Medical-R1-Distill-Data-Chinese

## Introduction
This dataset is an SFT dataset distilled from **Deepseek-R1 (Full Power Version)**, based on Chinese [medical verifiable problems](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem) from HuatuoGPT-o1.

The distillation originates from the native Deepseek-R1 API requests. We hope this distilled dataset can help initialize your models with the reasoning chain from R1. You can also use our previously built medical verified long reasoning chains based on GPT-4o on [medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT).

For details, see our [paper](https://arxiv.org/pdf/2412.18925) and [GitHub repository](https://github.com/FreedomIntelligence/HuatuoGPT-o1).

## Citation

If you find our data useful, please consider citing our work!
```
@misc{chen2024huatuogpto1medicalcomplexreasoning,
title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs},
author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang},
year={2024},
eprint={2412.18925},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.18925},
}
```

### 引言
本数据集为**监督微调（Supervised Fine-Tuning, SFT）**数据集，源自**Deepseek-R1（全量版本）** 的蒸馏产物，其基础数据来自华驼GPT-o1（HuatuoGPT-o1）中的中文[医疗可验证问题集（medical verifiable problems）](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem)。

本次蒸馏过程直接基于原生Deepseek-R1的API请求完成。我们期望本蒸馏数据集可帮助您基于R1的推理链对模型进行初始化。此外，您也可使用此前基于GPT-4o构建的医疗验证长推理链数据集，对应数据集的开源地址为：[medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT)。

如需了解更多细节，请参阅我们的[论文](https://arxiv.org/pdf/2412.18925)与[GitHub仓库](https://github.com/FreedomIntelligence/HuatuoGPT-o1)。

### 引用
若您认为本数据集对您的研究有所帮助，请考虑引用我们的工作：

@misc{chen2024huatuogpto1medicalcomplexreasoning,
title={"HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs"},
author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang},
year={2024},
eprint={2412.18925},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.18925},
}