Parameter-efficient fine tuning is a set of techniques that adapt large models to new tasks while changing only a small subset of parameters or adding compact modules. Methods in this family reduce compute and storage requirements when compared to full model re-training, making updates cheaper and faster.
PEFT approaches let organizations deploy many task specific variants without duplicating entire model checkpoints. They require care to maintain stability and must be combined with testing and monitoring to ensure adapted models still meet safety and performance needs.
