA
- MrRobotoAI/D3
- jspr/smut_llama_8b_smut_2k_romance_1k_peft
- Azazelle/L3-Hecate-8B-v1.2
- Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL
- ResplendentAI/NoWarning_Llama3
- MrRobotoAI/D3
- Azazelle/Llama-3-LongStory-LORA
- MrRobotoAI/D3
- aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench
- MrRobotoAI/D3
- jrahn/llama-3-8b-claudstruct-v3
- Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B
- ResplendentAI/NoWarning_Llama3
- MrRobotoAI/D3
- athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5
- hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) as a base.
### Models Merged
The following models were included in the merge:
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [jspr/smut_llama_8b_smutromance_32k_peft](https://huggingface.co/jspr/smut_llama_8b_smutromance_32k_peft)
* [jeiku/UnPoppy_8B](https://huggingface.co/jeiku/UnPoppy_8B)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [nothingiisreal/llama3-8B-DWP-lora](https://huggingface.co/nothingiisreal/llama3-8B-DWP-lora)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [jspr/llama3-instruct-wordcel-smutrom-8k_peft](https://huggingface.co/jspr/llama3-instruct-wordcel-smutrom-8k_peft)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [jspr/smut_llama_8b_peft](https://huggingface.co/jspr/smut_llama_8b_peft)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [Azazelle/Llama-3-8B-Abomination-LORA](https://huggingface.co/Azazelle/Llama-3-8B-Abomination-LORA)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3)
* [ResplendentAI/Nymph_8B](https://huggingface.co/ResplendentAI/Nymph_8B)
* [jeiku/Tuldur-8B](https://huggingface.co/jeiku/Tuldur-8B)
* [jeiku/Average_Normie_v3.69_8B](https://huggingface.co/jeiku/Average_Normie_v3.69_8B)
* [nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K](https://huggingface.co/nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K) + [Blackroot/Llama-3-LongStory-LORA](https://huggingface.co/Blackroot/Llama-3-LongStory-LORA)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [jspr/smut_llama_8b_smut_2k_romance_1k_peft](https://huggingface.co/jspr/smut_llama_8b_smut_2k_romance_1k_peft)
* [Azazelle/L3-Hecate-8B-v1.2](https://huggingface.co/Azazelle/L3-Hecate-8B-v1.2)
* [Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL](https://huggingface.co/Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [Azazelle/Llama-3-LongStory-LORA](https://huggingface.co/Azazelle/Llama-3-LongStory-LORA)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench](https://huggingface.co/aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench)
* [MrRobotoAI/D3](https://huggingface.co/MrRobotoAI/D3) + [jrahn/llama-3-8b-claudstruct-v3](https://huggingface.co/jrahn/llama-3-8b-claudstruct-v3)
* [Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B](https://huggingface.co/Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3)
* [athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5](https://huggingface.co/athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5)
* [hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit](https://huggingface.co/hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: MrRobotoAI/D3+jspr/llama3-instruct-wordcel-smutrom-8k_peft
- model: MrRobotoAI/D3+jspr/smut_llama_8b_smutromance_32k_peft
- model: MrRobotoAI/D3+jspr/smut_llama_8b_smut_2k_romance_1k_peft
- model: MrRobotoAI/D3+jspr/smut_llama_8b_peft
- model: Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B+ResplendentAI/NoWarning_Llama3
- model: Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL+ResplendentAI/NoWarning_Llama3
- model: nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K+Blackroot/Llama-3-LongStory-LORA
- model: MrRobotoAI/D3+nothingiisreal/llama3-8B-DWP-lora
- model: MrRobotoAI/D3+aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench
- model: MrRobotoAI/D3+jrahn/llama-3-8b-claudstruct-v3
