Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA)
Read OriginalThis technical article explains Low-Rank Adaptation (LoRA), a parameter-efficient finetuning technique for large language models. It covers how LoRA uses low-rank matrix decomposition to reduce computational costs during finetuning, compares it to other methods, and discusses the underlying concepts from linear algebra that make this approach effective for adapting pretrained models.
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