Fine-Tuning

Fine-tuning takes a pre-trained model and continues training it on a smaller, task-specific dataset. This is more efficient than training from scratch and produces models specialized for particular domains or tasks. Techniques include full fine-tuning, LoRA (Low-Rank Adaptation), and QLoRA. Fine-tuning is how general models become specialized tools.