92 lines
2.7 KiB
Markdown
92 lines
2.7 KiB
Markdown
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---
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license: mit
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datasets:
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- hongzhouyu/FineMed-SFT
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language:
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- en
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- zh
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base_model:
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- meta-llama/Llama-3.1-8B
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library_name: transformers
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tags:
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- medical
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---
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<div align="center">
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<h1>
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FineMedLM
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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/hongzhouyu/FineMed" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2501.09213" target="_blank">Paper</a>
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</div>
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# <span>Introduction</span>
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**FineMedLM** is a medical chat LLM trained via SFT on meticulously crafted synthetic data. By further applying DPO, the model acquires enhanced deep reasoning capabilities, culminating in the development of [FineMedLM-o1](https://huggingface.co/hongzhouyu/FineMedLM-o1).
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For more information, visit our GitHub repository.
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# <span>Usage</span>
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You can use FineMedLM in the same way as `Llama-3.1-8B-Instruct`:
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（⚠️**Note**: Please use the system prompt we provide to achieve better inference results）
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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main_model_name = "hongzhouyu/FineMedLM"
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model = AutoModelForCausalLM.from_pretrained(main_model_name, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(main_model_name)
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prompt = (
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"""The following are multiple choice questions (with answers) about health. Think step by step and then finish your answer with "the answer is (X)" where X is the correct letter choice.
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Question:
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Polio can be eradicated by which of the following?
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Options:
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A. Herbal remedies
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B. Use of antibiotics
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C. Regular intake of vitamins
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D. Administration of tetanus vaccine
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E. Attention to sewage control and hygiene
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F. Natural immunity acquired through exposure
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G. Use of antiviral drugs
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Answer: Let's think step by step.
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"""
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)
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messages = [
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{"role": "system", "content": "You are a helpful professional doctor. The user will give you a medical question, and you should answer it in a professional way."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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print(text)
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model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
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print("-----start generate-----")
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=2048,
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eos_token_id=tokenizer.eos_token_id
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)
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answer = tokenizer.decode(generated_ids[0], skip_special_tokens=False)
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print(answer)
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```
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# <span>Citation</span>
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```
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@misc{yu2025finemedlmo1enhancingmedicalreasoning,
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title={FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training},
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author={Hongzhou Yu and Tianhao Cheng and Ying Cheng and Rui Feng},
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year={2025},
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eprint={2501.09213},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2501.09213},
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}
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```
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