modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
filippo-baglini/biobert_finetuned_ncbi_disease | filippo-baglini | 2025-05-21T14:03:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"base_model:dmis-lab/biobert-base-cased-v1.1",
"base_model:finetune:dmis-lab/biobert-base-cased-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-21T13:09:26Z | ---
library_name: transformers
base_model: dmis-lab/biobert-base-cased-v1.1
tags:
- generated_from_trainer
datasets:
- ncbi_disease
model-index:
- name: biobert_finetuned_ncbi_disease
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. -->
# biobert_finetuned_ncbi_disease
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0878
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7803 | 1.0 | 170 | 0.2916 |
| 0.1847 | 2.0 | 340 | 0.1073 |
| 0.0879 | 3.0 | 510 | 0.0804 |
| 0.0565 | 4.0 | 680 | 0.0732 |
| 0.0421 | 5.0 | 850 | 0.0759 |
| 0.0329 | 6.0 | 1020 | 0.0772 |
| 0.0264 | 7.0 | 1190 | 0.0786 |
| 0.0215 | 8.0 | 1360 | 0.0788 |
| 0.0173 | 9.0 | 1530 | 0.0857 |
| 0.015 | 10.0 | 1700 | 0.0878 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
FormlessAI/68fa5dd8-4567-460b-91a7-3a9adbe58b81 | FormlessAI | 2025-05-21T14:03:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T11:26:03Z | ---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
library_name: transformers
model_name: 68fa5dd8-4567-460b-91a7-3a9adbe58b81
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 68fa5dd8-4567-460b-91a7-3a9adbe58b81
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B).
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="FormlessAI/68fa5dd8-4567-460b-91a7-3a9adbe58b81", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/z46gcfth)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0+cu118
- Datasets: 3.5.1
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
nguyenvulebinh/audio-seg-diarization | nguyenvulebinh | 2025-05-21T14:03:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"PyanNet",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T12:38:19Z | ---
library_name: transformers
tags: []
---
# Audio segmentation powered by speaker diarization
```bash
git clone https://github.com/nguyenvulebinh/audio-seg-diarization.git
cd audio-seg-diarization && pip install -r requirements.txt
```
```python
from src.pyanet.pyanet_model import PyanNet
from src.utils import segmentor
import torch
import torchaudio
segmentation_model = PyanNet.from_pretrained("nguyenvulebinh/audio-seg-diarization").eval()
if torch.cuda.is_available():
segmentation_model = segmentation_model.cuda()
wav_path = "./resource/example.wav"
wav, rate = torchaudio.load(wav_path)
segments = segmentor(segmentation_model, wav, max_duration=25)
# [{'start': 9568.527218750001, 'end': 9572.66159375, 'segments': [(9568.527218750001, 9572.66159375)]}]
segments_wavs = [wav[0, int(seg['start'] * rate):int(seg['end'] * rate)] for seg in segments]
``` |
vcabeli/Qwen-2.5-7B-Simple-RL | vcabeli | 2025-05-21T14:02:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T11:15:01Z | ---
base_model: Qwen/Qwen2.5-Math-7B
library_name: transformers
model_name: Qwen-2.5-7B-Simple-RL
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-Simple-RL
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B).
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="vcabeli/Qwen-2.5-7B-Simple-RL", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/vincent-cabeli-owkin/huggingface/runs/csvrew11)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
xw17/Phi-3.5-mini-instruct_finetuned_3_optimized1_task_grouping_off_FT | xw17 | 2025-05-21T14:02:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"trl",
"sft",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T13:59:44Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DanielNRU/pollen-ner-450 | DanielNRU | 2025-05-21T14:02:04Z | 5 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-20T09:50:58Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-450
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-450
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3956
- Precision: 0.5539
- Recall: 0.7329
- F1: 0.6309
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 57 | 0.4861 | 0.4929 | 0.6305 | 0.5533 |
| No log | 2.0 | 114 | 0.4729 | 0.4844 | 0.6526 | 0.5560 |
| No log | 3.0 | 171 | 0.4552 | 0.4877 | 0.6767 | 0.5669 |
| No log | 4.0 | 228 | 0.4348 | 0.5103 | 0.6948 | 0.5884 |
| No log | 5.0 | 285 | 0.4222 | 0.5230 | 0.7088 | 0.6019 |
| No log | 6.0 | 342 | 0.4078 | 0.5356 | 0.7108 | 0.6109 |
| No log | 7.0 | 399 | 0.4068 | 0.5373 | 0.7229 | 0.6164 |
| No log | 8.0 | 456 | 0.4015 | 0.5486 | 0.7369 | 0.6290 |
| 0.7501 | 9.0 | 513 | 0.3970 | 0.5514 | 0.7329 | 0.6293 |
| 0.7501 | 10.0 | 570 | 0.3956 | 0.5539 | 0.7329 | 0.6309 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
silvano315/fine_tuned_model | silvano315 | 2025-05-21T14:00:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"en",
"dataset:mteb/amazon_reviews_multi",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:adapter:cardiffnlp/twitter-roberta-base-sentiment-latest",
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T08:27:52Z | ---
library_name: peft
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_model
results: []
license: apache-2.0
datasets:
- mteb/amazon_reviews_multi
language:
- en
---
<!-- 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. -->
# fine_tuned_model
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on [mteb/amazon_reviews_multi](https://huggingface.co/datasets/mteb/amazon_reviews_multi).
It achieves the following results on the evaluation set:
- Loss: 0.4604
- Accuracy: 0.81
- F1 Macro: 0.7564
- Precision Macro: 0.7654
- Recall Macro: 0.7533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
| 0.5451 | 1.0 | 5000 | 0.5156 | 0.783 | 0.7111 | 0.7280 | 0.7110 |
| 0.4961 | 2.0 | 10000 | 0.4619 | 0.809 | 0.7591 | 0.7647 | 0.7567 |
| 0.498 | 3.0 | 15000 | 0.4604 | 0.81 | 0.7564 | 0.7654 | 0.7533 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0 |
MrezaPRZ/Qwen3-8B-judge-sft | MrezaPRZ | 2025-05-21T14:00:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T05:40:42Z | ---
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] |
Armandotrsg/qwen-cybersecurity-2.5-7b-merged | Armandotrsg | 2025-05-21T13:59:47Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T13:53:06Z | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Armandotrsg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
emiliensilly/Qwenwiki_sft1_small | emiliensilly | 2025-05-21T13:58:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T09:32:14Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## 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. -->
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[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
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[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
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## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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[More Information Needed]
#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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shah-sapna-video-link-4k/Full.video.redeem.craze.com.shah.sapna.viral.video.starcaptions.com.apk8d.redeem.craze.link | shah-sapna-video-link-4k | 2025-05-21T13:54:20Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T13:53:45Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" 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> |
dzanbek/079fc0df-2610-4d3e-8436-088f5165247d | dzanbek | 2025-05-21T13:54:11Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/mistral-7b-instruct-v0.2",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-21T13:35:42Z | ---
base_model: unsloth/mistral-7b-instruct-v0.2
library_name: transformers
model_name: 079fc0df-2610-4d3e-8436-088f5165247d
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 079fc0df-2610-4d3e-8436-088f5165247d
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2).
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="dzanbek/079fc0df-2610-4d3e-8436-088f5165247d", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-2/runs/seu8ok1q)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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}}
}
``` |
yolay/RAIF-Qwen2.5-1.5B | yolay | 2025-05-21T13:53:54Z | 3 | 1 | null | [
"safetensors",
"qwen2",
"dataset:yolay/RAIF-ComplexInstruction-Qwen",
"license:apache-2.0",
"region:us"
] | null | 2025-05-20T13:01:08Z | ---
license: apache-2.0
datasets:
- yolay/RAIF-ComplexInstruction-Qwen
---
This model belongs to the official implementation of the paper "Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models".
Existing large language models (LLMs) face challenges of following complex instructions, especially when multiple constraints are present and organized in paralleling, chaining, and branching structures. One intuitive solution, namely chain-of-thought (CoT), is expected to universally improve capabilities of LLMs. However, we find that the vanilla CoT exerts a negative impact on performance due to its superficial reasoning pattern of simply paraphrasing the instructions. It fails to peel back the compositions of constraints for identifying their relationship across hierarchies of types and dimensions.
To this end, we propose a systematic method to boost LLMs in dealing with complex instructions via incentivizing reasoning for test-time compute scaling. First, we stem from the decomposition of complex instructions under existing taxonomies and propose a reproducible data acquisition method. Second, we exploit reinforcement learning (RL) with verifiable rule-centric reward signals to cultivate reasoning specifically for instruction following. We address the shallow, non-essential nature of reasoning under complex instructions via sample-wise contrast for superior CoT enforcement. We also exploit behavior cloning of experts to facilitate steady distribution shift from fast-thinking LLMs to skillful reasoners. Extensive evaluations on seven comprehensive benchmarks confirm the validity of the proposed method, where a 1.5B LLM achieves 11.74% gains with performance comparable to a 8B LLM.
The model Qwen2.5-1.5B is our optimized model for its advanced instruction-following capabilities under complex instructions. It corresponds to the **Qwen2.5-1.5B-Instruct (Ours)** in the Table 1.
