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@@ -17,7 +17,8 @@ pipeline_tag: text-generation
# UNO-Scorer: A Unified General Scoring Model for UNO-Bench
<div align="center">
[![Project_Page](https://img.shields.io/badge/Project-Page-yellow)](https://meituan-longcat.github.io/UNO-Bench)
[![Paper](https://img.shields.io/badge/Paper-Arxiv%3A2510.18915-red)](https://arxiv.org/abs/2510.18915)
[![Base Model](https://img.shields.io/badge/Base%20Model-Qwen3--14B-blue)](https://huggingface.co/Qwen/Qwen3-14B)
[![License](https://img.shields.io/badge/License-Apache%202.0-green)]()
@@ -53,7 +54,7 @@ pip install -U transformers
python3 test_scorer_hf.py --model-name /path/to/your/model
```
We recommend using vLLM for inference as it offers significantly better efficiency compared to the standard HuggingFace approach. Please follow the steps below to set up the environment and run the inference script provided in our official repository.
We recommend using vLLM for inference as it offers significantly better efficiency compared to the standard HuggingFace approach. Please follow the steps below to set up the environment and run the inference script provided in our official repository [UNO-Bench](https://github.com/meituan-longcat/UNO-Bench).
### 1. Clone the Repository
@@ -61,7 +62,7 @@ First, clone the UNO-Bench repository:
```bash
git clone https://github.com/meituan-longcat/UNO-Bench.git
cd UNO-Bench/uno_eval
cd UNO-Bench/uno-eval
```
### 2. Install Dependencies
@@ -75,7 +76,7 @@ pip install -r requirement.txt
We provide an example script based on **vLLM** for efficient model inference. You can run the following command to test the scorer:
```bash
bash examples/test_scorer.sh
bash examples/test_scorer_vllm.sh
```
### 4. Adapt Your Reference Answer