---

library_name: transformers
tags: []

---

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# QuantFactory/Llama3.1-8B-PRM-Deepseek-Data-GGUF
This is quantized version of [RLHFlow/Llama3.1-8B-PRM-Deepseek-Data](https://huggingface.co/RLHFlow/Llama3.1-8B-PRM-Deepseek-Data) created using llama.cpp

# Original Model Card


This is a process-supervised reward (PRM) trained on Mistral-generated data from the project [RLHFlow/RLHF-Reward-Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling)

The model is trained from [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on [RLHFlow/Deepseek-PRM-Data](https://huggingface.co/datasets/RLHFlow/Deepseek-PRM-Data) for 1 epochs. We use a global batch size of 32 and a learning rate of 2e-6, where we pack the samples and split them into chunks of 8192 token. See more training details at https://github.com/RLHFlow/Online-RLHF/blob/main/math/llama-3.1-prm.yaml .


## BoN evaluation result for Mistral generator:

| Model      | Method     | GSM8K     | MATH |
| ------------- | ------------- | ------------- | -------- |
| Mistral-7B | Pass@1 | 77.9 |  28.4   |
| Mistral-7B | Majority Voting@1024 | 84.2 | 36.8  |
| Mistral-7B | Mistral-ORM@1024 | 90.1 | 43.6 |
| Mistral-7B | Mistral-PRM@1024 | 92.4 | 46.3 |

## Scaling the inference sampling to N=1024 for Deepseek generator:

| Model         | Method                    | GSM8K | MATH |
| ------------- | ------------- | ------------- | -------- |
| Deepseek-7B | Pass@1 | 83.9 | 38.4 |
| Deepseek-7B | Majority Voting@1024 | 89.7 | 57.4  |
| Deepseek-7B | Deepseek-ORM@1024 | 93.4 | 52.4 |
| Deepseek-7B | Deepseek-PRM@1024 | 93.0 | 58.1 |
| Deepseek-7B | Mistral-ORM@1024 (OOD) | 90.3 | 54.9 |
| Deepseek-7B | Mistral-PRM@1024 (OOD) | 91.9 | 56.9 |

## Visualization


![image/png](https://cdn-uploads.huggingface.co/production/uploads/643e59806db6ba8c5ee123f3/i622m76fvKv8drLmwl8Q3.png)

## Usage 

See https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/main/math-rm for detailed examples. 

## Citation

The automatic annotation was proposed in the Math-shepherd paper:

```
@inproceedings{wang2024math,
  title={Math-shepherd: Verify and reinforce llms step-by-step without human annotations},
  author={Wang, Peiyi and Li, Lei and Shao, Zhihong and Xu, Runxin and Dai, Damai and Li, Yifei and Chen, Deli and Wu, Yu and Sui, Zhifang},
  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={9426--9439},
  year={2024}
}

```

If you find the training recipe useful, please consider cite it as follows.

```
@misc{xiong2024rlhflowmath,
      author={Wei Xiong and Hanning Zhang and Nan Jiang and Tong Zhang},
  title = {An Implementation of Generative PRM},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/RLHFlow/RLHF-Reward-Modeling}}
}
```