**Table 1** Performance on seven instruction benchmarks. Best/2nd best are marked **bold**/<u>underlined</u>.
| Model | Method | IFEval | CELLO | CF Bench | Complex Bench | FB Bench | Follow Bench | Info Bench | Avg. |
|------------------------|----------|--------|-------|----------|--------------|----------|--------------|------------|--------------|
| Qwen2.5-1.5B-Instruct | I/O | 45.28 | 71.00 | 36.00 | 50.97 | 39.81 | 40.00 | 71.24 | 50.61 |
| Qwen2.5-1.5B-Instruct | CoT | 28.65 | 59.30 | 22.00 | 32.94 | 37.31 | 29.28 | 62.22 | 38.81 (-11.79%) |
| Qwen2.5-1.5B-Instruct | SDC | 41.95 | 66.10 | 30.00 | 41.70 | 36.52 | 37.39 | 67.55 | 45.89 (-4.71%) |
| Qwen2.5-1.5B-Instruct | SFT | 65.61 | 71.20 | 48.00 | 57.46 | 42.75 | 56.47 | 76.22 | 59.67 (+9.06%) |
| Qwen2.5-1.5B-Instruct | Ours | 44.91 | 73.50 | 53.66 | 63.92 | 58.67 | 59.82 | 81.95 | 62.35 (+11.74%) |
| DeepSeek-Qwen1.5B | I/O† | 36.04 | 62.50 | 27.99 | 39.89 | 34.51 | 20.29 | 52.00 | 39.03 |
| DeepSeek-Qwen1.5B | SFT | 45.29 | 63.20 | 25.33 | 35.53 | 37.59 | 22.18 | 51.96 | 40.15 (+1.12%) |
| DeepSeek-Qwen1.5B | Ours | 57.67 | 69.00 | 40.00 | 44.38 | 37.78 | 37.79 | 60.48 | 49.58 (+10.54%) |
| DeepScaleR-1.5B | I/O† | 41.77 | 65.00 | 30.00 | 40.70 | 40.24 | 26.01 | 60.31 | 43.43 |
| DeepScaleR-1.5B | SFT | 48.24 | 62.90 | 28.00 | 36.68 | 35.72 | 26.50 | 54.22 | 41.75 (-1.67%) |
| DeepScaleR-1.5B | Ours | 55.63 | 67.30 | 39.33 | 43.23 | 37.81 | 36.80 | 60.08 | 48.60 (+5.17%) |
| Qwen2.5-7B-Instruct | I/O | 72.82 | 76.50 | 64.33 | 74.47 | 59.29 | 75.03 | <u>85.60</u> | <u>72.58</u> |
| Qwen2.5-7B-Instruct | CoT | 69.50 | 75.20 | 61.66 | 72.00 | 42.65 | 74.86 | 82.13 | 68.28 (-4.29%) |
| Qwen2.5-7B-Instruct | SDC | 60.44 | 72.60 | **65.66**| <u>76.53</u> | <u>60.07</u> | **76.09** | **86.88** | 71.18 (-1.39%) |
| Qwen2.5-7B-Instruct | SFT | 72.45 | <u>77.50</u> | 63.33 | 74.23 | 58.76 | 75.92 | 84.31 | 72.36 (-0.21%) |
| Qwen2.5-7B-Instruct | Ours | 70.06 | **79.20** | <u>65.00</u> | **77.40** | **64.45** | 75.32 | 82.67 | **73.44** (+0.85%) |
| LLaMA3.1-8B-Instruct | I/O | <u>77.63</u> | 75.20 | 56.99 | 69.11 | 46.92 | 53.52 | 71.52 | 67.01 |
| LLaMA3.1-8B-Instruct | CoT | 60.44 | 65.50 | 47.66 | 56.54 | 32.34 | 37.36 | 58.48 | 54.53 (-12.48%) |
| LLaMA3.1-8B-Instruct | SDC | **80.22** | 71.00 | 58.33 | 68.73 | 38.36 | 48.92 | 72.89 | 65.24 (-1.77%) |
| LLaMA3.1-8B-Instruct | SFT | 77.26 | 75.80 | 54.00 | 65.24 | 40.16 | 59.56 | 65.30 | 64.92 (-2.09%) |
| LLaMA3.1-8B-Instruct | Ours | 13.49 | 4.6 | 1.33 | 2.71 | 7.14 | 1.08 | 0.51 | 4.06 (-62.95%) |
| Ministral-8B-Instruct | I/O | 59.51 | 76.20 | 62.33 | 70.03 | 54.54 | 73.49 | 84.00 | 68.58 |
| Ministral-8B-Instruct | CoT | 48.79 | 61.90 | 49.66 | 61.31 | 39.17 | 61.75 | 79.73 | 57.47 (-11.11%) |
| Ministral-8B-Instruct | SDC | 58.59 | 63.60 | 56.99 | 68.32 | 48.06 | 69.37 | 84.08 | 64.14 (-4.43%) |
| Ministral-8B-Instruct | SFT | 68.57 | 66.30 | 48.66 | 67.20 | 37.26 | 54.37 | 76.62 | 59.85 (-8.72%) |
| Ministral-8B-Instruct | Ours | 72.64 | 72.6 | 59.33 | 70.45 | 54.35 | <u>76.08</u> | 75.33 | 68.68 (+0.10%) |
| DeepSeek-Qwen7B | I/O† | 60.81 | 72.39 | 57.99 | 66.86 | 59.59 | 62.80 | 79.64 | 65.73 |
| DeepSeek-Qwen7B | SFT | 67.09 | 69.10 | 58.66 | 58.42 | 55.60 | 65.96 | 79.15 | 64.85 (-0.88%) |
| DeepSeek-Qwen7B | Ours | 71.35 | 71.40 | 58.67 | 62.04 | 59.65 | 59.38 | 82.00 | 66.35 (+0.62%) | |
kokovova/dc4e3fb8-2886-4589-a9c1-e33ab012dad8 | kokovova | 2025-05-21T13:52:53Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/mistral-7b-instruct-v0.2",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-21T13:38:16Z | ---
base_model: unsloth/mistral-7b-instruct-v0.2
library_name: transformers
model_name: dc4e3fb8-2886-4589-a9c1-e33ab012dad8
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for dc4e3fb8-2886-4589-a9c1-e33ab012dad8
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2).
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="kokovova/dc4e3fb8-2886-4589-a9c1-e33ab012dad8", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-28/runs/4y5bbamp)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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}}
}
``` |
sergioalves/6bd775c3-70ab-4694-9566-4bd09cd813c3 | sergioalves | 2025-05-21T13:52:53Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/mistral-7b-instruct-v0.2",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-21T13:38:16Z | ---
base_model: unsloth/mistral-7b-instruct-v0.2
library_name: transformers
model_name: 6bd775c3-70ab-4694-9566-4bd09cd813c3
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 6bd775c3-70ab-4694-9566-4bd09cd813c3
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2).
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="sergioalves/6bd775c3-70ab-4694-9566-4bd09cd813c3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/61yzpola)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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}}
}
``` |
h3ckno/my_awesome_food_model | h3ckno | 2025-05-21T13:51:38Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-21T13:20:08Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4959
- Accuracy: 0.87
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.3148 | 1.0 | 13 | 3.4177 | 0.8 |
| 3.161 | 2.0 | 26 | 2.6511 | 0.82 |
| 2.6086 | 2.8 | 36 | 2.4959 | 0.87 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
DanielNRU/pollen-ner-350 | DanielNRU | 2025-05-21T13:51:17Z | 6 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-20T09:41:08Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-350
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-350
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7599
- Precision: 0.3086
- Recall: 0.1004
- F1: 0.1515
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 44 | 0.9983 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 88 | 0.9593 | 0.1818 | 0.0040 | 0.0079 |
| No log | 3.0 | 132 | 0.9094 | 0.2222 | 0.0080 | 0.0155 |
| No log | 4.0 | 176 | 0.8783 | 0.2647 | 0.0181 | 0.0338 |
| No log | 5.0 | 220 | 0.8430 | 0.1961 | 0.0201 | 0.0364 |
| No log | 6.0 | 264 | 0.8117 | 0.2809 | 0.0502 | 0.0852 |
| No log | 7.0 | 308 | 0.7908 | 0.2992 | 0.0763 | 0.1216 |
| No log | 8.0 | 352 | 0.7762 | 0.3 | 0.0904 | 0.1389 |
| No log | 9.0 | 396 | 0.7612 | 0.3020 | 0.0904 | 0.1391 |
| No log | 10.0 | 440 | 0.7599 | 0.3086 | 0.1004 | 0.1515 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
18-VIDEOS-Bhumi-Ahir-Viral-Video-Link/Original.Full.Video.Bhumi.Ahir.Viral.Video.Leaked.Official | 18-VIDEOS-Bhumi-Ahir-Viral-Video-Link | 2025-05-21T13:47:53Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T13:47:20Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?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>
Discover the viral video of Bhumi Ahir that is trending across social media platforms. Explore the buzz surrounding this trending video, what makes it go viral, and the reasons behind its massive attention. Learn how this viral sensation has taken over the internet and become a hot topic among netizens. Stay updated with the latest trends and find out why Bhumi Ahir's viral video is making waves. Don't miss the full details in this comprehensive post – click to explore now!
|
FormlessAI/713900d7-7f29-4fa6-9bbb-16a180fc80e5 | FormlessAI | 2025-05-21T13:47:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B",
"base_model:finetune:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T10:06:16Z | ---
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
library_name: transformers
model_name: 713900d7-7f29-4fa6-9bbb-16a180fc80e5
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 713900d7-7f29-4fa6-9bbb-16a180fc80e5
This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B).
