Knowledge Distillation Using Qwen-7B and GPT-2

Introduction

In the age of large language models (LLMs) like GPT-4, Qwen, and PaLM, the demand for massive compute resources and memory to train, fine-tune, and deploy these models is continuously growing. While these large models offer unprecedented capabilities in various NLP tasks, they are often impractical for deployment in resource-constrained environments. This is where knowledge distillation comes into play.

Knowledge distillation is a method of transferring the “knowledge” from a larger, more capable model (known as the teacher) to a smaller, more efficient model (known as the student). The idea is to allow the student model to mimic the teacher’s behaviour, learning from both the teacher’s predictions and the ground truth labels. This results in a smaller model that retains much of the teacher’s performance, but with reduced computational and memory overhead.

In this guide, we’ll walk through an in-depth implementation of knowledge distillation using Qwen-7B as the teacher and GPT-2 as the student. We’ll delve into the theoretical foundation behind knowledge distillation, break down the code with rich code snippets, and discuss the practical applications of this approach. By the end, you’ll have a deep understanding of how to implement knowledge distillation effectively, and how it can benefit…