About this document
Paper V Gard Lora by lecongba2024 is a document available to read on EtoBox.
The document introduces GARD, a gradient-aware rank allocation framework for Low-Rank Adaptation (LoRA) fine-tuning of large language models, which optimizes the distribution of LoRA ranks across layers based on layer-wise gradient spectral analysis. This approach addresses inefficiencies in uniform rank allocation by dynamically adjusting ranks according to the sensitivity and gradient structure of each layer while adhering to a fixed parameter budget. Empirical results demonstrate that GARD can effectivel
- Author
- lecongba2024
- Language
- EN