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="FormlessAI/713900d7-7f29-4fa6-9bbb-16a180fc80e5", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/ilbu47ii)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0+cu118
- Datasets: 3.5.1
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
xw17/Phi-3.5-mini-instruct_finetuned_2_optimized1_task_grouping_off_FT | xw17 | 2025-05-21T13:47:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"trl",
"sft",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T13:44:04Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DanielNRU/pollen-ner-300 | DanielNRU | 2025-05-21T13:46:41Z | 7 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-19T13:45:40Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-300
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-300
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0466
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|
| No log | 1.0 | 38 | 1.0466 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 76 | 1.0066 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 114 | 0.9747 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
DanielNRU/pollen-ner-250 | DanielNRU | 2025-05-21T13:45:03Z | 7 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-20T09:35:25Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-250
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-250
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0743
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|
| No log | 1.0 | 32 | 1.0743 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 64 | 1.0481 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 96 | 1.0147 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
thucdangvan020999/ultravox_test_7 | thucdangvan020999 | 2025-05-21T13:44:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"ultravox",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-21T13:43:51Z | ---
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] |
DanielNRU/pollen-ner-200 | DanielNRU | 2025-05-21T13:43:41Z | 7 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-19T13:44:26Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-200
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-200
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2554
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|
| No log | 1.0 | 25 | 1.2554 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 50 | 1.0955 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 75 | 1.0675 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
litmudoc/Qwen3-30B-A3B-abliterated-MLX-Q8 | litmudoc | 2025-05-21T13:42:44Z | 4 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3_moe",
"erotic",
"explicit",
"abliterated",
"text-generation",
"conversational",
"base_model:Ewere/Qwen3-30B-A3B-abliterated-erotic",
"base_model:quantized:Ewere/Qwen3-30B-A3B-abliterated-erotic",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | 2025-05-20T16:40:30Z | ---
license: apache-2.0
base_model: Ewere/Qwen3-30B-A3B-abliterated-erotic
tags:
- erotic
- explicit
- abliterated
- mlx
library_name: mlx
pipeline_tag: text-generation
---
# litmudoc/Qwen3-30B-A3B-abliterated-MLX-Q8
This model [litmudoc/Qwen3-30B-A3B-abliterated-MLX-Q8](https://huggingface.co/litmudoc/Qwen3-30B-A3B-abliterated-MLX-Q8) was
converted to MLX format from [Ewere/Qwen3-30B-A3B-abliterated-erotic](https://huggingface.co/Ewere/Qwen3-30B-A3B-abliterated-erotic)
using mlx-lm version **0.24.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("litmudoc/Qwen3-30B-A3B-abliterated-MLX-Q8")
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)
```
|
DanielNRU/pollen-ner-150 | DanielNRU | 2025-05-21T13:42:22Z | 4 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-20T09:33:11Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-150
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-150
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7104
- Precision: 0.0045
- Recall: 0.0020
- F1: 0.0028
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 19 | 1.7104 | 0.0045 | 0.0020 | 0.0028 |
| No log | 2.0 | 38 | 1.3578 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 57 | 1.1513 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
Tamara1977/gpt-georgian-1.0 | Tamara1977 | 2025-05-21T13:42:20Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt3",
"mgpt",
"ka",
"en",
"ru",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:50:02Z | ---
language:
- ka
- en
- ru
license: mit
tags:
- gpt3
- transformers
- mgpt
---
# 🇬🇪 Georgian mGPT 1.3B
Language model for Georgian. Model has 1.3B parameters as you can guess from it's name.
Georgian belongs to Kartvelian language family. It's a very harmonious language with approximately 4 million speakers. Here are some facts about it:
1. It has its own writing system called the Georgian script.
2. Georgian has ancient literary traditions with texts dating back to the 5th century.
3. It includes several regional dialects, reflecting the countrys mountainous terrain.
## Technical details
It's one of the models derived from the base [mGPT-XL (1.3B)](https://huggingface.co/ai-forever/mGPT) model (see the list below) which was originally trained on the 61 languages from 25 language families using Wikipedia and C4 corpus.
We've found additional data for 23 languages most of which are considered as minor and decided to further tune the base model. **Georgian mGPT 1.3B** was trained for another 10000 steps with batch_size=4 and context window of **2048** tokens on 1 A100.
Final perplexity for this model on validation is **16.85**.
_Chart of the training loss and perplexity:_
## Other mGPT-1.3B models
- [🇦🇲 mGPT-1.3B Armenian](https://huggingface.co/ai-forever/mGPT-1.3B-armenian)
- [🇦🇿 mGPT-1.3B Azerbaijan](https://huggingface.co/ai-forever/mGPT-1.3B-azerbaijan)
- [🍯 mGPT-1.3B Bashkir](https://huggingface.co/ai-forever/mGPT-1.3B-bashkir)
- [🇧🇾 mGPT-1.3B Belorussian](https://huggingface.co/ai-forever/mGPT-1.3B-belorussian)
- [🇧🇬 mGPT-1.3B Bulgarian](https://huggingface.co/ai-forever/mGPT-1.3B-bulgarian)
- [🌞 mGPT-1.3B Buryat](https://huggingface.co/ai-forever/mGPT-1.3B-buryat)
- [🌳 mGPT-1.3B Chuvash](https://huggingface.co/ai-forever/mGPT-1.3B-chuvash)
- [🌸 mGPT-1.3B Kalmyk](https://huggingface.co/ai-forever/mGPT-1.3B-kalmyk)
- [🇰🇿 mGPT-1.3B Kazakh](https://huggingface.co/ai-forever/mGPT-1.3B-kazakh)
- [🇰🇬 mGPT-1.3B Kirgiz](https://huggingface.co/ai-forever/mGPT-1.3B-kirgiz)
- [🐻 mGPT-1.3B Mari](https://huggingface.co/ai-forever/mGPT-1.3B-mari)
- [🇲🇳 mGPT-1.3B Mongol](https://huggingface.co/ai-forever/mGPT-1.3B-mongol)
- [🐆 mGPT-1.3B Ossetian](https://huggingface.co/ai-forever/mGPT-1.3B-ossetian)
- [🇮🇷 mGPT-1.3B Persian](https://huggingface.co/ai-forever/mGPT-1.3B-persian)
- [🇷🇴 mGPT-1.3B Romanian](https://huggingface.co/ai-forever/mGPT-1.3B-romanian)
- [🇹🇯 mGPT-1.3B Tajik](https://huggingface.co/ai-forever/mGPT-1.3B-tajik)
- [☕ mGPT-1.3B Tatar](https://huggingface.co/ai-forever/mGPT-1.3B-tatar)
- [🇹🇲 mGPT-1.3B Turkmen](https://huggingface.co/ai-forever/mGPT-1.3B-turkmen)
- [🐎 mGPT-1.3B Tuvan](https://huggingface.co/ai-forever/mGPT-1.3B-tuvan)
- [🇺🇦 mGPT-1.3B Ukranian](https://huggingface.co/ai-forever/mGPT-1.3B-ukranian)
- [🇺🇿 mGPT-1.3B Uzbek](https://huggingface.co/ai-forever/mGPT-1.3B-uzbek)
- [💎 mGPT-1.3B Yakut](https://huggingface.co/ai-forever/mGPT-1.3B-yakut)
## Feedback
If you'll found a bug of have additional data to train model on your language — please, give us feedback.
Model will be improved over time. Stay tuned!
