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