The Sequence Knowledge #886: Demystifying Model Distillation
TheSequence Jesus Rodriguez
Knowledge distillation trains a smaller, cheaper model to learn from a larger model's predictions rather than training directly on raw data. The approach involves having a high-capacity teacher model generate outputs that a smaller student model learns to replicate, combining both the original dataset and the teacher's interpretations. This enables deployment of faster and cheaper models that retain more capability than they would achieve through standard training alone.
Why it matters
Understanding the key principles of distillation in simple terms.