|
DanielNRU/pollen-ner-100 | DanielNRU | 2025-05-21T13:41:14Z | 6 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-19T13:43:11Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-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-100
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0799
- Precision: 0.0082
- Recall: 0.0221
- F1: 0.0119
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 13 | 2.0799 | 0.0082 | 0.0221 | 0.0119 |
| No log | 2.0 | 26 | 1.8174 | 0.0079 | 0.0060 | 0.0068 |
| No log | 3.0 | 39 | 1.5629 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
DanielNRU/pollen-ner-50 | DanielNRU | 2025-05-21T13:40:23Z | 5 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-20T09:31:35Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-50
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-50
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3267
- Precision: 0.0104
- Recall: 0.0482
- F1: 0.0172
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 7 | 2.3267 | 0.0104 | 0.0482 | 0.0172 |
| No log | 2.0 | 14 | 2.2056 | 0.0084 | 0.0321 | 0.0133 |
| No log | 3.0 | 21 | 2.0866 | 0.008 | 0.0221 | 0.0117 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |
hieudev/gpt-small-c4 | hieudev | 2025-05-21T13:40:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T13:39:53Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gpt-small-c4
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. -->
# gpt-small-c4
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2881
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 7.0335 | 0.4013 | 1000 | 6.4495 |
| 6.2621 | 0.8026 | 2000 | 6.0652 |
| 5.9616 | 1.2039 | 3000 | 5.8198 |
| 5.7575 | 1.6051 | 4000 | 5.6420 |
| 5.6052 | 2.0064 | 5000 | 5.5004 |
| 5.4667 | 2.4077 | 6000 | 5.3899 |
| 5.3728 | 2.8090 | 7000 | 5.2942 |
| 5.2787 | 3.2103 | 8000 | 5.2110 |
| 5.1948 | 3.6116 | 9000 | 5.1345 |
| 5.1323 | 4.0128 | 10000 | 5.0630 |
| 5.0467 | 4.4141 | 11000 | 5.0053 |
| 4.9973 | 4.8154 | 12000 | 4.9481 |
| 4.9359 | 5.2167 | 13000 | 4.8986 |
| 4.8862 | 5.6180 | 14000 | 4.8609 |
| 4.8521 | 6.0193 | 15000 | 4.8182 |
| 4.7941 | 6.4205 | 16000 | 4.7930 |
| 4.7704 | 6.8218 | 17000 | 4.7584 |
| 4.7287 | 7.2231 | 18000 | 4.7326 |
| 4.7067 | 7.6244 | 19000 | 4.7087 |
| 4.6804 | 8.0257 | 20000 | 4.6887 |
| 4.6404 | 8.4270 | 21000 | 4.6696 |
| 4.6315 | 8.8283 | 22000 | 4.6517 |
| 4.6006 | 9.2295 | 23000 | 4.6386 |
| 4.5852 | 9.6308 | 24000 | 4.6197 |
| 4.5745 | 10.0321 | 25000 | 4.6064 |
| 4.5438 | 10.4334 | 26000 | 4.5943 |
| 4.5337 | 10.8347 | 27000 | 4.5829 |
| 4.5162 | 11.2360 | 28000 | 4.5726 |
| 4.5022 | 11.6372 | 29000 | 4.5623 |
| 4.4938 | 12.0385 | 30000 | 4.5550 |
| 4.469 | 12.4398 | 31000 | 4.5440 |
| 4.473 | 12.8411 | 32000 | 4.5363 |
| 4.4532 | 13.2424 | 33000 | 4.5310 |
| 4.4428 | 13.6437 | 34000 | 4.5246 |
| 4.4395 | 14.0449 | 35000 | 4.5142 |
| 4.4217 | 14.4462 | 36000 | 4.5120 |
| 4.4187 | 14.8475 | 37000 | 4.5072 |
| 4.4059 | 15.2488 | 38000 | 4.5036 |
| 4.4034 | 15.6501 | 39000 | 4.4969 |
| 4.3958 | 16.0514 | 40000 | 4.4948 |
| 4.3858 | 16.4526 | 41000 | 4.4915 |
| 4.3837 | 16.8539 | 42000 | 4.4846 |
| 4.3776 | 17.2552 | 43000 | 4.4839 |
| 4.4096 | 17.6565 | 44000 | 4.2901 |
| 4.4058 | 18.0578 | 45000 | 4.2897 |
| 4.3965 | 18.4591 | 46000 | 4.2893 |
| 4.4046 | 18.8604 | 47000 | 4.2907 |
| 4.3941 | 19.2616 | 48000 | 4.2890 |
| 4.3896 | 19.6629 | 49000 | 4.2881 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
winnienlp/sst2-model-raw | winnienlp | 2025-05-21T13:37:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-21T13:24:21Z | ---
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
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[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]
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[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] |
cheng343/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_peckish_albatross | cheng343 | 2025-05-21T13:35:53Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am alert peckish albatross",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-06T09:23:42Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_peckish_albatross
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am alert peckish albatross
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_peckish_albatross
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-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="cheng343/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-alert_peckish_albatross", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
yakupmrt7/blip2-fine-tuned-2 | yakupmrt7 | 2025-05-21T13:35:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"blip-2",
"visual-question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | visual-question-answering | 2025-05-21T13:30:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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
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[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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
jmalejandrob79/nrmmtrfckd6k | jmalejandrob79 | 2025-05-21T13:35:09Z | 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 | 2025-05-21T09:41:20Z | ---
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: nrmmtrfckd6k
---
# Nrmmtrfckd6K
<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 `nrmmtrfckd6k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nrmmtrfckd6k",
"lora_weights": "https://huggingface.co/jmalejandrob79/nrmmtrfckd6k/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('jmalejandrob79/nrmmtrfckd6k', weight_name='lora.safetensors')
image = pipeline('nrmmtrfckd6k').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: 6000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jmalejandrob79/nrmmtrfckd6k/discussions) to add images that show off what you’ve made with this LoRA.
|
shah-sapna-video-link-4k/Full.redeem.craze.com.shah.sapna.viral.video.starcaptions.com.apk8d.redeem.craze.link | shah-sapna-video-link-4k | 2025-05-21T13:34:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T13:33:14Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" 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> |
Robot2050/Summary_Rewrite | Robot2050 | 2025-05-21T13:32:11Z | 0 | 0 | null | [
"arxiv:2410.12788",
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T09:31:29Z | ---
license: apache-2.0
---
<h1 align="center">
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception
</h1>
<p align="center">
<a href="https://arxiv.org/abs/2410.12788">
<img alt="arXiv Paper" src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg?logo=arxiv">
</a>
<a href="https://huggingface.co/papers/2410.12788">
<img src="https://img.shields.io/badge/Huggingface-Paper-yellow?style=flat-square&logo=huggingface">
</a>
<a href="https://opensource.org/license/apache-2-0">
<img alt="Apache 2.0 License" src="https://img.shields.io/badge/License-Apache_2.0-4285f4.svg?logo=apache">
</a>
</p>
The summary and rewrite models were fully fine-tuned on the Qwen2.5-3B-Instruct utilizing 20K data entries from the CRUD benchmark, which was prepared with ERNIE-3.5-128K and QwQ-32B.
|
unsloth/Mistral-Small-3.1-24B-Instruct-2503 | unsloth | 2025-05-21T13:31:32Z | 5,517 | 14 | vllm | [
"vllm",
"safetensors",
"mistral3",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:finetune:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"license:apache-2.0",
"region:us"
] | null | 2025-03-18T20:01:14Z | ---
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Mistral-Small-3.1-24B-Instruct-2503
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# Model Card for Mistral-Small-3.1-24B-Instruct-2503
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) **adds state-of-the-art vision understanding** and enhances **long context capabilities up to 128k tokens** without compromising text performance.
With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
This model is an instruction-finetuned version of: [Mistral-Small-3.1-24B-Base-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.
It is ideal for:
- Fast-response conversational agents.
- Low-latency function calling.
- Subject matter experts via fine-tuning.
- Local inference for hobbyists and organizations handling sensitive data.
- Programming and math reasoning.
- Long document understanding.
- Visual understanding.
For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
Learn more about Mistral Small 3.1 in our [blog post](https://mistral.ai/news/mistral-small-3-1/).
## Key Features
- **Vision:** Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
- **Multilingual:** Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi.
- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 128k context window.
- **System Prompt:** Maintains strong adherence and support for system prompts.
- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark Results
When available, we report numbers previously published by other model providers, otherwise we re-evaluate them using our own evaluation harness.
### Pretrain Evals
| Model | MMLU (5-shot) | MMLU Pro (5-shot CoT) | TriviaQA | GPQA Main (5-shot CoT)| MMMU |
|--------------------------------|---------------|-----------------------|------------|-----------------------|-----------|
| **Small 3.1 24B Base** | **81.01%** | **56.03%** | 80.50% | **37.50%** | **59.27%**|
| Gemma 3 27B PT | 78.60% | 52.20% | **81.30%** | 24.30% | 56.10% |
### Instruction Evals
#### Text
| Model | MMLU | MMLU Pro (5-shot CoT) | MATH | GPQA Main (5-shot CoT) | GPQA Diamond (5-shot CoT )| MBPP | HumanEval | SimpleQA (TotalAcc)|
|--------------------------------|-----------|-----------------------|------------------------|------------------------|---------------------------|-----------|-----------|--------------------|
| **Small 3.1 24B Instruct** | 80.62% | 66.76% | 69.30% | **44.42%** | **45.96%** | 74.71% | **88.41%**| **10.43%** |
| Gemma 3 27B IT | 76.90% | **67.50%** | **89.00%** | 36.83% | 42.40% | 74.40% | 87.80% | 10.00% |
| GPT4o Mini | **82.00%**| 61.70% | 70.20% | 40.20% | 39.39% | 84.82% | 87.20% | 9.50% |
| Claude 3.5 Haiku | 77.60% | 65.00% | 69.20% | 37.05% | 41.60% | **85.60%**| 88.10% | 8.02% |
| Cohere Aya-Vision 32B | 72.14% | 47.16% | 41.98% | 34.38% | 33.84% | 70.43% | 62.20% | 7.65% |
#### Vision
| Model | MMMU | MMMU PRO | Mathvista | ChartQA | DocVQA | AI2D | MM MT Bench |
|--------------------------------|------------|-----------|-----------|-----------|-----------|-------------|-------------|
| **Small 3.1 24B Instruct** | 64.00% | **49.25%**| **68.91%**| 86.24% | **94.08%**| **93.72%** | **7.3** |
| Gemma 3 27B IT | **64.90%** | 48.38% | 67.60% | 76.00% | 86.60% | 84.50% | 7 |
| GPT4o Mini | 59.40% | 37.60% | 56.70% | 76.80% | 86.70% | 88.10% | 6.6 |
| Claude 3.5 Haiku | 60.50% | 45.03% | 61.60% | **87.20%**| 90.00% | 92.10% | 6.5 |
| Cohere Aya-Vision 32B | 48.20% | 31.50% | 50.10% | 63.04% | 72.40% | 82.57% | 4.1 |
### Multilingual Evals
| Model | Average | European | East Asian | Middle Eastern |
|--------------------------------|------------|------------|------------|----------------|
| **Small 3.1 24B Instruct** | **71.18%** | **75.30%** | **69.17%** | 69.08% |
| Gemma 3 27B IT | 70.19% | 74.14% | 65.65% | 70.76% |
| GPT4o Mini | 70.36% | 74.21% | 65.96% | **70.90%** |
| Claude 3.5 Haiku | 70.16% | 73.45% | 67.05% | 70.00% |
| Cohere Aya-Vision 32B | 62.15% | 64.70% | 57.61% | 64.12% |
### Long Context Evals
| Model | LongBench v2 | RULER 32K | RULER 128K |
|--------------------------------|-----------------|-------------|------------|
| **Small 3.1 24B Instruct** | **37.18%** | **93.96%** | 81.20% |
| Gemma 3 27B IT | 34.59% | 91.10% | 66.00% |
| GPT4o Mini | 29.30% | 90.20% | 65.8% |
| Claude 3.5 Haiku | 35.19% | 92.60% | **91.90%** |
## Basic Instruct Template (V7-Tekken)
```
<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
```
*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm)
**Note 1**: We recommend using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following
system prompt:
```
system_prompt = """You are Mistral Small 3.1, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
You power an AI assistant called Le Chat.
