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Model: QuantFactory/HuatuoGPT-o1-8B-GGUF Source: Original Platform
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---
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license: apache-2.0
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datasets:
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- FreedomIntelligence/medical-o1-reasoning-SFT
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- FreedomIntelligence/medical-o1-verifiable-problem
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language:
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- en
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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pipeline_tag: text-generation
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tags:
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- medical
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/HuatuoGPT-o1-8B-GGUF
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This is quantized version of [FreedomIntelligence/HuatuoGPT-o1-8B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) created using llama.cpp
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# Original Model Card
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<div align="center">
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<h1>
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HuatuoGPT-o1-8B
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</h1>
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</div>
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<div align="center">
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<a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank">GitHub</a> | <a href="https://arxiv.org/pdf/2412.18925" target="_blank">Paper</a>
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</div>
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# <span>Introduction</span>
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**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.
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For more information, visit our GitHub repository:
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[https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1).
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# <span>Model Info</span>
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| | Backbone | Supported Languages | Link |
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| -------------------- | ------------ | ----- | --------------------------------------------------------------------- |
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| **HuatuoGPT-o1-8B** | LLaMA-3.1-8B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) |
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| **HuatuoGPT-o1-70B** | LLaMA-3.1-70B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) |
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| **HuatuoGPT-o1-7B** | Qwen2.5-7B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) |
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| **HuatuoGPT-o1-72B** | Qwen2.5-72B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) |
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# <span>Usage</span>
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You can use HuatuoGPT-o1 in the same way as `Llama-3.1-8B-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:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B",torch_dtype="auto",device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B")
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input_text = "How to stop a cough?"
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messages = [{"role": "user", "content": input_text}]
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inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True
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), return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=2048)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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HuatuoGPT-o1 adopts a *thinks-before-it-answers* approach, with outputs formatted as:
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```
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## Thinking
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[Reasoning process]
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## Final Response
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[Output]
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```
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# <span>📖 Citation</span>
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```
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@misc{chen2024huatuogpto1medicalcomplexreasoning,
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title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs},
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author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang},
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year={2024},
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eprint={2412.18925},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2412.18925},
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}
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```
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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