---
title: HuatuoGPT-o1-7B
canonical_url: "https://www.modelscope.cn/models/FreedomIntelligence/HuatuoGPT-o1-7B"
md_url: "https://www.modelscope.cn/models/FreedomIntelligence/HuatuoGPT-o1-7B.md"
repository: FreedomIntelligence/HuatuoGPT-o1-7B
last_updated: 2025-01-07
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
base_model:
  - Qwen/Qwen2.5-7B-Instruct
base_model_relation: finetune
parameters: 7.6B
tensor_type:
  - BF16
library_name:
  - transformer
  - safetensors
language:
  - en
  - zh
downloads: 5359
stars: 13
tags:
  - medical
---

# HuatuoGPT-o1-7B

> HuatuoGPT-o1-7B - FreedomIntelligence 在 ModelScope 开源的模型。Introduction HuatuoGPT-o1 is a medical LLM designed for advanced medical reasoning. It generates a complex thought process, reflecting and refining its reasoning, before providing a final response.

FreedomIntelligence/HuatuoGPT-o1-7B 是 ModelScope 魔搭社区上的 7.6B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen2.5-7B-Instruct 构建。

- **Repository**: FreedomIntelligence/HuatuoGPT-o1-7B
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 7.6B
- **Base model**: Qwen/Qwen2.5-7B-Instruct
- **Tags**: medical
- **Downloads**: 5359
- **Stars**: 13
- **Last updated**: 2025-01-07

Source: https://www.modelscope.cn/models/FreedomIntelligence/HuatuoGPT-o1-7B

---

<div align="center">
<h1>
  HuatuoGPT-o1-7B
</h1>
</div>

<div align="center">
<a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank">GitHub</a> | <a href="https://arxiv.org/pdf/2412.18925" target="_blank">Paper</a>
</div>

# <span>Introduction</span>
**HuatuoGPT-o1** is a medical LLM designed for advanced medical reasoning.  It generates a complex thought process, reflecting and refining its reasoning, before providing a final response. 

For more information, visit our GitHub repository: 
[https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1).

# <span>Model Info</span>
|                      | Backbone     | Supported Languages | Link                                                                  |
| -------------------- | ------------ | ----- | --------------------------------------------------------------------- |
| **HuatuoGPT-o1-8B**  | LLaMA-3.1-8B  | English    | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) |
| **HuatuoGPT-o1-70B** | LLaMA-3.1-70B | English    | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) |
| **HuatuoGPT-o1-7B**  | Qwen2.5-7B   | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) |
| **HuatuoGPT-o1-72B** | Qwen2.5-72B  | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) |



# <span>Usage</span>
You can use HuatuoGPT-o1-7B in the same way as `Qwen2.5-7B-Instruct`. 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 = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-7B",torch_dtype="auto",device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-7B")

input_text = "How to stop a cough?"
messages = [{"role": "user", "content": input_text}]

inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True
), return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

HuatuoGPT-o1 adopts a *thinks-before-it-answers* approach, with outputs formatted as:

```
## Thinking
[Reasoning process]

## Final Response
[Output]
```

# <span>📖 Citation</span>
```
@misc{chen2024huatuogpto1medicalcomplexreasoning,
      title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}, 
      author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang},
      year={2024},
      eprint={2412.18925},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.18925}, 
}
```