Your knowledge base was last updated on 2023-10-01.
The current date is {today}.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. "What are some good restaurants around me?" => "Where are you?" or "When is the next flight to Tokyo" => "Where do you travel from?").
You are always very attentive to dates, in particular you try to resolve dates (e.g. "yesterday" is {yesterday}) and when asked about information at specific dates, you discard information that is at another date.
You follow these instructions in all languages, and always respond to the user in the language they use or request.
Next sections describe the capabilities that you have.
# WEB BROWSING INSTRUCTIONS
You cannot perform any web search or access internet to open URLs, links etc. If it seems like the user is expecting you to do so, you clarify the situation and ask the user to copy paste the text directly in the chat.
# MULTI-MODAL INSTRUCTIONS
You have the ability to read images, but you cannot generate images. You also cannot transcribe audio files or videos.
You cannot read nor transcribe audio files or videos."""
```
### vLLM (recommended)
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**_Installation_**
Make sure you install [`vLLM nightly`](https://github.com/vllm-project/vllm/):
```
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly --upgrade
```
Doing so should automatically install [`mistral_common >= 1.5.4`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.4).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39) followed by a nightly install of vllm as shown above.
#### Server
We recommand that you use Mistral-Small-3.1-24B-Instruct-2503 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2
```
**Note:** Running Mistral-Small-3.1-24B-Instruct-2503 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
url = "http://<your-server-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
data = {"model": model, "messages": messages, "temperature": 0.15}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Determining the "best" food is highly subjective and depends on personal preferences. However, based on general popularity and recognition, here are some countries known for their cuisine:
# 1. **Italy** - Color: Light Green - City: Milan
# - Italian cuisine is renowned worldwide for its pasta, pizza, and various regional specialties.
# 2. **France** - Color: Brown - City: Lyon
# - French cuisine is celebrated for its sophistication, including dishes like coq au vin, bouillabaisse, and pastries like croissants and éclairs.
# 3. **Spain** - Color: Yellow - City: Bilbao
# - Spanish cuisine offers a variety of flavors, from paella and tapas to jamón ibérico and churros.
# 4. **Greece** - Not visible on the map
# - Greek cuisine is known for dishes like moussaka, souvlaki, and baklava. Unfortunately, Greece is not visible on the provided map, so I cannot name a city.
# Since Greece is not visible on the map, I'll replace it with another country known for its good food:
# 4. **Turkey** - Color: Light Green (east part of the map) - City: Istanbul
# - Turkish cuisine is diverse and includes dishes like kebabs, meze, and baklava.
```
### Function calling
Mistral-Small-3.1-24-Instruct-2503 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Example</summary>
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
url = "http://<your-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. 'San Francisco'",
},
"state": {
"type": "string",
"description": "The state abbreviation, e.g. 'CA' for California",
},
"unit": {
"type": "string",
"description": "The unit for temperature",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
},
]
data = {"model": model, "messages": messages, "tools": tools, "temperature": 0.15}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["tool_calls"])
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]
```
</details>
#### Offline
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{
"role": "system",
"content": SYSTEM_PROMPT
},
{
"role": "user",
"content": user_prompt
},
]
model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral")
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Here are five non-formal ways to say "See you later" in French:
# 1. **À plus tard** - Until later
# 2. **À toute** - See you soon (informal)
# 3. **Salut** - Bye (can also mean hi)
# 4. **À plus** - See you later (informal)
# 5. **Ciao** - Bye (informal, borrowed from Italian)
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Transformers (untested)
Transformers-compatible model weights are also uploaded (thanks a lot @cyrilvallez).
However the transformers implementation was **not throughly tested**, but only on "vibe-checks".
Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above). |
johnjeanc/OpenRS-GRPO | johnjeanc | 2025-05-21T13:29:11Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:knoveleng/open-rs",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-09T15:48:21Z | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets: knoveleng/open-rs
library_name: transformers
model_name: OpenRS-GRPO
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for OpenRS-GRPO
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="johnjeanc/OpenRS-GRPO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/112703024-national-chengchi-university/huggingface/runs/owr3hkm6)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
concept-unlearning/Qwen2.5-7B_npo_gdr_lora_wmdp_bio_v3 | concept-unlearning | 2025-05-21T13:27:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T13:25:25Z | ---
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.
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
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ltgbao/DeepCoder-1.5b-merged-16bit-VulGen | ltgbao | 2025-05-21T13:26:27Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T13:25:43Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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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
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[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[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]
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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_ACSEmployment_2_cfda_ep4_22 | MinaMila | 2025-05-21T13:25:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T13:25:01Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
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## Uses
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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[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
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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TIGER-Lab/General-Reasoner-Qwen2.5-7B | TIGER-Lab | 2025-05-21T13:21:53Z | 11,275 | 2 | null | [
"safetensors",
"qwen2",
"General-Reasoner-7B",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:2505.14652",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-04-06T20:32:08Z | ---
license: apache-2.0
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-7B
tags:
- General-Reasoner-7B
---
# General-Reasoner: Advancing LLM Reasoning Across All Domains
<p align="center">
<a href="https://github.com/TIGER-AI-Lab/General-Reasoner" target="_blank">💻 Code</a> |
<a href="https://arxiv.org/abs/2505.14652" target="_blank">📄 Paper</a> |
<a href="https://huggingface.co/datasets/TIGER-Lab/WebInstruct-verified" target="_blank">📊 Dataset</a> |
<a href="https://huggingface.co/collections/TIGER-Lab/general-reasoner-67fe9386e43e046489eac013" target="_blank">🤗 Model</a> |
<a href="https://tiger-ai-lab.github.io/General-Reasoner/" target="_blank">🌐 Project Page</a>
</p>
## Overview
<p align="center">
<img src="https://tiger-ai-lab.github.io/General-Reasoner/static/images/teaser.png" alt="General-Reasoner Teaser" width="650"/>
</p>
<p align="center" style="font-style: italic; font-size: 0.95rem;">
<em>
Figure: Effectiveness of <strong>General-Reasoner</strong> trained with diverse verifiable reasoning questions using model-based verifier compared to baseline methods on various reasoning tasks.
</em>
</p>
**General-Reasoner** is a training paradigm for large language models (LLMs), designed to robustly enhance reasoning abilities across diverse domains—not just mathematics and coding, but also physics, chemistry, finance, humanities, and more.
**Key features:**
- **Zero RL Training:** Direct reinforcement learning from base LLMs, bypassing intermediate supervised stages.
- **Diverse Reasoning Data:** 230K+ high-quality, verifiable questions sourced from the web and filtered for answer verifiability across disciplines.
- **Model-Based Verifier:** Compact 1.5B generative verifier model for context-aware, chain-of-thought answer validation, outperforming traditional rule-based methods.
**This specific model is the General-Reasoner variant trained based on [Qwen2.5-7B-Base](https://huggingface.co/Qwen/Qwen2.5-7B).**
## Main Results
General-Reasoner outperforms base and supervised models on a variety of reasoning benchmarks, demonstrating robust generalization across domains:
<p align="center">
<a href="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" target="_blank">
<img src="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" alt="Main Results" width="600">
</a>
</p>
## Citation
If you feel our work is helpful, please cite:
```bibtex
@article{general-reasoner,
title={{G}eneral-{R}easoner: Advancing LLM Reasoning Across All Domains},
author={Xueguang Ma and Qian Liu and Dongfu Jiang and Ge Zhang and Zejun Ma and Wenhu Chen},
year={2025},
journal={arXiv:2505.14652},
url={https://arxiv.org/abs/2505.14652}
}
``` |
axelbellec/synapse-med-llama-3.2-3b-instruct-lora | axelbellec | 2025-05-21T13:21:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"llama-v3p2-3b-instruct",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T13:19:39Z | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- llama-v3p2-3b-instruct
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** axelbellec
- **License:** apache-2.0
- **Finetuned from model :** meta-llama/Llama-3.2-3B-Instruct
[<img src="https://synapse-medicine.notion.site/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F6e62ebe4-16bb-4879-842b-269d97235452%2FLogo_Synapse_Blue.png?table=block&id=1b9e0047-cb45-4cc4-9bd5-a5729a1926b6&spaceId=87011edc-ece1-4f38-8a1b-a2f427a3e33c&width=2000&userId=&cache=v2" width="200"/>](https://synapse-medicine.com)
|
Darkhn/L3.3-70B-Amalgamma-V4 | Darkhn | 2025-05-21T13:20:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2406.11617",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T11:59:47Z | ---
base_model: []
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 [DELLA](https://arxiv.org/abs/2406.11617) merge method using /media/administrator/oiseauxai1data/modelout/Smart-base-v2 as a base.