- model: MrRobotoAI/D3+Azazelle/Llama-3-8B-Abomination-LORA
- model: MrRobotoAI/D3+Azazelle/Llama-3-LongStory-LORA
- model: MrRobotoAI/D3+ResplendentAI/NoWarning_Llama3
- model: hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit
- model: athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5
- model: jeiku/Tuldur-8B
- model: jeiku/Average_Normie_v3.69_8B
- model: jeiku/UnPoppy_8B
- model: Azazelle/L3-Hecate-8B-v1.2
- model: ResplendentAI/Nymph_8B
merge_method: model_stock
base_model: MrRobotoAI/D3
normalize: true
dtype: float16
```
|
mgunaydin/unit_4
|
mgunaydin
| 2025-04-28T00:03:20Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T00:03:19Z |
---
license: apache-2.0
---
|
vitus9988/Qwen2.5-0.5B-ko-merge
|
vitus9988
| 2025-04-27T23:21:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:MarkrAI/KOpen-HQ-Hermes-2.5-60K",
"arxiv:2306.01708",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:merge:Qwen/Qwen2.5-0.5B",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:merge:Qwen/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T13:29:24Z |
---
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
- Qwen/Qwen2.5-0.5B
library_name: transformers
tags:
- mergekit
- merge
datasets:
- MarkrAI/KOpen-HQ-Hermes-2.5-60K
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Qwen2.5-0.5B-ko-merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) as a base.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
* Qwen2.5-0.5B-Instruct-lora-merge
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Qwen2.5-0.5B-Instruct-lora-merge
parameters:
weight: 1
density: 1
- model: Qwen/Qwen2.5-0.5B-Instruct
parameters:
weight: 1
density: 1
merge_method: ties
base_model: Qwen/Qwen2.5-0.5B
parameters:
weight: 1
density: 1
normalize: true
int8_mask: true
dtype: bfloat16
```
|
Quest-AI/quest-corruption-7b-s375-v3-GRPO
|
Quest-AI
| 2025-04-27T23:18:34Z | 15 | 5 | null |
[
"safetensors",
"qwen2",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:Quest-AI/quest-corruption-truncated4grpo-6k-dataset-v1",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-02-24T14:10:27Z |
---
license: apache-2.0
datasets:
- Quest-AI/quest-corruption-truncated4grpo-6k-dataset-v1
base_model:
- Qwen/Qwen2.5-7B
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
Custom pseudo "fill in the middle" trained model, designed to handle varying "corruption rates" (randomized UTF8 character substitution).
Two custom GRPO reward functions were used to improve the pre-existing SFT trained model in order to have it more reliably attend to the XML styling.
Designed to be used with the (jank, hacky, personalized) PyQT GUI tooling seen at:
https://github.com/kalomaze/quest-tools
Wandb logs for this run can be found [here,](https://wandb.ai/kalomaze/verifiers-examples/runs/7l9b6pvi?nw=nwuserkalomaze) as well as the [attached RL code](https://wandb.ai/kalomaze/verifiers-examples/runs/7l9b6pvi/files/run_files_20250224_112244).
Full hyperparameters are observable in the configuration py as well.
## Prompt Formatting
Trained without ChatML templating. This model uses a pattern of:
- Raw "corrupted" text at the beginning with UTF8 substitution for parts of the input.
- The "objective" as a Claude-style XML tag with newline separators.
- The beginning of an "original" tag.
```
def _format_prompt(self, example: Dict) -> str:
return (
f"{example['corrupted']}\n\n"
"<objective>\n"
"gently repair the <original> content\n"
"</objective>\n\n"
"<original>\n"
)
```
The primary utility of this model is as a means to synthesize rejected / lower quality preference data from pre-existing SFT data (i.e, the general pretraining corpus).
This is useful in the context of teaching a reward model **generalized preferences** from lower quality, subtly incoherent base model-esque completions, of which are trivial to produce compared to human annotations.
