What are the best practices for AI distillation?

What are the best practices for AI distillation?

AI distillation involves transferring knowledge from a large model to a smaller one. Key practices include selecting the right teacher model, ensuring high-quality training data, and using appropriate loss functions.

How do I choose the right teacher model?

Choose a teacher model that is well-trained and performs well on your specific task. It should have a good balance between complexity and performance.

What kind of data should I use for training?

Use high-quality, diverse, and representative data that reflects the tasks the distilled model will perform. This helps in retaining essential knowledge.

Are there specific loss functions that work best?

Yes, common loss functions for distillation include Kullback-Leibler divergence and mean squared error, as they help in aligning the outputs of the teacher and student models.

Can you give me an example of a successful AI distillation?

A notable example is the distillation of BERT into DistilBERT, which retains 97% of BERT's language understanding while being 60% smaller.