Summary of Splitlora: a Split Parameter-efficient Fine-tuning Framework For Large Language Models, by Zheng Lin et al.
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
by Zheng Lin, Xuanjie Hu, Yuxin Zhang, Zhe Chen, Zihan Fang, Xianhao Chen, Ang Li, Praneeth Vepakomma, Yue Gao
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework, called SplitLoRA, for large language model (LLM) fine-tuning on distributed private data. This framework addresses the scalability issue of LLMs by leveraging split learning (SL), which offloads primary training to a server and exchanges activation gradients with smaller data sizes. The authors demonstrate that SplitLoRA achieves target accuracy in significantly less time than state-of-the-art LLM fine-tuning frameworks, making it a promising solution for democratizing LLM fine-tuning. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to train large language models without sharing raw data. This is important because there’s not much public training data left. The approach uses something called split learning, which helps computers communicate less and trains the model faster. The researchers created a new framework called SplitLoRA that does this splitting and shows it can work well. |
Keywords
* Artificial intelligence * Fine tuning * Large language model