Summary of Mos: Unleashing Parameter Efficiency Of Low-rank Adaptation with Mixture Of Shards, by Sheng Wang et al.
MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards
by Sheng Wang, Liheng Chen, Pengan Chen, Jingwei Dong, Boyang Xue, Jiyue Jiang, Lingpeng Kong, Chuan Wu
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A lightweight fine-tuning method for large language models is proposed to mitigate the significant GPU memory overhead caused by scaling. The approach, called Mixture of Shards (MoS), combines inter-layer and intra-layer sharing schemes with four differentiation strategies. MoS achieves approximately 8x parameter savings in a standard LoRA setting while retaining the advantages of LoRA. The ablation study confirms the significance of each component. This research provides insights into parameter sharing and MoS, which may guide future developments of more efficient finetuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting bigger! To make them work on smaller computers, scientists developed a way to reuse old information instead of creating new models. They called it Mixture of Shards (MoS). It’s like having a team of experts working together, sharing their knowledge and skills to get the job done quickly and efficiently. This method saved a lot of computer memory and can help make language models even better. |
Keywords
» Artificial intelligence » Fine tuning » Lora
