Foundation and relevance – an introduction to fine-tuning

Fine-tuning is the process of leveraging a model pre-trained on a large dataset and continuing the training process on a smaller, task-specific dataset to improve its performance on that task. It may also involve additional training that adapts a model to the nuances of a new domain. The latter is known as domain adaptation, which we will cover in Chapter 6. The former is typically referred to as task-specific fine-tuning, and it can be performed to accomplish several tasks, including Q&A, summarization, classification, and many others. For this chapter, we will focus on task-specific fine-tuning to improve a general-purpose model’s performance when answering questions.

For StyleSprint, fine-tuning a model to handle a specific task such as answering customer inquiries about products introduces unique challenges. Unlike generating product descriptions, which primarily involves language generation using an...