Parameter-Efficient Fine-Tuning

Techniques such as LoRA reduce the compute, memory, and storage required to specialize foundation models for a task or domain. PEFT can make customization more practical, though teams still need high-quality training data and evaluation to verify that the adapted model improves the intended behavior.