Model Adaptation and Fine-Tuning

Describe transfer learning in the context of LLMs and why pre-training followed by fine-tuning is effective.

Explain supervised fine-tuning (SFT) and how it adapts a pre-trained model to specific tasks or instruction-following behavior.

Explain Low-Rank Adaptation (LoRA), detailing how it freezes pre-trained weights and injects trainable low-rank matrices.

Compare full fine-tuning with parameter-efficient fine-tuning (PEFT) methods like LoRA in terms of computational cost, memory, and performance tradeoffs.