QServe-benchmarks

# QServe benchmarks

This huggingface repository contains configurations and tokenizer files for all models benchmarked in our [QServe](https://github.com/mit-han-lab/qserve) project:

- Llama-3-8B
- Llama-2-7B
- Llama-2-13B
- Llama-2-70B
- Llama-30B
- Mistral-7B
- Yi-34B
- Qwen1.5-72B

Please clone this repository if you wish to run our QServe benchmark code without cloning full models.

Please consider citing our paper if it is helpful:

```
@article{lin2024qserve,
title={QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving},
author={Lin*, Yujun and Tang*, Haotian and Yang*, Shang and Zhang, Zhekai and Xiao, Guangxuan and Gan, Chuang and Han, Song},
year={2024}
}
```

# QServe 基准测试集

本Hugging Face仓库包含了我们[QServe](https://github.com/mit-han-lab/qserve)项目中所有参与基准测试的模型的配置文件与分词器文件：
- Llama-3-8B
- Llama-2-7B
- Llama-2-13B
- Llama-2-70B
- Llama-30B
- Mistral-7B
- Yi-34B
- Qwen1.5-72B

若您希望运行QServe基准测试代码且无需克隆完整模型，请克隆本仓库。

若本项目对您的研究有所帮助，请考虑引用我们的论文：

@article{lin2024qserve,
title={QServe：面向高效大语言模型（LLM）服务的W4A8KV4量化与系统协同设计},
author={Lin*, Yujun and Tang*, Haotian and Yang*, Shang and Zhang, Zhekai and Xiao, Guangxuan and Gan, Chuang and Han, Song},
year={2024}
}

搜集汇总

背景与挑战

背景概述

该数据集提供了QServe项目中基准测试模型的配置和分词器文件，包括Llama、Mistral等多个系列模型。用户可克隆此仓库以运行QServe基准测试代码，无需下载完整模型。