Summary of Second-order Fine-tuning Without Pain For Llms:a Hessian Informed Zeroth-order Optimizer, by Yanjun Zhao et al.
Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer
by Yanjun Zhao, Sizhe Dang, Haishan Ye, Guang Dai, Yi Qian, Ivor W.Tsang
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes HiZOO, a novel zeroth-order optimizer for fine-tuning large language models (LLMs) that leverages the diagonal Hessian to enhance model convergence. The traditional backpropagation process used for LLM fine-tuning is memory-prohibitive due to GPU requirements. Zeroth-order optimizers offer a solution by using two forward passes, but they are limited by heterogeneous parameter curvatures. HiZOO addresses this limitation and provides significant improvements in model accuracy while reducing training steps. The proposed method avoids expensive memory costs and only requires one additional forward pass per step. Experimental results on various LLMs with 350M to 66B parameters demonstrate the effectiveness of HiZOO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper presents a new way to train large language models using less computer memory. Currently, training these models is too demanding for computers and requires a lot of processing power. The authors suggest a method called HiZOO that can reduce the amount of memory needed without sacrificing performance. They tested their approach on several models and found it improved results while requiring less computing power. |
Keywords
* Artificial intelligence * Backpropagation * Fine tuning