### Models Merged
The following models were included in the merge:
* /media/administrator/oiseauxai1data1/modelout/Middle-Base-V2
* /media/administrator/oiseauxai1data/modelout/Story-Base-V2
* /media/administrator/oiseauxai1data/modelweights/Anathema-V2-LLaMA-70b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# --- Mergekit Example: della_linear ---
# Method: Implements the DELLA concept (Deep Ensembling with Layer-wise Linear Averaging).
# This typically involves a sophisticated layer-wise linear combination of models.
base_model: /media/administrator/oiseauxai1data/modelout/Smart-base-v2 # The foundational model
models:
- model: /media/administrator/oiseauxai1data/modelweights/Anathema-V2-LLaMA-70b
parameters:
weight: 0.40 # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.80 # Sparsity/pruning factor for this model's contribution.
epsilon: 0.1 # Single epsilon for the pruning
- model: /media/administrator/oiseauxai1data/modelout/Story-Base-V2
parameters:
weight: 0.35 # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.75 # Sparsity/pruning factor for this model's contribution.
epsilon: 0.10 # Single epsilon for the pruning
- model: /media/administrator/oiseauxai1data1/modelout/Middle-Base-V2
parameters:
weight: 0.30 # Contribution of this model (e.g., 50%) (can also use a gradiant) [0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.60 # Sparsity/pruning factor for this model's contribution.
epsilon: 0.15 # Single epsilon for the pruning
model_name: L3.3-70B-Amalgamma-V4 # Name of your merge
dtype: float32 # Input size float32, float16, bfloat16
out_dtype: bfloat16 # output size float32, float16, bfloat16
merge_method: della
parameters:
normalize: false # If true (default), weights are normalized to sum to 1.
# If false, absolute weights are used.
lambda: 1.1 # Single lambda for scaling the final merged deltas
tokenizer_source: base # Or 'base' if base_model is set, or 'union', careful with this one
chat_template: llama3 # Template for chat (Chatml, llama3, etc...)
license: apache-2.0 # License type
```
|
meerkqat/model-mirror | meerkqat | 2025-05-21T13:18:34Z | 0 | 0 | null | [
"region:us"
] | null | 2025-01-29T05:06:54Z | Personal use mirror of CivitAI models for simplifying env setup automation.
Model links:
- NTR MIX | illustrious-XL | Noob-XL : https://civitai.com/models/926443
- RealismIllustriousByStableYogi : https://civitai.com/models/974693
- RealisticVision : https://civitai.com/models/4201
- WAI-NSFW-illustrious-SDXL : https://civitai.com/models/827184
- ZEP8 : https://civitai.com/models/1067151/zep8
Lora links:
- 2ViewsPanties : https://civitai.com/models/1153238/2views-panties
- AnatomyHelper : https://civitai.com/models/1171869/anatomy-helper
- AshleyGraves : https://civitai.com/models/819229/ashley-graves-the-coffin-of-andy-and-leyley-illustriousxl-lora
- CasyTayStyle : https://civitai.com/models/1228487/style-casytay-illustrious-xlnoobai-xl
- CharlieMorningstar : https://civitai.com/models/1491650/charlie-morningstar-hazbin-hotel-2-outfits
- ContortionSuspendedHanging : https://civitai.com/models/1222948/contortion-suspended-hanging-concept-illustrious
- ClosedMouthFullOfCum : https://civitai.com/models/539254/closed-mouth-full-of-cum-lora-or-ponyxl-and-illustrious
- CunnilingusWhileStandingOnPenis : https://civitai.com/models/1013415/cunnilingus-while-standing-on-penis
- DanglingLegs : https://civitai.com/models/535745/dangling-legs-lora-or-ponyxl-and-illustrious
- DarkFireStyle01 : https://civitai.com/models/1233963/darkfirestyle01
- DemonTongue : https://civitai.com/models/1306998/demon-tongues
- DreadmirthStyle : https://civitai.com/models/883535/dreadmirth-style-art
- FaceInAss : https://civitai.com/models/932951/face-in-ass
- FeetAnimeIllustriousXL_v2.5 : https://civitai.com/models/1107767/odor-feetanimeillustriousxl
- FemdomSandwichThreesome : https://civitai.com/models/436979/femdom-sandwich-threesome
- FlatColor : https://civitai.com/models/1132089/flat-color-style
- FootWorship : https://civitai.com/models/946498/pov-footworship-nai-vpred-or-illustrious-or-pony
- FullWeightFacesitting : https://civitai.com/models/876630/fullweight-facesitting-part-4
- GamingClothes : https://civitai.com/models/1238948/gaming-clothes
- GloryWallLora : https://civitai.com/models/1255358/glory-wall-lora-or-illustrious
- HazbinHotelStyle : https://civitai.com/models/1343918/hazbin-hotel-style-il
- KissMultipleViewCloseUp : https://civitai.com/models/1256430/kiss-multiple-view-close-up-illustrious
- MGEMonsterGirls : https://civitai.com/models/556479/mge-monster-girls
- MomoAyase : https://civitai.com/models/942367/dandadan-momo-ayase-pony-illustrious
- NipplePenetration : https://civitai.com/models/492887/nipple-penetration-lora-or-ponyxl-and-illustrious
- NoseHookLora : https://civitai.com/models/1250664/nose-hook-lora-or-illustrious
- PinchingBellyFat : https://civitai.com/models/977343/pinching-belly-fat-one-two-hands
- PissDrinkingFemdom : https://civitai.com/models/869192/piss-drinking-femdom-pissing-part-4
- PussyWorshipFemdom : https://civitai.com/models/904315/pussy-worship-femdom
- RealisticVaginas_InniePussy1 : https://civitai.com/models/10332/realistic-vaginas-innie-pussy-1
- ReverseFellatio : https://civitai.com/models/543154/reverse-fellatio-lora-or-ponyxl-and-illustrious
- ReverseHeadscissor: https://civitai.com/models/1601201/reverse-headscissor?modelVersionId=1811995
- ReverseShrimpSuspension : https://civitai.com/models/680453
- SharpTeethAndFangs : https://civitai.com/models/1063052/sharp-teeth-and-fangs
- StealthFellatio : https://civitai.com/models/537491/stealth-fellatio-under-table-lora-or-ponyxl-and-illustrious
- StuckInSmallSpace : https://civitai.com/models/1110539/stuck-in-small-space
- Tarot : https://civitai.com/models/1117304/tarot-illustrious-concept
- TesticleGrab : https://civitai.com/models/1207602
- ThickCum : https://civitai.com/models/1107369/thick-cum-illustrious
- ThighSexLora : https://civitai.com/models/634236/thigh-sex-lora-or-ponyxl-and-illustrious
- VixionsDetailedPixelArtStyle : https://civitai.com/models/1130279/vixons-illustrious-styles-detailed-pixel-art
- VoreStyleLoraVoraciousMoga : https://civitai.com/models/1089819
- XRayGlasses : https://civitai.com/models/177451
Other links:
- Seljak wildcards : https://civitai.com/models/1138197/7000-sexy-prompts-snitched-from-seljak |
taguser/kantra-epoch3-2025-May-21 | taguser | 2025-05-21T13:17:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Coder-14B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-14B-Instruct",
"license:other",
"region:us"
] | null | 2025-05-21T13:17:29Z | ---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: test
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. -->
# test
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the training_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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: 3
### Training results
### Framework versions
- PEFT 0.15.1
- Transformers 4.51.0
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1 |
New-tutorial-Kulhad-Pizza-Viral-Video/Orginal.Full.Clip.Kulhad.Pizza.Viral.Video.Leaks.Official | New-tutorial-Kulhad-Pizza-Viral-Video | 2025-05-21T13:16:01Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T13:15:29Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?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>
Kulhad Pizza new viral video trending! This latest food sensation is capturing millions of views online. Discover what makes Kulhad Pizza special and why food lovers are going crazy over this unique dish. Watch the latest viral video and explore the story behind this trending street food. Learn how it’s made, where to get it, and why it’s a must-try in 2025. Stay updated with the newest food trends and viral videos. Click now to watch and share!
|
TIGER-Lab/General-Reasoner-Qwen3-4B | TIGER-Lab | 2025-05-21T13:15:10Z | 4 | 1 | null | [
"safetensors",
"qwen3",
"question-answering",
"dataset:TIGER-Lab/WebInstruct-verified",
"arxiv:2505.14652",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:finetune:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"region:us"
] | question-answering | 2025-05-20T23:23:47Z | ---
license: apache-2.0
datasets:
- TIGER-Lab/WebInstruct-verified
base_model:
- Qwen/Qwen3-4B-Base
pipeline_tag: question-answering
---
# General-Reasoner: Advancing LLM Reasoning Across All Domains
<p align="center">
<a href="https://github.com/TIGER-AI-Lab/General-Reasoner" target="_blank">💻 Code</a> |
<a href="https://arxiv.org/abs/2505.14652" target="_blank">📄 Paper</a> |
<a href="https://huggingface.co/datasets/TIGER-Lab/WebInstruct-verified" target="_blank">📊 Dataset</a> |
<a href="https://huggingface.co/collections/TIGER-Lab/general-reasoner-67fe9386e43e046489eac013" target="_blank">🤗 Model</a> |
<a href="https://tiger-ai-lab.github.io/General-Reasoner/" target="_blank">🌐 Project Page</a>
</p>
## Overview
<p align="center">
<img src="https://tiger-ai-lab.github.io/General-Reasoner/static/images/teaser.png" alt="General-Reasoner Teaser" width="650"/>
</p>
<p align="center" style="font-style: italic; font-size: 0.95rem;">
<em>
Figure: Effectiveness of <strong>General-Reasoner</strong> trained with diverse verifiable reasoning questions using model-based verifier compared to baseline methods on various reasoning tasks.
