Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA)
Read OriginalThis technical article details the Low-Rank Adaptation (LoRA) method for fine-tuning large language models. It explains how LoRA uses low-rank matrix decomposition to make weight updates more computationally efficient compared to full fine-tuning, covering its core concepts, how it works, and its relation to techniques like PCA and SVD.
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