Fine-tuning a completion model

Fine-tuning is the process of taking a pre-trained model and further adapting it to a specific task or dataset. The goal is typically to take an original model that has been trained on a large, general dataset and apply it to a more specialized domain or to improve its performance on a specific type of data.

We previously saw a version of fine-tuning in the first recipe within Chapter 1, where we added examples of outputs in the messages parameter to fine-tune the output response. In this case, the model had not technically been fine-tuned – we instead performed few-shot learning, where we gave examples of the output within the prompt itself to the Chat Completion model. Fine-tuning, however, is a process where a whole new subset Chat Completion model is created with training data (inputs and outputs).

In this recipe, we will explore how to fine-tune a model and execute that fine-tuned model. Then, we will discuss the benefits and drawbacks...