Fine-Tuning Models with OpenAI

Concluding Part 2, this chapter explores how to train large language models (LLMs) to specialize in completing a specific task. This is achieved by fine-tuning the model’s parameters, known as weights, through the OpenAI platform. This is quite like the prompt engineering strategy few-shot learning, sometimes referred to as in-context learning, which we introduced earlier in the book as a technique for helping the model to specialize in a particular domain. Fine-tuning, however, is best suited for larger-scale projects where we want to separate the specialization aspect from the actual prompt.

We will delve into a use case where this specialization of the model is required to produce code while avoiding comments clutter. We will examine how outputs from the GPT-4o mini model may be programmatically correct but fail to follow our style guidelines and requirements for concise explanations and code comments. We will construct a fine-tuning JSONL...