---
base_model:
- meta-llama/Llama-3.2-3B-Instruct
language:
- en
- zh
license: cc-by-nc-nd-4.0
tags:
- instruction-finetuning
- reasoning
- evaluation
library_name: transformers
pipeline_tag: text-generation
inference: false
---
🔍 xVerify-3B-Ia
xVerify is an evaluation tool fine-tuned from a pre-trained large language model, designed specifically for objective questions with a single correct answer. It accurately extracts the final answer from lengthy reasoning processes and efficiently identifies equivalence across different forms of expressions.
This model was introduced in the paper [xVerify: Efficient Answer Verifier for Reasoning Model Evaluations](https://huggingface.co/papers/2504.10481).
---
## ✨ Key Features
### 📊 Broad Applicability
Suitable for various objective question evaluation scenarios including math problems, multiple-choice questions, classification tasks, and short-answer questions.
### ⛓️ Handles Long Reasoning Chains
Effectively processes answers with extensive reasoning steps to extract the final answer, regardless of complexity.
### 🌐 Multilingual Support
Primarily handles Chinese and English responses while remaining compatible with other languages.
### 🔄 Powerful Equivalence Judgment
- ✓ Recognizes basic transformations like letter case changes and Greek letter conversions
- ✓ Identifies equivalent mathematical expressions across formats (LaTeX, fractions, scientific notation)
- ✓ Determines semantic equivalence in natural language answers
- ✓ Matches multiple-choice responses by content rather than just option identifiers
---
## 🚀 Usage
For detailed instructions on installation and batch evaluation using the `xVerify` framework, please refer to the [official GitHub repository](https://github.com/IAAR-Shanghai/xVerify).
Since this is a Llama-based model, you can also use it directly with the `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "IAAR-Shanghai/xVerify-3B-Ia"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
# Your input prompt logic here
```
---
## 📚 Citation
```bibtex
@article{xVerify,
title={xVerify: Efficient Answer Verifier for Reasoning Model Evaluations},
author={Ding Chen and Qingchen Yu and Pengyuan Wang and Wentao Zhang and Bo Tang and Feiyu Xiong and Xinchi Li and Minchuan Yang and Zhiyu Li},
journal={arXiv preprint arXiv:2504.10481},
year={2025},
}
```
