Large language model fine-tuning

Consider the computational costs - fine-tuning large models requires significant resources.

LoRA (Low-Rank Adaptation) is a game-changer for efficient fine-tuning of large models.

Data quality is crucial - clean, domain-specific data often matters more than quantity.

Instruction tuning and RLHF (Reinforcement Learning from Human Feedback) are essential for alignment.