What is Model Distillation?

Model distillation is a technique for training a smaller "student" model to mimic the behavior of a larger "teacher" model, transferring much of the larger model's capability into a more efficient package.

• The student model is trained to match the teacher's output probabilities, not just its final answers
• Produces a smaller model that runs faster and cheaper than the original
• Usually results in some capability trade-off compared to the full-sized teacher model, though a well-distilled model can retain much of its usefulness

Distillation is one of several complementary approaches, alongside quantization, for making a powerful but expensive model practical to deploy at scale.

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