</em>
</p>
**General-Reasoner** is a training paradigm for large language models (LLMs), designed to robustly enhance reasoning abilities across diverse domains—not just mathematics and coding, but also physics, chemistry, finance, humanities, and more.
**Key features:**
- **Zero RL Training:** Direct reinforcement learning from base LLMs, bypassing intermediate supervised stages.
- **Diverse Reasoning Data:** 230K+ high-quality, verifiable questions sourced from the web and filtered for answer verifiability across disciplines.
- **Model-Based Verifier:** Compact 1.5B generative verifier model for context-aware, chain-of-thought answer validation, outperforming traditional rule-based methods.
**This specific model is the General-Reasoner variant trained based on [Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base).**
## Main Results
General-Reasoner outperforms base and supervised models on a variety of reasoning benchmarks, demonstrating robust generalization across domains:
<p align="center">
<a href="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" target="_blank">
<img src="https://github.com/TIGER-AI-Lab/General-Reasoner/raw/refs/heads/gh-pages/static/images/results_general.png" alt="Main Results" width="600">
</a>
</p>
## Citation
If you feel our work is helpful, please cite:
```bibtex
@article{general-reasoner,
title={{G}eneral-{R}easoner: Advancing LLM Reasoning Across All Domains},
author={Xueguang Ma and Qian Liu and Dongfu Jiang and Ge Zhang and Zejun Ma and Wenhu Chen},
year={2025},
journal={arXiv:2505.14652},
url={https://arxiv.org/abs/2505.14652}
}
``` |
shirro/Llama3.1_8B_SFT_GRPO_GGUF | shirro | 2025-05-21T13:15:02Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T13:10:14Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** shirro
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
moayl/LLAMA_3.2_MMAD | moayl | 2025-05-21T13:13:10Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mllama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-18T12:37:53Z | ---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** moayl
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama 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)
|
mjessup/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_shaggy_dinosaur | mjessup | 2025-05-21T13:11:53Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scurrying shaggy dinosaur",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T02:51:08Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_shaggy_dinosaur
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scurrying shaggy dinosaur
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_shaggy_dinosaur
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-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="mjessup/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_shaggy_dinosaur", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Steveeeeeeen/XCodec2 | Steveeeeeeen | 2025-05-21T13:10:11Z | 22 | 0 | transformers | [
"transformers",
"safetensors",
"xcodec2",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-20T15:59:58Z | ---
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] |
mljn/mdeberta-v3-base-finetuned-nuclear-classification | mljn | 2025-05-21T13:09:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/mdeberta-v3-base",
"base_model:finetune:microsoft/mdeberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-21T12:25:07Z | ---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mdeberta-v3-base-finetuned-climate-green-vs-environment-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mdeberta-v3-base-finetuned-climate-green-vs-environment-classification
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3798
- Accuracy: 0.9315
- F1 Macro: 0.9301
- Accuracy Balanced: 0.9300
- F1 Micro: 0.9315
- Precision Macro: 0.9302
- Recall Macro: 0.9300
- Precision Micro: 0.9315
- Recall Micro: 0.9315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 0.06
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.528 | 0.6775 | 500 | 0.4832 | 0.8502 | 0.8429 | 0.8364 | 0.8502 | 0.8594 | 0.8364 | 0.8502 | 0.8502 |
| 0.2934 | 1.3550 | 1000 | 0.2576 | 0.9302 | 0.9289 | 0.9302 | 0.9302 | 0.9278 | 0.9302 | 0.9302 | 0.9302 |
| 0.2357 | 2.0325 | 1500 | 0.3746 | 0.9268 | 0.9246 | 0.9215 | 0.9268 | 0.9291 | 0.9215 | 0.9268 | 0.9268 |
| 0.1437 | 2.7100 | 2000 | 0.3223 | 0.9295 | 0.9279 | 0.9274 | 0.9295 | 0.9285 | 0.9274 | 0.9295 | 0.9295 |
| 0.1139 | 3.3875 | 2500 | 0.3798 | 0.9315 | 0.9301 | 0.9300 | 0.9315 | 0.9302 | 0.9300 | 0.9315 | 0.9315 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
tes76/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leggy_shaggy_vulture | tes76 | 2025-05-21T13:09:14Z | 15 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am leggy shaggy vulture",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-14T15:16:57Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leggy_shaggy_vulture
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am leggy shaggy vulture
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leggy_shaggy_vulture
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="tes76/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leggy_shaggy_vulture", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
filipesantoscv11/97a1461b-b87a-4be0-ad02-3bc607c92052 | filipesantoscv11 | 2025-05-21T13:07:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-1.5B",
"base_model:adapter:unsloth/Qwen2-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-21T12:53:39Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 97a1461b-b87a-4be0-ad02-3bc607c92052
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: unsloth/Qwen2-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 8238689af7edb3c9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: system
field_output: prompt
format: '{instruction}'
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: true
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: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: filipesantoscv11/97a1461b-b87a-4be0-ad02-3bc607c92052
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 240
micro_batch_size: 5
mixed_precision: bf16
mlflow_experiment_name: /tmp/8238689af7edb3c9_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: dd656613-2166-41f4-8840-76ceb5e9b641
wandb_project: s56-2
wandb_run: your_name
wandb_runid: dd656613-2166-41f4-8840-76ceb5e9b641
warmup_steps: 40
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 97a1461b-b87a-4be0-ad02-3bc607c92052
This model is a fine-tuned version of [unsloth/Qwen2-1.5B](https://huggingface.co/unsloth/Qwen2-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7674
## 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-06
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 10
- 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: 40
- training_steps: 240
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2539 | 0.0394 | 240 | 1.7674 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Ding0702/qingshanchangzai | Ding0702 | 2025-05-21T13:07:45Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T13:07:45Z | ---
license: apache-2.0
---
|
tyasmul/8c4ce3be-1fa2-4662-bde4-cc553e6fd474 | tyasmul | 2025-05-21T13:07:31Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-1.5B",
"base_model:adapter:unsloth/Qwen2-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-21T12:53:31Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8c4ce3be-1fa2-4662-bde4-cc553e6fd474
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: unsloth/Qwen2-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 8238689af7edb3c9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8238689af7edb3c9_train_data.json
type:
field_instruction: system
field_output: prompt
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: 0.55
group_by_length: false
hub_model_id: tyasmul/8c4ce3be-1fa2-4662-bde4-cc553e6fd474
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5e-5
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/8238689af7edb3c9_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: 2048
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: dd656613-2166-41f4-8840-76ceb5e9b641
wandb_project: s56-7
wandb_run: your_name
wandb_runid: dd656613-2166-41f4-8840-76ceb5e9b641
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8c4ce3be-1fa2-4662-bde4-cc553e6fd474
This model is a fine-tuned version of [unsloth/Qwen2-1.5B](https://huggingface.co/unsloth/Qwen2-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5543
## 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_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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6385 | 0.0197 | 150 | 1.5543 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
tarundachepally/EGL_granite_3b_testmodel4_final1-Q3_K_S-GGUF | tarundachepally | 2025-05-21T13:07:07Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:tarundachepally/EGL_granite_3b_testmodel4_final1",
"base_model:quantized:tarundachepally/EGL_granite_3b_testmodel4_final1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T13:06:39Z | ---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: tarundachepally/EGL_granite_3b_testmodel4_final1
---
# tarundachepally/EGL_granite_3b_testmodel4_final1-Q3_K_S-GGUF
This model was converted to GGUF format from [`tarundachepally/EGL_granite_3b_testmodel4_final1`](https://huggingface.co/tarundachepally/EGL_granite_3b_testmodel4_final1) 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/tarundachepally/EGL_granite_3b_testmodel4_final1) 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 tarundachepally/EGL_granite_3b_testmodel4_final1-Q3_K_S-GGUF --hf-file egl_granite_3b_testmodel4_final1-q3_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo tarundachepally/EGL_granite_3b_testmodel4_final1-Q3_K_S-GGUF --hf-file egl_granite_3b_testmodel4_final1-q3_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 tarundachepally/EGL_granite_3b_testmodel4_final1-Q3_K_S-GGUF --hf-file egl_granite_3b_testmodel4_final1-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo tarundachepally/EGL_granite_3b_testmodel4_final1-Q3_K_S-GGUF --hf-file egl_granite_3b_testmodel4_final1-q3_k_s.gguf -c 2048
```
|
bruhzair/author-base10 | bruhzair | 2025-05-21T13:06:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:48:55Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# author-base10
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 /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--MaziyarPanahi--calme-2.3-llama3.1-70b/snapshots/6d0ae253d0d3ec2005182e0484d82c7f46e5ee5c
* /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
* /workspace/cache/models--Steelskull--L3.3-Cu-Mai-R1-70b/snapshots/b91f4c0521b59336a71da961ac133458d81f2f4e
* /workspace/cache/models--hitachi-nlp--Llama-3.1-70B-FLDx2/snapshots/051461669991c591aab9e96182b84bdc97733c7f
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7
- model: /workspace/cache/models--hitachi-nlp--Llama-3.1-70B-FLDx2/snapshots/051461669991c591aab9e96182b84bdc97733c7f
- model: /workspace/cache/models--Steelskull--L3.3-Cu-Mai-R1-70b/snapshots/b91f4c0521b59336a71da961ac133458d81f2f4e
- model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
- model: /workspace/cache/models--MaziyarPanahi--calme-2.3-llama3.1-70b/snapshots/6d0ae253d0d3ec2005182e0484d82c7f46e5ee5c
base_model: /workspace/cache/models--TheSkullery--L3.1x3.3-Hydroblated-R1-70B-v5/snapshots/885b8ba1b37ca0ec5135b20c7ec4ed35441536f7
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: bfloat16
```
|
Armandotrsg/qwen-cybersecurity-2.5-7b | Armandotrsg | 2025-05-21T13:04:44Z | 66 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T00:46:07Z | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Armandotrsg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
FINGU-AI/Sesame-1B-Korean | FINGU-AI | 2025-05-21T13:04:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"csm",
"text-to-audio",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/csm-1b",
"base_model:finetune:unsloth/csm-1b",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-05-21T12:55:12Z | ---
base_model: unsloth/csm-1b
tags:
- text-generation-inference
- transformers
- unsloth
- csm
license: cc-by-sa-4.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** FINGU-AI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/csm-1b
This csm 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) |
marimmo/Lab2_SFT_openorca | marimmo | 2025-05-21T13:02:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-21T13:01:13Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** marimmo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
emiliensilly/poststemQ | emiliensilly | 2025-05-21T13:02:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:43:10Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Hemenguel/mt0-small-finetuned | Hemenguel | 2025-05-21T13:01:57Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bigscience/mt0-small",
"base_model:adapter:bigscience/mt0-small",
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T12:55:26Z | ---
library_name: peft
license: apache-2.0
base_model: bigscience/mt0-small
tags:
- generated_from_trainer
model-index:
- name: mt0-small-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt0-small-finetuned
This model is a fine-tuned version of [bigscience/mt0-small](https://huggingface.co/bigscience/mt0-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- 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: 3
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |
aipglu/my_awesome_asr_mind_model | aipglu | 2025-05-21T13:01:31Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-21T11:58:07Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
model-index:
- name: my_awesome_asr_mind_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
