Summary of Fira: Can We Achieve Full-rank Training Of Llms Under Low-rank Constraint?, by Xi Chen et al.
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
by Xi Chen, Kaituo Feng, Changsheng Li, Xunhao Lai, Xiangyu Yue, Ye Yuan, Guoren Wang
First submitted to arxiv on: 2 Oct 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 The proposed plug-and-play training framework, Fira, aims to address the limitations of previous low-rank training methods for Large Language Models (LLMs) by preserving the low-rank constraint while achieving full-rank training. This is achieved through a norm-based scaling method that utilizes the scaling impact of adaptive optimizers, such as Adam, on gradient norms. Additionally, Fira includes a norm-growth limiter to smooth sudden gradient rises and prevent loss spikes during optimization. Experimental results show that Fira outperforms existing methods, LoRA and GaLore, and achieves performance comparable to or better than full-rank training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fira is a new way to train Large Language Models (LLMs) using less memory. Usually, when we train these models, we need lots of memory to store the information. Fira helps by reducing this memory usage while still getting good results. It does this by adjusting how the model’s weights and gradients are updated during training. This is important because it allows us to train bigger and more powerful language models without running out of memory. |
Keywords
* Artificial intelligence * Lora * Optimization
