@inproceedings{lv-etal-2023-collie,
title = "{C}o{LL}i{E}: Collaborative Training of Large Language Models in an Efficient Way",
author = "Lv, Kai and
Zhang, Shuo and
Gu, Tianle and
Xing, Shuhao and
Hong, Jiawei and
Chen, Keyu and
Liu, Xiaoran and
Yang, Yuqing and
Guo, Honglin and
Liu, Tengxiao and
Sun, Yu and
Guo, Qipeng and
Yan, Hang and
Qiu, Xipeng",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.48",
doi = "10.18653/v1/2023.emnlp-demo.48",
pages = "527--542",
abstract = "Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, and LOMO. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.",
}
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<abstract>Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, and LOMO. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.</abstract>
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%0 Conference Proceedings
%T CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
%A Lv, Kai
%A Zhang, Shuo
%A Gu, Tianle
%A Xing, Shuhao
%A Hong, Jiawei
%A Chen, Keyu
%A Liu, Xiaoran
%A Yang, Yuqing
%A Guo, Honglin
%A Liu, Tengxiao
%A Sun, Yu
%A Guo, Qipeng
%A Yan, Hang
%A Qiu, Xipeng
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lv-etal-2023-collie
%X Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has made it possible to adapt these models to specific applications for enhanced performance. However, the substantial resources required for training these models necessitate efficient solutions. This paper introduces CoLLiE, an efficient library that facilitates collaborative training of large language models using 3D parallelism, parameter-efficient fine-tuning (PEFT) methods, and optimizers such as Lion, Adan, Sophia, and LOMO. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization. CoLLiE has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. Furthermore, we provide an empirical evaluation of the correlation between model size and GPU memory consumption under different optimization methods, as well as an analysis of the throughput. Lastly, we carry out a comprehensive comparison of various optimizers and PEFT methods within the instruction-tuning context. CoLLiE is available at https://github.com/OpenLMLab/collie.
%R 10.18653/v1/2023.emnlp-demo.48
%U https://aclanthology.org/2023.emnlp-demo.48
%U https://doi.org/10.18653/v1/2023.emnlp-demo.48
%P 527-542
Markdown (Informal)
[CoLLiE: Collaborative Training of Large Language Models in an Efficient Way](https://aclanthology.org/2023.emnlp-demo.48) (Lv et al., EMNLP 2023)
ACL
- Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, and Xipeng Qiu. 2023. CoLLiE: Collaborative Training of Large Language Models in an Efficient Way. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 527–542, Singapore. Association for Computational Linguistics.