---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
language:
- en
- zh
base_model:
- FreedomIntelligence/openPangu-Embedded-7B
tags:
- medical
- reasoning
---

<div align="center">
  <h1>🩺 HuatuoGPT-3-7B-Pangu</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.

**HuatuoGPT-3-7B-Pangu** is the Pangu-based variant in the HuatuoGPT-3 series. Different from the Qwen-based versions, it is built on **FreedomIntelligence/openPangu-Embedded-7B** and trained with **Ascend NPUs**.

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

> [!IMPORTANT]
> **HuatuoGPT-3-7B-Pangu is set to thinking mode by default.** Since it is based on openPangu, the generated reasoning content is placed between `[unused16]` and `[unused17]`, and the final response starts after `[unused17]`.

# <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-3-7B-Pangu in the same way as `FreedomIntelligence/openPangu-Embedded-7B`. 

When using this model, please note that the Pangu backbone requires `trust_remote_code=True` in Transformers, and `--trust_remote_code` when serving with vLLM.

- Direct inference:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "FreedomIntelligence/HuatuoGPT-3-7B-Pangu"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    use_fast=False,
    trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "What are the common causes of chest pain?"}
]

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,
    eos_token_id=45892,
    return_dict_in_generate=True
)

input_length = inputs.input_ids.shape[1]
generated_tokens = outputs.sequences[:, input_length:]
output_text = tokenizer.decode(generated_tokens[0])

thinking_content = output_text.split("[unused17]")[0].split("[unused16]")[-1].strip()
content = output_text.split("[unused17]")[-1].split("[unused10]")[0].strip()

print("thinking content:", thinking_content)
print("content:", content)
```

- You can also serve the model with vLLM. Make sure to include `--trust_remote_code`:

```bash
CUDA_VISIBLE_DEVICES=0 \
vllm serve FreedomIntelligence/HuatuoGPT-3-7B-Pangu \
  --served-model-name HuatuoGPT-3-7B-Pangu \
  --trust_remote_code \
  --port 8000
```

Or:

```bash
CUDA_VISIBLE_DEVICES=0 \
python -m vllm.entrypoints.openai.api_server \
  --model FreedomIntelligence/HuatuoGPT-3-7B-Pangu \
  --served-model-name HuatuoGPT-3-7B-Pangu \
  --trust_remote_code \
  --port 8000
```


# <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}
}
```