## Acknowledgements
Trained on 8xH200s provided free of charge by [Deepshard](https://github.com/deepshard) for research & open source experimentation. Big McThankies.
|
TUANLIEM111/TUANLIME
|
TUANLIEM111
| 2025-04-27T23:04:35Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-04-27T23:04:34Z |
---
license: bigscience-openrail-m
---
|
brixablox/Arsenic
|
brixablox
| 2025-04-27T22:24:10Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-04-27T22:24:09Z |
---
license: cc-by-nc-nd-4.0
---
|
3mily1u/new-codegen-350m-mono-dpoed-control-50-1
|
3mily1u
| 2025-04-27T22:22:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T22:21:29Z |
---
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. 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]
|
10-NEW-EXCLUSIVE-HOT-CLIP/FULL.VIDEO.LINK.Samiya.Hijab.Viral.Video.Leaks.official
|
10-NEW-EXCLUSIVE-HOT-CLIP
| 2025-04-27T21:29:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T21:28:49Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/24tm3bsa?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Samiya Hijab Viral Video Trending: watch, Full Story, Facts & Public Reaction
Table of content
Discover the real story behind the Samiya Hijab viral video that's trending across social media. What happened, why it's viral, and public response – all here.
The Samiya Hijab viral video has captured widespread attention online, creating waves on platforms like TikTok, Instagram, and Twitter. In this post, we will explore what the video is about, why it became viral, and how it reflects social trends and public sentiments. This post follows Blogger, AdSense, and SEO guidelines and contains no explicit content. It's focused on information, awareness, and responsible reporting while keeping our audience updated with accurate details.
|
iancu003/WhichFelineIsIt
|
iancu003
| 2025-04-27T21:12:47Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2025-04-27T13:55:25Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Ishan0612/biobert-ner-disease
|
Ishan0612
| 2025-04-27T19:04:49Z | 52 | 2 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"medical-ner",
"biobert",
"healthcare",
"disease-extraction",
"named-entity-recognition",
"huggingface",
"ncbi-disease-dataset",
"en",
"dataset:ncbi/ncbi_disease",
"base_model:dmis-lab/biobert-base-cased-v1.1",
"base_model:finetune:dmis-lab/biobert-base-cased-v1.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-12T21:34:11Z |
---
library_name: transformers
tags:
- medical-ner
- biobert
- healthcare
- disease-extraction
- named-entity-recognition
- huggingface
- ncbi-disease-dataset
license: apache-2.0
datasets:
- ncbi/ncbi_disease
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- dmis-lab/biobert-base-cased-v1.1
pipeline_tag: token-classification
---
# Note: New Improved Model Available (F1 Score: 89.04%)
For better performance and an **improved F1 score, please use the updated model here: https://huggingface.co/Ishan0612/biobert-ner-disease-ncbi**
# BioBERT Disease NER Model
One of the **powerful medical NER models**, fine-tuned on BioBERT with the NCBI Disease dataset.
It achieves **98.79% accuracy** and an **F1-score of 86.98%**, delivering reliable performance for disease
extraction tasks by accurately identifying diseases and symptoms in medical texts.
# Model Performance
- **Precision:** 85.69%
- **Recall:** 88.31%
- **F1-Score:** 86.98%
- **Accuracy:** 98.79%
✅ Fine-tuned over **6,800+ annotated examples** for **5 epochs**, achieving consistently high validation scores.
## Intended Use
This model is designed for:
- Extracting disease mentions from clinical and biomedical texts.
- Powering information retrieval, research automation, and medical chatbots.
# Training Data
This model was trained on the [NCBI disease dataset](https://huggingface.co/datasets/ncbi_disease), which consists of 793 PubMed abstracts with 6892 disease mentions.
## How to Use
You can use this model with the Hugging Face Transformers library:
*Note:* LABEL_0 corresponds to "O" (Outside), LABEL_1 to "B-Disease", and LABEL_2 to "I-Disease" following the BIO tagging format.
```python
from transformers import pipeline
# Load from Hugging Face
nlp = pipeline("ner", model="Ishan0612/biobert_medical_ner", tokenizer="Ishan0612/biobert_medical_ner", aggregation_strategy="simple")
# Sample medical text
text = """Robert suffering from chest pain and thiroid."""
