初始化项目，由ModelHub XC社区提供模型

Model: RLHFlow/Llama3.1-8B-PRM-Mistral-Data
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+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/Mistral-PRM-Data](https://huggingface.co/datasets/RLHFlow/Mistral-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}}
+}
+```
+
+