supervised fine-tuning

Supervised fine-tuning (SFT) adapts a pretrained model on labeled instruction-response pairs to improve task-specific performance, like following directives. It aligns general models to user needs with minimal data and precedes RLHF, enhancing instruction adherence and reducing hallucinations.