Collie: Collaborative Training Of Large Language Models In An Efficient Way

Lv Kai, Zhang Shuo, Gu Tianle, Xing Shuhao, Hong Jiawei, Chen Keyu, Liu Xiaoran, Yang Yuqing, Guo Honglin, Liu Tengxiao, Sun Yu, Guo Qipeng, Yan Hang, Qiu Xipeng. Arxiv 2023

[Paper] [Code]    
Applications Efficiency And Optimization Fine Tuning Has Code Pretraining Methods Tools Training Techniques

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, LOMO and AdaLomo. 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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