Summary of Adarankgrad: Adaptive Gradient-rank and Moments For Memory-efficient Llms Training and Fine-tuning, by Yehonathan Refael et al.
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
by Yehonathan Refael, Jonathan Svirsky, Boris Shustin, Wasim Huleihel, Ofir Lindenbaum
First submitted to arxiv on: 23 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 In this paper, researchers tackle the challenges associated with training and fine-tuning large language models (LLMs), which require significant memory and computational resources due to the increasing size of model weights and optimizer states. They propose a new approach inspired by the phenomenon that the rank of estimated layer gradients gradually decreases during training, eventually approaching rank one. This method involves adaptively reducing the rank of gradients during Adam optimization steps using an efficient online-updating low-rank projections rule. The authors also present a randomized SVD scheme for efficiently finding the projection matrix. This technique enables full-parameter fine-tuning with adaptive low-rank gradient updates, significantly reducing memory requirements while improving model performance in both pretraining and fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists find a way to make big language models work more efficiently on computers. They show that as these models learn, they start using fewer “building blocks” (called gradients) to update their weights. The researchers use this discovery to create a new method for training and fine-tuning language models that uses less memory and computation. This approach is better than other methods because it allows the model to learn without needing too many resources. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Pretraining
