  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.
