Knowledge Distillation How LLMs train each other
AI Summary
The video explains knowledge distillation in large language models (LLMs), illustrating its benefits and historical context. Knowledge distillation allows larger models to train smaller, faster models by leveraging the information learned in the training process. This is essential for scaling model performance without increasing inference costs. The author discusses the evolution of model compression since 2006, connecting it to the introduction of soft labels, which provide more information than hard labels during training. Furthermore, the video contrasts between proper distillation and behavioral cloning, particularly in the context of popular LLM providers like Google, Meta, and DeepSeek. Key concepts such as temperature in distillation and the challenges in implementing these techniques effectively are also explored.