model-distillation

Techniques for transferring knowledge from large teacher models into smaller, faster student models that keep much of the original capability at a fraction of the cost. Coverage spans practical training methods, evaluation trade-offs, and deployment gains, alongside the messy legal and licensing questions that arise when distilling from proprietary APIs. Expect analysis of where distillation helps, where it breaks down, and the Terms of Service disputes shaping who can build on whose outputs.