Efficient Learning through Model Distillation

This chapter explores the concept of model distillation in artificial intelligence, where knowledge from larger, pre-trained models is used to train smaller models more effectively. It highlights the process of using synthetic data, the advantages of open-source models, and the challenges related to compute requirements and operational complexities in data centers.

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