Text Generation
Transformers
Safetensors
English
Chinese
instruction-finetuning
reasoning
evaluation
Instructions to use IAAR-Shanghai/xVerify-1.5B-I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IAAR-Shanghai/xVerify-1.5B-I with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IAAR-Shanghai/xVerify-1.5B-I")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IAAR-Shanghai/xVerify-1.5B-I", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use IAAR-Shanghai/xVerify-1.5B-I with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IAAR-Shanghai/xVerify-1.5B-I" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IAAR-Shanghai/xVerify-1.5B-I", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IAAR-Shanghai/xVerify-1.5B-I
- SGLang
How to use IAAR-Shanghai/xVerify-1.5B-I with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "IAAR-Shanghai/xVerify-1.5B-I" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IAAR-Shanghai/xVerify-1.5B-I", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "IAAR-Shanghai/xVerify-1.5B-I" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IAAR-Shanghai/xVerify-1.5B-I", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IAAR-Shanghai/xVerify-1.5B-I with Docker Model Runner:
docker model run hf.co/IAAR-Shanghai/xVerify-1.5B-I
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base_model:
- Qwen/Qwen2.5-1.5B-Instruct
language:
- en
- zh
license: cc-by-nc-nd-4.0
library_name: transformers
pipeline_tag: text-generation
tags:
- instruction-finetuning
- reasoning
- evaluation
inference: false
---
<h1 align="center">
π xVerify-1.5B-I
</h1>
<p align="center">
<div style="display: flex; justify-content: center; gap: 10px;">
<a href="https://github.com/IAAR-Shanghai/xVerify">
<img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub"/>
</a>
<a href="https://huggingface.co/IAAR-Shanghai/xVerify-1.5B-I">
<img src="https://img.shields.io/badge/π€%20Hugging%20Face-xVerify--1.5B--I-yellow" alt="Hugging Face"/>
</a>
<a href="https://huggingface.co/papers/2504.10481">
<img src="https://img.shields.io/badge/Paper-HF%20Papers-red" alt="Paper"/>
</a>
</div>
</p>
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 was introduced in the paper [xVerify: Efficient Answer Verifier for Reasoning Model Evaluations](https://huggingface.co/papers/2504.10481).
The model accurately extracts the final answer from lengthy reasoning processes and efficiently identifies equivalence across different forms of expressions, helping to evaluate reasoning models that adopt slow-thinking strategies.
---
## β¨ Key Features
### π Broad Applicability
Suitable for various objective question evaluation scenarios including math problems, multiple-choice questions, classification tasks, and short-answer questions.
### βοΈ Handles Long Reasoning Chains
Effectively processes answers with extensive reasoning steps to extract the final answer, regardless of complexity.
### π Multilingual Support
Primarily handles Chinese and English responses while remaining compatible with other languages.
### π Powerful Equivalence Judgment
- **Basic Transformations**: Recognizes letter case changes and Greek letter conversions.
- **Mathematical Expressions**: Identifies equivalent expressions across formats like LaTeX, fractions, and scientific notation.
- **Semantic Equivalence**: Determines if natural language answers align with the correct reference.
- **Advanced Multiple-Choice**: Matches responses by content rather than just option identifiers.
---
## π 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},
}
```
---
*Authors: [Ding Chen](https://huggingface.co/Hush-cd), [Qingchen Yu](https://huggingface.co/Duguce), [Pengyuan Wang](https://huggingface.co/deflinhec), Wentao Zhang, Bo Tang, Feiyu Xiong, Xinchi Li, Minchuan Yang, Zhiyu Li.* |