Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Fomenko, Vlad, Yu, Han, Lee, Jongho, Hsieh, Stanley, Chen, Weizhu
Institution: Microsoft (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4394672424