# Extract entities
ner_results = nlp(text)
# Display results
print("Extracted Medical Entities:")
for entity in ner_results:
print(f"{entity['word']} ({entity['entity_group']}) - Confidence: {entity['score']:.2f}")
```
This should output:
Extracted Medical Entities:
Robert suffering from (LABEL_0) - Confidence: 1.00
chest (LABEL_1) - Confidence: 1.00
pain (LABEL_2) - Confidence: 1.00
and (LABEL_0) - Confidence: 1.00
th (LABEL_1) - Confidence: 1.00
##iroid (LABEL_2) - Confidence: 0.97
. (LABEL_0) - Confidence: 1.00
|
Jonjew/LilyCollinsCa2008
|
Jonjew
| 2025-04-27T17:52:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] |
text-to-image
| 2025-04-27T17:52:01Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
<lora:Lily_Collins_Ca2008:1> woman Film Still Print, overlaid on the very
bottom of the image is silver hand-written text that says "all my love",
Looking Directly At The Viewer, Centered, Making Eye Contact, Looking
Straight Ahead, Looking Forward, Striking A Dynamic Pose,
<lora:zz_s_Chest_Size_Slider:-2> buttoned up top
output:
url: images/Lily_Collins_Ca2008_0011.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: woman
license: unknown
---
# Lily Collins (Ca 2008) by matziq
<Gallery />
## Model description
FROM https://civitai.com/models/1510831/lily-collins-ca-2008?modelVersionId=1709008
Please support the original creator by donating BUZZ and LIKING at the PAGE ABOVE
Trigger woman
## Trigger words
You should use `woman` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/LilyCollinsCa2008/tree/main) them in the Files & versions tab.
|
bikingSolo/vk-nlp-course-hometask-2-reward-model
|
bikingSolo
| 2025-04-27T17:03:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-classification",
"en",
"dataset:HumanLLMs/Human-Like-DPO-Dataset",
"base_model:HuggingFaceTB/SmolLM-135M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM-135M-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-27T09:06:30Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HumanLLMs/Human-Like-DPO-Dataset
language:
- en
base_model:
- HuggingFaceTB/SmolLM-135M-Instruct
pipeline_tag: text-classification
---
# Описание
Данная модель была создана в рамках курса по [NLP от VK](https://education.vk.company/program/kurs-nlp-yazykovye-modeli-ml).
Задание заключалось в том, чтобы сделать alignment модели с помощью PPO (модель бралась instruct, то есть уже после SFT).
Данная модель - обученная Reward Model. Базовая модель: HuggingFaceTB/SmolLM-135M-Instruct. Обучался только один линейный слой поверх.
Обучался один выход - логит (без подачи в сигмоиду).
Набор данных: HumanLLMs/Human-Like-DPO-Dataset.
# Обучение
Обучалось с помощью [TRL](https://huggingface.co/docs/trl/en/index).
* num_train_epochs=1,
* per_device_train_batch_size=2
* gradient_accumulation_steps=8
* max_length=1024
* disable_dropout=True
* learning_rate=3e-4
* seed=42
Обучалось в Kaggle на GPU P100 примерно 30 минут.
|
mlfoundations-dev/c1_science_10d_4s_0.3k
|
mlfoundations-dev
| 2025-04-27T15:08:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:05:21Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_science_10d_4s_0.3k
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. -->
# c1_science_10d_4s_0.3k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_10d_4s_0.3k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 13.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
mlfoundations-dev/c1_math_nod_4s_1k
|
mlfoundations-dev
| 2025-04-27T15:07:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T15:04:56Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: c1_math_nod_4s_1k
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. -->
# c1_math_nod_4s_1k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_math_nod_4s_1k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0a0+b465a5843b.nv24.09
- Datasets 3.5.0
- Tokenizers 0.20.3
|
DoppelReflEx/QWQ-32B-ForgeinFlow-TokenizerTest-Experiment
|
DoppelReflEx
| 2025-04-27T14:26:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3",
"base_model:merge:ArliAI/QwQ-32B-ArliAI-RpR-v3",
"base_model:Qwen/QwQ-32B",
"base_model:merge:Qwen/QwQ-32B",
"base_model:trashpanda-org/QwQ-32B-Snowdrop-v0",
"base_model:merge:trashpanda-org/QwQ-32B-Snowdrop-v0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-27T14:08:54Z |
---
base_model:
- trashpanda-org/QwQ-32B-Snowdrop-v0
- ArliAI/QwQ-32B-ArliAI-RpR-v3
- Qwen/QwQ-32B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) as a base.
