Summary

In this chapter on performance optimization for LLMs, advanced techniques were introduced to enhance efficiency without compromising effectiveness. It discussed several methods, starting with quantization, which compresses models by reducing bit precision, thus shrinking model size and accelerating inference – a crucial phase where a model generates predictions. This involves a trade-off between model size and speed against accuracy, with tools such as quantization-aware training used to balance these aspects.

Pruning was another method discussed, focusing on eliminating less critical weights from LLMs to make them leaner and faster, which is particularly beneficial for devices with limited processing capabilities. Knowledge distillation was also covered, which involves transferring insights from a large, complex model (teacher) to a smaller, simpler one (student), retaining performance while ensuring that the model is lightweight enough for real-time applications...