Summary of Gradient Weight-normalized Low-rank Projection For Efficient Llm Training, by Jia-hong Huang et al.
Gradient Weight-normalized Low-rank Projection for Efficient LLM Training
by Jia-Hong Huang, Yixian Shen, Hongyi Zhu, Stevan Rudinac, Evangelos Kanoulas
First submitted to arxiv on: 27 Dec 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 paper introduces Gradient Weight-Normalized Low-Rank Projection (GradNormLoRP), a novel approach to improve the efficiency of Large Language Models (LLMs) in both training and fine-tuning. GradNormLoRP enhances parameter and memory efficiency while maintaining comparable performance to full fine-tuning, making it suitable for large LLMs like LLaMA 7B. The method normalizes the weight matrix to improve gradient conditioning, and applies low-rank approximations to the weight and gradient matrices, reducing memory usage during training by up to 89.5%. The authors demonstrate the effectiveness of GradNormLoRP through extensive experiments on various tasks, including fine-tuning the RoBERTa model on GLUE tasks, achieving an average score of 80.65. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to train and fine-tune large language models like LLaMA 7B using consumer-level GPUs like the NVIDIA RTX 4090 without extra costs. The new approach, called Gradient Weight-Normalized Low-Rank Projection (GradNormLoRP), helps with training by making it more efficient. |
Keywords
» Artificial intelligence » Fine tuning » Llama
