Parameter-efficient fine-tuning (PEFT) reduces computational cost and memory by updating only a subset of a pretrained model's parameters while freezing the rest. It enables faster training and lower resource usage, making fine-tuning feasible for large models like GPT-3, BERT, or T5.
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