Introduction
HuatuoGPT-3-9B is a medical LLM built on Qwen3.5-9B with One-stage Policy Optimization (OnePO). OnePO adapts language models to medicine in a single reinforcement-learning stage, without preceding domain-specific supervised fine-tuning. Teacher responses provide temporary guidance and are retired as the model improves.
We release the training code, medical RL dataset, and 8B rubric grader.
HuatuoGPT-3 requires thinking mode. Keep
enable_thinking=Trueduring inference. The model generates reasoning before providing its final answer after</think>.
Model Info
| Model | Backbone | Purpose | Access |
|---|---|---|---|
| HuatuoGPT-3-9B | Qwen3.5-9B | Medical reasoning | HF Link |
| HuatuoGPT-3-27B | Qwen3.8-27B | Medical reasoning | HF Link |
Usage
The included generation config enables sampling with temperature=0.2. Override these settings in model.generate() when needed.
HuatuoGPT-3-9B can be used like Qwen3.5-9B and deployed with vLLM or SGLang.
For direct text inference, use a Transformers version with Qwen3.5 support (transformers>=5.4.0) and accelerate:
from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "FreedomIntelligence/HuatuoGPT-3-9B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
dtype="auto",
device_map="auto",
).eval()
messages = [{
"role": "user",
"content": [{"type": "text", "text": "What are the common causes of chest pain?"}],
}]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
enable_thinking=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096)
response = outputs[0, inputs["input_ids"].shape[-1]:]
print(processor.decode(response, skip_special_tokens=True))
📖 Citation
@inproceedings{chen2026onepo,
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},
year={2026}
}