Summary of Adafish: Fast Low-rank Parameter-efficient Fine-tuning by Using Second-order Information, By Jiang Hu et al.
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information
by Jiang Hu, Quanzheng Li
First submitted to arxiv on: 19 Mar 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 The paper introduces an efficient fine-tuning algorithm called AdaFish, which accelerates the training process within low-rank decomposition-based frameworks for natural language processing and computer vision tasks. Building on recent advancements in large-scale pretrained models, AdaFish leverages parameter-efficient methods to reduce computational resources and memory requirements. By exploiting the low-rank or small-scaled nature of the generalized Fisher information matrix, which is equivalent to the Hessian matrix, AdaFish achieves global convergence while maintaining competitive performance with state-of-the-art AdamW. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AdaFish is a new algorithm that helps train models faster and more efficiently. It uses ideas from large-scale models to make training smaller models easier. The key insight is that some parts of the model don’t need as much attention, so AdaFish can skip those parts. This makes training take less time and uses less computer power. The paper shows that AdaFish works well and is competitive with other popular algorithms like AdamW. |
Keywords
* Artificial intelligence * Attention * Fine tuning * Natural language processing * Parameter efficient
