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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.05563 |
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| _version_ | 1866916315747319808 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05563 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Computation and Language 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. |
| title | LLMBox: A Comprehensive Library for Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.05563 |