Llmbox: A Comprehensive Library For Large Language Models

Tang Tianyi, Hu Yiwen, Li Bingqian, Luo Wenyang, Qin Zijing, Sun Haoxiang, Wang Jiapeng, Xu Shiyi, Cheng Xiaoxue, Guo Geyang, Peng Han, Zheng Bowen, Tang Yiru, Min Yingqian, Chen Yushuo, Chen Jie, Zhao Yuanqian, Ding Luran, Wang Yuhao, Dong Zican, Xia Chunxuan, Li Junyi, Zhou Kun, Zhao Wayne Xin, Wen Ji-rong. Arxiv 2024

[Paper] [Code]    
Efficiency And Optimization Has Code RAG Reinforcement Learning Tools Training Techniques

To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data interface that supports the flexible implementation of various training strategies, (2) a comprehensive evaluation that covers extensive tasks, datasets, and models, and (3) more practical consideration, especially on user-friendliness and efficiency. With our library, users can easily reproduce existing methods, train new models, and conduct comprehensive performance comparisons. To rigorously test LLMBox, we conduct extensive experiments in a diverse coverage of evaluation settings, and experimental results demonstrate the effectiveness and efficiency of our library in supporting various implementations related to LLMs. The detailed introduction and usage guidance can be found at https://github.com/RUCAIBox/LLMBox.

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