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--- |
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license: apache-2.0 |
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datasets: |
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- FreedomIntelligence/medical-o1-reasoning-SFT |
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- FreedomIntelligence/medical-o1-verifiable-problem |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen2.5-7B-Instruct |
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pipeline_tag: text-generation |
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tags: |
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- medical |
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--- |
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<div align="center"> |
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<h1> |
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HuatuoGPT-o1-7B |
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</h1> |
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</div> |
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<div align="center"> |
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<a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank">GitHub</a> | <a href="https://arxiv.org/pdf/2412.18925" target="_blank">Paper</a> |
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</div> |
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# <span>Introduction</span> |
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**HuatuoGPT-o1** is a medical LLM designed for advanced medical reasoning. It generates a complex thought process, reflecting and refining its reasoning, before providing a final response. |
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For more information, visit our GitHub repository: |
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[https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1). |
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# <span>Model Info</span> |
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| | Backbone | Supported Languages | Link | |
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| -------------------- | ------------ | ----- | --------------------------------------------------------------------- | |
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| **HuatuoGPT-o1-8B** | LLaMA-3.1-8B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) | |
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| **HuatuoGPT-o1-70B** | LLaMA-3.1-70B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) | |
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| **HuatuoGPT-o1-7B** | Qwen2.5-7B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) | |
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| **HuatuoGPT-o1-72B** | Qwen2.5-72B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) | |
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# <span>Usage</span> |
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You can use HuatuoGPT-o1-7B in the same way as `Qwen2.5-7B-Instruct`. You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), or perform direct inference: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-7B",torch_dtype="auto",device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-7B") |
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input_text = "How to stop a cough?" |
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messages = [{"role": "user", "content": input_text}] |
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inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True |
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), return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=2048) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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HuatuoGPT-o1 adopts a *thinks-before-it-answers* approach, with outputs formatted as: |
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``` |
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## Thinking |
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[Reasoning process] |
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## Final Response |
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[Output] |
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``` |
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# <span>๐ Citation</span> |
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``` |
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@misc{chen2024huatuogpto1medicalcomplexreasoning, |
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title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}, |
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author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang}, |
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year={2024}, |
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eprint={2412.18925}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2412.18925}, |
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} |
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``` |