HuatuoGPT-3-32B/README.md

library_name, license, pipeline_tag, language, base_model, tags

library_name	license	pipeline_tag	language	base_model	tags
transformers	apache-2.0	text-generation	en zh	Qwen/Qwen3-32B	medical reasoning

🩺 HuatuoGPT-3-32B

🏠 GitHub | 📄 Paper

Introduction

HuatuoGPT-3 is an open-source medical LLM trained with SeedRL, an RL-only domain adaptation paradigm that transforms a base model into a medical expert in a single RL stage.

For more information, visit our GitHub repository: https://github.com/FreedomIntelligence/HuatuoGPT-3

Important

HuatuoGPT-3-32B is set to thinking mode by default. The output contains a <think>...</think> reasoning block followed by the final response after </think>.

Model Info

Model	Description	Backbone	Link
HuatuoGPT-3-32B	32B medical LLM trained with SeedRL	Qwen3-32B	HF Link
HuatuoGPT-3-8B	8B medical LLM trained with SeedRL	Qwen3-8B-Base	HF Link
HuatuoGPT-3-7B-Pangu	7B medical LLM trained with SeedRL	openPangu-Embedded-7B	HF Link

Usage

You can use HuatuoGPT-3-8B in the same way as Qwen3-32B. You can deploy it with tools like vLLM or SGLang, or perform direct inference:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "FreedomIntelligence/HuatuoGPT-3-32B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "A patient has fever, cough, and shortness of breath. What should be considered first?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📖 Citation

@inproceedings{huatuogpt3,
  title={OnePO: Direct One-stage Policy Optimization for SFT-free Domain Adaptation},
  author={Chen, Junying and Xie, Xinyuan and Li, Ziniu and Wang, Benyou},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning},
  series={Proceedings of Machine Learning Research},
  volume={306},
  year={2026}
}