HuatuoGPT-o1-72B

GitHub | Paper

# Introduction **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. For more information, visit our GitHub repository: [https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1). # Model Info | | Backbone | Supported Languages | Link | | -------------------- | ------------ | ----- | --------------------------------------------------------------------- | | **HuatuoGPT-o1-8B** | LLaMA-3.1-8B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) | | **HuatuoGPT-o1-70B** | LLaMA-3.1-70B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) | | **HuatuoGPT-o1-7B** | Qwen2.5-7B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) | | **HuatuoGPT-o1-72B** | Qwen2.5-72B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) | # Usage You can use HuatuoGPT-o1-72B in the same way as `Qwen2.5-72B-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: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-72B",torch_dtype="auto",device_map="auto") tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-72B") input_text = "How to stop a cough?" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True ), return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` HuatuoGPT-o1 adopts a *thinks-before-it-answers* approach, with outputs formatted as: ``` ## Thinking [Reasoning process] ## Final Response [Output] ``` # 📖 Citation ``` @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}, } ```