Abstract
<PRE_TAG><PRE_TAG>LoRA (Low-Rank Adaptation)</POST_TAG></POST_TAG> has emerged as a preferred method for efficiently adapting <PRE_TAG><PRE_TAG>Large Language Models (LLMs)</POST_TAG></POST_TAG> 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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