{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/llmbox-a-comprehensive-library-for-large","title":"LLMBox: A Comprehensive Library for Large Language Models","arxiv_id":"2407.05563","date":"2024-07-08","proceeding":null,"authors":["Tianyi Tang","Yiwen Hu","Bingqian Li","Wenyang Luo","Zijing Qin","Haoxiang Sun","Jiapeng Wang","Shiyi Xu","Xiaoxue Cheng","Geyang Guo","Han Peng","Bowen Zheng","Yiru Tang","Yingqian Min","Yushuo Chen","Jie Chen","Yuanqian Zhao","Luran Ding","Yuhao Wang","Zican Dong","Chunxuan Xia","Junyi Li","Kun Zhou","Wayne Xin Zhao","Ji-Rong Wen"],"abstract":"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. 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