FreedomIntelligence/HuatuoGPT-o1-70B
<div align="center"> <h1> HuatuoGPT-o1-70B </h1> </div>
<div align="center"> <a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="blank">GitHub</a> | <a href="https://arxiv.org/pdf/2412.18925" target="blank">Paper</a> </div>
<span>Introduction</span>
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.
<span>Model Info</span>
<span>Usage</span>
You can use HuatuoGPT-o1 in the same way as Llama-3.1-70B-Instruct. You can deploy it with tools like vllm or Sglang, or perform direct inference:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-70B",torch_dtype="auto",device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-70B")
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]<span>📖 Citation</span>
@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},
}