Edge 461: The Many Challenges of Kowledge Distillation
Some of the non-obvious limitations of knowledge distillation methods.
In this issue:
An overview about the challenges of knowledge distillation.
A review of Meta’s famous System 2 Distillation paper.
An introduction to the Llama Stack framework for building generative AI apps.
💡 ML Concept of the Day: The Challenges of Knowledge Distillation
Throughout this series, we have explored the different techniques and benefits of knowledge distillations for foundation models. However, distillation does not come without major drawbacks. To conclude this series, we would like to dive a bit into the challenges.
Knowledge distillation in foundation models presents several unique challenges that stem from the inherent complexity and scale of foundation models. One of the primary difficulties lies in the substantial capacity gap between the teacher (foundation model) and the student model. Foundation models often contain billions of parameters, while the goal of distillation is to create a much smaller, more efficient model. This extreme difference in model size makes it challenging to effectively transfer the rich, nuanced knowledge encoded in the teacher's vast parameter space to the more constrained student model.