Hu, Edward J.Shen, YelongWallis, PhillipAllen‑Zhu, ZeyuanLi, YuanzhiWang, SheanWang, LuChen, Weizhu2025-06-022025-06-022021-06-17https://arxiv.org/abs/2106.09685http://data.inu.ac.kr/handle/123456789/1959모델 파라미터를 고정한 상태에서 LoRA라는 저차원 학습 구조를 주입하여, 대규모 언어모델의 파인튜닝 효율성과 메모리 사용을 크게 개선한 방법을 제안합니다. 10,000배 적은 학습 파라미터와 3배 낮은 메모리 사용량을 보입니다 ©2021 Microsoft ResearchAn important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at this https URL.en-USLoRALow-Rank AdaptationEfficient Fine-TuningTransformerNLPArticle