A Note on LoRA

• URL: http://arxiv.org/abs/2404.05086v1
• Date: Sun, 7 Apr 2024 22:00:50 GMT
• Title: A Note on LoRA
• Authors: Vlad Fomenko, Han Yu, Jongho Lee, Stanley Hsieh, Weizhu Chen,
• Abstract summary: This note extends the original LoRA paper by offering new perspectives that were not initially discussed. Without introducing new experiments, we aim to improve the understanding and application of LoRA.
• Score: 53.862304172882105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the original LoRA paper by offering new perspectives that were not initially discussed and presents a series of insights for deploying LoRA at scale. Without introducing new experiments, we aim to improve the understanding and application of LoRA.

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