### Models Merged
The following models were included in the merge:
* [trashpanda-org/QwQ-32B-Snowdrop-v0](https://huggingface.co/trashpanda-org/QwQ-32B-Snowdrop-v0)
* [ArliAI/QwQ-32B-ArliAI-RpR-v3](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: trashpanda-org/QwQ-32B-Snowdrop-v0
parameters:
density: 0.9
weight: 1
- model: ArliAI/QwQ-32B-ArliAI-RpR-v3
parameters:
density: 0.8
weight: 0.8
merge_method: dare_ties
base_model: Qwen/QwQ-32B
parameters:
normalize: true
rescale: true
tokenizer_source: Qwen/Qwen2.5-32B-Instruct
dtype: bfloat16
```
|
casque/PhoneExposurePonyXL
|
casque
| 2025-04-27T14:25:54Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-04-27T14:25:01Z |
---
license: creativeml-openrail-m
---
|
wsbagnsv1/SkyReels-V2-DF-1.3B-540P-GGUF
|
wsbagnsv1
| 2025-04-27T12:16:35Z | 18 | 0 |
gguf
|
[
"gguf",
"video",
"video-generation",
"image-to-video",
"base_model:Skywork/SkyReels-V2-DF-1.3B-540P",
"base_model:quantized:Skywork/SkyReels-V2-DF-1.3B-540P",
"license:apache-2.0",
"region:us"
] |
image-to-video
| 2025-04-25T09:42:31Z |
---
license: apache-2.0
library_name: gguf
base_model:
- Skywork/SkyReels-V2-DF-1.3B-540P
tags:
- video
- video-generation
pipeline_tag: image-to-video
---
This is a direct GGUF conversion of [Skywork/SkyReels-V2-DF-1.3B-540P](https://huggingface.co/Skywork/SkyReels-V2-DF-1.3B-540P)
All quants are created from the FP32 base file, though I only uploaded the Q8_0 and less, if you want the F16 or BF16 one I would upload it per request.
The model files can be used with the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) custom node.
Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.
The VAE can be downloaded from [this repository by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VAE_bf16.safetensors)
Please refer to [this chart](https://github.com/ggerganov/llama.cpp/blob/master/examples/perplexity/README.md#llama-3-8b-scoreboard) for a basic overview of quantization types.
For conversion I used the conversion scripts from [city96](https://huggingface.co/city96)
|
8-NEW-EXCLUSIVE-TRENDING-CLIP/FULL-VIDEO-LINK-Bu.Guru.Salsa.Viral.Video.Leaks.official.tutorial
|
8-NEW-EXCLUSIVE-TRENDING-CLIP
| 2025-04-27T11:06:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-04-27T11:05:17Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/2x869u6x?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Bu Guru Salsa Video 5 Menit Trending kini viral dan banyak dicari! Tonton dan unduh video viral ini yang sedang ramai di berbagai platform sosial media. Jangan lewatkan momen menarik dari video yang membuat netizen heboh. Cari tahu kenapa video ini menjadi tren dan bagaimana reaksi netizen. Temukan link serta informasi lengkapnya di sini! Kami menyediakan pembahasan detail dan cara menonton video dengan kualitas terbaik. Jangan sampai ketinggalan hype dari video yang sedang viral ini! Klik sekarang dan dapatkan akses eksklusif ke video terbaru yang sedang booming!