paulhjwu/my_awesome_nllb_twen_books_model_3 | paulhjwu | 2025-05-21T13:01:12Z | 0 | 0 | null | [
"safetensors",
"m2m_100",
"region:us"
] | null | 2025-05-21T12:25:07Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
zktmp/2grpo-dof-r16u8-math-step40 | zktmp | 2025-05-21T13:01:11Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2025-05-21T12:58:08Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
aisha-org/whisper-400h | aisha-org | 2025-05-21T13:00:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-21T12:58:23Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
koreankiwi99/dpo_model_p_data_correctness_template | koreankiwi99 | 2025-05-21T12:59:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:58:36Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
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vermoney/7280b65f-ad58-4f50-84b2-960f36120294 | vermoney | 2025-05-21T12:58:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-1.5B",
"base_model:adapter:unsloth/Qwen2-1.5B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-21T12:53:37Z | <!DOCTYPE html>
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Shannonjunior/a6bd0d30-60b4-49bd-83e2-7ac0d30d5d50 | Shannonjunior | 2025-05-21T12:58:51Z | 0 | 0 | null | [
"region:us"
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Damrongbou/kcpx1_model | Damrongbou | 2025-05-21T12:58:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T10:46:29Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
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<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
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color: rgba(107, 114, 128, 1);
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}
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background-color: rgb(11, 15, 25);
}
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}
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</style>
<script>
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RRashmini/google-umt5-small-28 | RRashmini | 2025-05-21T12:58:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"umt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-21T12:55:11Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
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color: rgba(107, 114, 128, 1);
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}
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background-color: rgb(11, 15, 25);
}
.dark h1 {
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TEC2004/SafeEar-ASV19-spoof-detection | TEC2004 | 2025-05-21T12:56:01Z | 0 | 1 | null | [
"audio",
"speech-antispoofing",
"spoof-detection",
"voice-liveness",
"safeear",
"asvspoof2019",
"pytorch",
"audio-classification",
"license:apache-2.0",
"region:us"
] | audio-classification | 2025-05-21T11:07:08Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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margin: 0;
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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TechitoTamani/Qwen3-4B_FinetuneWithMyData-1and3 | TechitoTamani | 2025-05-21T12:54:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T12:54:28Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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GODBlessU2/250d7728-bdba-40f9-ad38-bed2f3bc9255 | GODBlessU2 | 2025-05-21T12:54:42Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T09:57:58Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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margin: 0;
}
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background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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if (storageTheme) {
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genixo/Llama3.2-learn | genixo | 2025-05-21T12:51:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:38:03Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
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/>
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Noto Color Emoji;
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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disrespect9/7f94fbc1-ba02-4974-a92a-66ac1a67d74c | disrespect9 | 2025-05-21T12:50:59Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:50:47Z | <!DOCTYPE html>
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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box-sizing: border-box;
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.dark main {
background-color: rgb(11, 15, 25);
}
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color: rgb(209, 213, 219);
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Anilhugginface/fine-tuned-roberta-spam-500k | Anilhugginface | 2025-05-21T12:49:08Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-20T18:11:30Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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<style>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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alt=""
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<div>
<h1>429</h1>
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Wizard0504/qwen3-mcqa-finetuned3 | Wizard0504 | 2025-05-21T12:48:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:47:10Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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document.documentElement.classList.add("dark");
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document.documentElement.classList.remove("dark");
}
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<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
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</body>
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int1306866/ab7a8c92-15fc-422f-b029-20211fb78764 | int1306866 | 2025-05-21T12:48:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T10:51:47Z | <!DOCTYPE html>
<html class="" lang="en">
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<meta
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
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const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
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alt=""
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<h1>429</h1>
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int1306866/f89780da-3f4b-45ec-9dde-92944f0e218c | int1306866 | 2025-05-21T12:47:49Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T10:51:36Z | <!DOCTYPE html>
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<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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alt=""
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<h1>429</h1>
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code-red-20/hyperbolic_latent_sae | code-red-20 | 2025-05-21T12:47:26Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:47:26Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<style>
body {
margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
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<div>
<h1>429</h1>
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int1306866/af18b24a-ac35-427f-a35b-f6d0a70ce8ad | int1306866 | 2025-05-21T12:47:25Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T10:50:25Z | <!DOCTYPE html>
<html class="" lang="en">
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
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<h1>429</h1>
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walidnihbos3/967e10b9-5844-401a-a060-d3341eed3de2 | walidnihbos3 | 2025-05-21T12:46:14Z | 0 | 0 | null | [
"region:us"
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johngreendr1/244661e0-f00a-4598-988b-9b0a8e2943e9 | johngreendr1 | 2025-05-21T12:45:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:adapter:Qwen/Qwen3-8B-Base",
"region:us"
] | null | 2025-05-21T11:34:39Z | <!DOCTYPE html>
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.dark main {
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color: rgb(209, 213, 219);
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.dark p, .dark a {
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</style>
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int1306866/6e05778f-e786-47c7-89e0-44a4b9189097 | int1306866 | 2025-05-21T12:45:39Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T10:52:54Z | <!DOCTYPE html>
<html class="" lang="en">
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img {
width: 6rem;
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h1 {
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.dark main {
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color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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bluw37-test/loras | bluw37-test | 2025-05-21T12:45:21Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-18T13:32:29Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
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Raihan2212/sssdsd | Raihan2212 | 2025-05-21T12:45:17Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-05-21T12:45:17Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
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Alviniqnacio/YOLOv11-M_Normal_Dataset | Alviniqnacio | 2025-05-21T12:44:40Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:44:36Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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davgauch/MNLP_M2_mcqa_model_test | davgauch | 2025-05-21T12:44:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-21T12:23:31Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta name="twitter:site" content="@huggingface" />
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content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
/>
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sheheb/Cotagem_Bois | sheheb | 2025-05-21T12:44:30Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-21T12:37:14Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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content="Hugging Face - The AI community building the future."
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<meta property="og:type" content="website" />
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<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
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Tiba/Qwen2.5-7B-Instruct-Gensyn-Swarm-soft_pensive_ant | Tiba | 2025-05-21T12:43:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am soft pensive ant",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-7B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T02:40:24Z | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
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</style>
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Intelligent-Instruments-Lab/tungnaa-models-public | Intelligent-Instruments-Lab | 2025-05-21T12:42:56Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-05-21T11:37:05Z | <!DOCTYPE html>
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h1 {
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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max-width: 28rem;
box-sizing: border-box;
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}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
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sergioalves/a12798f5-9aae-47a4-9cad-c92ab59dfd29 | sergioalves | 2025-05-21T12:42:50Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:13:02Z | <!DOCTYPE html>
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
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font-weight: 700;
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p, a {
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font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
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}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
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hoanghuy100202/FineTune_JobAdviceApplication | hoanghuy100202 | 2025-05-21T12:42:01Z | 0 | 0 | null | [
"safetensors",
"gguf",
"llama",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-21T12:33:21Z | <!DOCTYPE html>
<html class="" lang="en">
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<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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AventIQ-AI/Paraphrase_Detection | AventIQ-AI | 2025-05-21T12:41:38Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:40:41Z | <!DOCTYPE html>
<html class="" lang="en">
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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yjrfhjtduh/rshgfhstrh | yjrfhjtduh | 2025-05-21T12:41:07Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:41:07Z | <!DOCTYPE html>
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
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color: rgb(209, 213, 219);
}
.dark p, .dark a {
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}
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GERRSRGstr/wafe | GERRSRGstr | 2025-05-21T12:41:07Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-05-21T12:41:07Z | <!DOCTYPE html>
<html class="" lang="en">
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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tsrfhg/jhgbnshtgft | tsrfhg | 2025-05-21T12:41:07Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-21T12:41:07Z | <!DOCTYPE html>
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text-align: center;
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
Subsets and Splits