Instructions to use FreedomIntelligence/HuatuoGPT-3-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/HuatuoGPT-3-32B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FreedomIntelligence/HuatuoGPT-3-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-3-32B")
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-3-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FreedomIntelligence/HuatuoGPT-3-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/HuatuoGPT-3-32B"
# 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-3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/HuatuoGPT-3-32B

SGLang

How to use FreedomIntelligence/HuatuoGPT-3-32B 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-3-32B" \
    --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-3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-3-32B" \
        --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-3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FreedomIntelligence/HuatuoGPT-3-32B with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/HuatuoGPT-3-32B
```

HuatuoGPT-3-32B / README.md

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

	<div align="center">
	<h1>🩺 HuatuoGPT-3-32B</h1>
	</div>

	<div align="center">
	<a href="https://github.com/FreedomIntelligence/HuatuoGPT-3" target="_blank">🏠 GitHub</a>
	\|
	<a href="" target="_blank">📄 Paper</a>
	</div>

	# <span>Introduction</span>

	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](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>`.

	# <span>Model Info</span>

	\| Model \| Description \| Backbone \| Link \|
	\| --- \| --- \| --- \| --- \|
	\| HuatuoGPT-3-32B \| 32B medical LLM trained with SeedRL \| Qwen3-32B \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-32B) \|
	\| HuatuoGPT-3-8B \| 8B medical LLM trained with SeedRL \| Qwen3-8B-Base \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-8B) \|
	\| HuatuoGPT-3-7B-Pangu \| 7B medical LLM trained with SeedRL \| openPangu-Embedded-7B \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-7B-Pangu) \|


	# <span>Usage</span>

	You can use HuatuoGPT-3-8B in the same way as `Qwen3-32B`. You can deploy it with tools like [vLLM](https://github.com/vllm-project/vllm) or [SGLang](https://github.com/sgl-project/sglang), or perform direct inference:

	```python
	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))
	```

	# <span>📖 Citation</span>

	```bibtex
	@article{huatuogpt3,
	title={HuatuoGPT-3: RL-Only Domain Adaptation from Base Models via Off-Policy Seeding},
	author={Coming soon},
	journal={arXiv preprint},
	year={2026}
	}
	```