Introduction
This Verifier is built upon Qwen2.5-3B-Instruct, which supports answer verification in both English and Chinese.
This is a medical verifier designed to evaluate the correctness of LLM outputs on medical verifiable problems. Such verification can be utilized to enhance the medical reasoning capabilities of LLMs.
For details, please refer to our paper and GitHub repository. Additionally, you can explore HuatuoGPT-o1, our advanced medical LLM specializing in complex medical reasoning.
Usage
Follow the code below to utilize this model:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
# Load tokenizer and model
model_path = 'FreedomIntelligence/medical_o1_verifier_3B_Qwen2.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(
model_path, torch_dtype="auto", device_map="auto", attn_implementation="flash_attention_2", num_labels=2
)
# Evaluation template
template = """<Model Response>
{}
</Model Response>
<Reference Answer>
{}
</Reference Answer>
Your task is to evaluate the model response by comparing it to the reference answer. If the model response is correct and aligns with the reference answer, output "True" . If it is incorrect or fails to select the correct option (if options are provided), output "False" . {}"""
# Tokenize input and evaluate
LLM_response = 'The answer is 25 percentage'
ground_truth_answer = '25%'
input_batch = tokenizer([template.format(LLM_response,ground_truth_answer,tokenizer.eos_token)], return_tensors="pt").to(model.device)
logits = model(**input_batch,return_dict=True).logits
probabilities = F.softmax(logits, dim=-1)
result = "True" if probabilities[0, 1] > 0.5 else "False"
print(f"Evaluation Result: {result}")
📖 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},
}