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
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IAAR-Shanghai/xVerify-1.5B-I", dtype="auto")Configuration Parsing Warning:Invalid JSON for config file config.json
Configuration Parsing Warning:Invalid JSON for config file tokenizer_config.json
π xVerify-1.5B-I
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.
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
@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, Qingchen Yu, Pengyuan Wang, Wentao Zhang, Bo Tang, Feiyu Xiong, Xinchi Li, Minchuan Yang, Zhiyu Li.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IAAR-Shanghai/xVerify-1.5B-I")