The Sequence Knowledge #886: Demystifying Model Distillation

Knowledge distillation involves training a smaller, cheaper ‘student’ model to mimic the behavior of a larger, more expensive ‘teacher’ model. Instead of learning solely from the original dataset, the student learns from the teacher’s interpretation of that data. This practical approach allows the student model to become more capable while remaining efficient and easy to deploy.

• Knowledge distillation is a method to train a small model using a large model.
• The ‘teacher’ is a large, capable, but expensive model.
• The ‘student’ is a smaller, faster, and cheaper model.
• The student learns from the teacher’s interpretations, not just the original data.
• This allows the student to achieve higher capabilities than if trained conventionally.

https://thesequence.substack.com/p/the-sequence-knowledge-886-demystifying