Full fine-tuning (FT) is commonly employed to tailor general pretrained models for specific downstream tasks. To reduce the training cost, parameter-efficient…
Full fine-tuning (FT) is commonly employed to tailor general pretrained models for specific downstream tasks. To reduce the training cost, parameter-efficient fine-tuning (PEFT) methods have been introduced to fine-tune pretrained models with a minimal number of parameters. Among these, Low-Rank Adaptation (LoRA) and its variants have gained considerable popularity because they avoid additional…
