---
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}
}
```