Fine-tuning & Model Adaptation

Fine-tuning and model adaptation is the art of deciding when a base model is not enough and changing its behaviour with your own data. It covers the full spectrum of adaptation techniques — parameter-efficient methods like LoRA and QLoRA, full supervised fine-tuning, and preference methods like RLHF and DPO — alongside the harder judgement of when adaptation actually beats better prompting or retrieval. The most valuable skill here is knowing the trade-offs: dataset curation, evaluation against a held-out set, the cost and serving implications, and catastrophic forgetting.