README.md

---
license: apache-2.0
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
- FreedomIntelligence/medical-o1-verifiable-problem
language:
- en
- zh
base_model: FreedomIntelligence/HuatuoGPT-o1-7B
pipeline_tag: text-generation
tags:
- medical
- llama-cpp
- gguf-my-repo
---

# Triangle104/HuatuoGPT-o1-7B-Q5_K_M-GGUF
This model was converted to GGUF format from [`FreedomIntelligence/HuatuoGPT-o1-7B`](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) for more details on the model.

---
Model details:
-
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.


		Usage
	



You can use HuatuoGPT-o1-7B in the same way as Qwen2.5-7B-Instruct. You can deploy it with tools like vllm or Sglang,  or perform direct inference:

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]

		📖 Citation
	



@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}, 
}

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/HuatuoGPT-o1-7B-Q5_K_M-GGUF --hf-file huatuogpt-o1-7b-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/HuatuoGPT-o1-7B-Q5_K_M-GGUF --hf-file huatuogpt-o1-7b-q5_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/HuatuoGPT-o1-7B-Q5_K_M-GGUF --hf-file huatuogpt-o1-7b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/HuatuoGPT-o1-7B-Q5_K_M-GGUF --hf-file huatuogpt-o1-7b-q5_k_m.gguf -c 2048
```