Summary of Helene: Hessian Layer-wise Clipping and Gradient Annealing For Accelerating Fine-tuning Llm with Zeroth-order Optimization, by Huaqin Zhao et al.
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization
by Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Wei Liu, Xiang Li, Fei Dou, Tianming Liu, Jin Lu
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel optimizer called HELENE for fine-tuning large language models (LLMs). HELENE addresses the memory challenge posed by traditional back-propagation methods, which require extensive resources. It integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping to achieve faster and more stable convergence. The paper’s theoretical analysis shows that HELENE improves convergence rates, particularly for models with heterogeneous layer dimensions. Experimental results on RoBERTa-large and OPT-1.3B across multiple tasks demonstrate HELENE’s effectiveness, achieving up to a 20x speedup compared to MeZO, with average accuracy improvements of 1.5%. The proposed optimizer is compatible with both full parameter tuning and parameter-efficient fine-tuning (PEFT), outperforming several state-of-the-art optimizers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HELENE is a new way to make computers learn from language models more efficiently. Right now, these models use a lot of memory because they need to calculate lots of information. HELENE helps by reducing the amount of memory needed and making calculations faster. It’s especially good for big models with different types of layers. The researchers tested HELENE on two popular models (RoBERTa and OPT-1.3B) and found that it was 20 times faster than a previous method called MeZO, while also getting slightly better results. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
