Summary of Switchlora: Switched Low-rank Adaptation Can Learn Full-rank Information, by Kaiye Zhou et al.
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information
by Kaiye Zhou, Shucheng Wang, Jun Xu
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce SwitchLoRA, a novel parameter-efficient training technique for large language models. The authors build upon existing techniques like LoRA and ReLoRA to optimize memory usage during the fine-tuning phase. However, they find that directly applying these methods during the pre-training phase leads to poor performance due to premature implementation of low-rank training. To address this challenge, SwitchLoRA updates the low-rank subspace incrementally, targeting only a few dimensions at a time to minimize the impact on optimizer states. This allows for higher update frequencies and enables the updated parameters to closely mimic full-rank behavior during pre-training. The authors demonstrate that SwitchLoRA surpasses full-rank training, reducing perplexity by 0.22 points on the LLaMA 1.3B model while cutting communication overhead by 54% and memory usage by 13%. Additionally, after fine-tuning, the SwitchLoRA pre-trained model shows an average accuracy gain of about 1% over the full-rank pre-trained model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a new way to train language models so they can use less memory and communicate faster. Right now, there are ways to do this during fine-tuning, but it doesn’t work well when you’re training the model from scratch. The authors came up with a solution called SwitchLoRA that updates the model’s weights gradually, which helps it learn better. They tested their idea and found that it performs even better than traditional methods while using less memory and communicating faster. This could be important for big language models like the ones used in chatbots or language translation apps. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Lora » Parameter efficient » Perplexity » Translation