|
fedovtt/d4ce9e78-4ab5-4438-994f-d6af54908e06
|
fedovtt
| 2025-04-26T20:54:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:numind/NuExtract-1.5",
"base_model:adapter:numind/NuExtract-1.5",
"license:mit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-26T20:45:53Z |
---
library_name: peft
license: mit
base_model: numind/NuExtract-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d4ce9e78-4ab5-4438-994f-d6af54908e06
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: numind/NuExtract-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 00609df011d63e92_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/00609df011d63e92_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: fedovtt/d4ce9e78-4ab5-4438-994f-d6af54908e06
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/00609df011d63e92_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77d08cc6-0207-4bb4-9813-e44e8a8c2c9e
wandb_project: s56-1
wandb_run: your_name
wandb_runid: 77d08cc6-0207-4bb4-9813-e44e8a8c2c9e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d4ce9e78-4ab5-4438-994f-d6af54908e06
This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6501 | 0.0812 | 200 | 0.5708 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
bhaviktheslider/json-lora-merged-16bit-model
|
bhaviktheslider
| 2025-04-26T20:53:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"grpo",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-26T20:48:51Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- grpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bhaviktheslider
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
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)
|
DanielNRU/pollen-ner-cycle-100
|
DanielNRU
| 2025-04-26T15:05:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/bert-base-bg-cs-pl-ru-cased",
"base_model:adapter:DeepPavlov/bert-base-bg-cs-pl-ru-cased",
"region:us"
] | null | 2025-04-25T05:51:37Z |
---
library_name: peft
base_model: DeepPavlov/bert-base-bg-cs-pl-ru-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-cycle-100
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. -->
# pollen-ner-cycle-100
This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1291
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 13 | 1.8381 | 0.0194 | 0.0232 | 0.0211 |
| No log | 2.0 | 26 | 1.5410 | 0.0254 | 0.0058 | 0.0094 |
| No log | 3.0 | 39 | 1.2779 | 0.1154 | 0.0058 | 0.0110 |
| 1.5907 | 4.0 | 52 | 1.1574 | 0.0 | 0.0 | 0.0 |
| 1.5907 | 5.0 | 65 | 1.1291 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Docty/cloth_controlnet
|
Docty
| 2025-04-26T01:35:01Z | 7 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:mit",
"region:us"
] |
text-to-image
| 2025-04-22T01:14:59Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
inference: true
license: mit
library_name: diffusers
instance_prompt: a professional studio photograph of an attractive model wearing a
teal top with lace detail
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ControlNet for cloth- Docty/cloth_controlnet
These are ControlNet for stable-diffusion-v1-5/stable-diffusion-v1-5. You can find some example images in the following.
```python
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch
base_model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
controlnet_path = "Docty/cloth_controlnet"
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model_path, controlnet=controlnet, torch_dtype=torch.float16
)
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed or when using Torch 2.0.
#pipe.enable_xformers_memory_efficient_attention()
# memory optimization.
pipe.enable_model_cpu_offload()
control_image = load_image("./cond1.jpg")
prompt = "a professional studio photograph of an attractive model wearing a teal top with lace detail"
# generate image
#generator = torch.manual_seed(0)
image = pipe(
prompt, num_inference_steps=20, image=control_image
).images[0]
image
```
|
nolalaverna/nolalaverna
|
nolalaverna
| 2025-04-26T00:57:53Z | 0 | 0 | null |
[
"license:bsd-2-clause",
"region:us"
] | null | 2025-04-26T00:57:53Z |
---
license: bsd-2-clause
---
|
aryolotfi/SFT_gsm8k_Mistral-7B-v0.1_epoch_5_global_step_145
|
aryolotfi
| 2025-04-25T22:22:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-25T22:21:02Z |
---
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]
|
KodaCodex/clinical-qa-model
|
KodaCodex
| 2025-04-25T21:53:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T21:52:59Z |
---
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]
|
msamdan/meu-llama-3-8b
|
msamdan
| 2025-04-24T21:54:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"unsloth",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-24T21:32:18Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
owenpastor21/LawPseudoReasonSmall-Qwen25R15
|
owenpastor21
| 2025-04-24T12:13:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-24T12:12:41Z |
---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** owenpastor21
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-1.5B-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)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
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The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.