QuantFactory/HuatuoGPT-o1-7B-GGUF
This is quantized version of FreedomIntelligence/HuatuoGPT-o1-7B created using llama.cpp
Original Model Card
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.
Model Info
| Backbone | Supported Languages | Link | |
|---|---|---|---|
| HuatuoGPT-o1-8B | LLaMA-3.1-8B | English | HF Link |
| HuatuoGPT-o1-70B | LLaMA-3.1-70B | English | HF Link |
| HuatuoGPT-o1-7B | Qwen2.5-7B | English & Chinese | HF Link |
| HuatuoGPT-o1-72B | Qwen2.5-72B | English & Chinese | HF Link |
Usage
You can use HuatuoGPT-o1-7B in the same way as Qwen2.5-7B-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-o1-7B",torch_dtype="auto",device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-7B")
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},
}