Instructions to use FreedomIntelligence/HuatuoGPT-3-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

• Libraries
• Transformers How to use FreedomIntelligence/HuatuoGPT-3-9B with Transformers: ```

Use a pipeline as a high-level helper

from transformers import pipeline pipe = pipeline("image-text-to-text", model="FreedomIntelligence/HuatuoGPT-3-9B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)

Load model directly

from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("FreedomIntelligence/HuatuoGPT-3-9B") model = AutoModelForMultimodalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-3-9B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
-  vLLM  How to use FreedomIntelligence/HuatuoGPT-3-9B with vLLM: ##### Install from pip and serve model```
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/HuatuoGPT-3-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/HuatuoGPT-3-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Dockerdocker model run hf.co/FreedomIntelligence/HuatuoGPT-3-9B

• SGLang How to use FreedomIntelligence/HuatuoGPT-3-9B with SGLang: ##### Install from pip and serve model```

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server
--model-path "FreedomIntelligence/HuatuoGPT-3-9B"
--host 0.0.0.0
--port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions"
-H "Content-Type: application/json"
--data '{ "model": "FreedomIntelligence/HuatuoGPT-3-9B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

##### Use Docker images```
docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FreedomIntelligence/HuatuoGPT-3-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/HuatuoGPT-3-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

• Docker Model Runner How to use FreedomIntelligence/HuatuoGPT-3-9B with Docker Model Runner: docker model run hf.co/FreedomIntelligence/HuatuoGPT-3-9B

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=True during inference. The model generates reasoning before providing its final answer after </think>.

Model Info

Usage

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}
}

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