Summary of Dare the Extreme: Revisiting Delta-parameter Pruning For Fine-tuned Models, by Wenlong Deng et al.
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models
by Wenlong Deng, Yize Zhao, Vala Vakilian, Minghui Chen, Xiaoxiao Li, Christos Thrampoulidis
First submitted to arxiv on: 12 Oct 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 This research paper presents solutions to address the issue of redundancy and increased response times in applications utilizing multiple open-source fine-tuned models. The authors focus on delta-parameter pruning (DPP) methods, particularly the random drop and rescale (DARE) approach proposed by Yu et al. They highlight two key reasons why DARE fails when dealing with large pruning rates or magnitude of delta parameters: an excessively large rescaling factor and high mean and variance in the delta parameters. To overcome these limitations, the authors introduce DAREx (DARE the eXtreme), which features two algorithmic improvements: DAREx-q, a rescaling factor modification that boosts performance at high pruning rates, and DAREx-L2, which combines DARE with AdamR for delta regularization before DPP. The paper also explores the application of importance-based pruning techniques within DPP, showing they outperform random-based methods when dealing with large delta parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to solve a problem in machine learning where many models are used together, causing extra work and slow responses. Researchers looked at a way to make these models more efficient by removing some of the unimportant parts. They found that a method called DARE (random drop and rescale) wasn’t working well when they tried to remove too much or when the differences between the original model and the fine-tuned model were very large. To fix this, they created a new method called DAREx, which makes some changes to how the important parts are removed. They also found that using another technique called importance-based pruning can help make things more efficient. |
Keywords
» Artificial intelligence » Machine learning » Pruning » Regularization
