Distilling Large Language Models (LLMs)

The practice of training a compact, more resource-efficient model to replicate the functionality of a larger, more intricate LLM. This process involves training the smaller model on identical tasks as the larger counterpart, leveraging the predictions of the larger model as “soft targets” or guidance during training. By mimicking the behavior of the larger model, the distilled model aims to capture its performance while requiring fewer computational resources and memory. Distillation enables the deployment of LLMs in resource-constrained environments without compromising performance, facilitating their widespread adoption across diverse applications and platforms.