Fine-tuning

Fine-tuning takes a pre-trained model and continues training it on your task-specific data. In 2026 this almost always means parameter-efficient methods — LoRA or QLoRA — that update a small fraction of weights and ship as adapters rather than full models. Full fine-tunes are reserved for cases where you need deep adaptation (specialty domains, languages, or compliance). Distillation from a frontier model to a smaller production-target is the most common production fine-tuning pattern.