Adapting Models Efficiently

Most of the value you’ll ship with LLMs comes from adapting a capable base model to your data and constraints – not from training from scratch. In Python, the winning recipe is to quantize the base to fit memory, attach a small set of trainable parameters (LoRA/adapters), fine-tune with steady tooling, and (when needed) distill knowledge into something smaller. This chapter shows the patterns and the trade-offs you’ll actually feel in practice.