6 Finetuning for Classification
This chapter covers
- Introducing different LLM finetuning approaches
- Preparing a dataset for text classification
- Modifying a pretrained LLM for finetuning
- Finetuning an LLM to identify spam messages
- Evaluating the accuracy of a finetuned LLM classifier
- Using a finetuned LLM to classify new data
In previous chapters, we coded the LLM architecture, pretrained it, and learned how to import pretrained weights from an external source, such as OpenAI, into our model. In this chapter, we are reaping the fruits of our labor by finetuning the LLM on a specific target task, such as classifying text, as illustrated in figure 6.1. The concrete example we will examine is classifying text messages as spam or not spam.
Figure 6.1 A mental model of the three main stages of coding an LLM, pretraining the LLM on a general text dataset and finetuning it. This chapter focuses on finetuning a pretrained LLM as a classifier.
Figure 6.1 shows two main ways of finetuning an LLM: finetuning for classification (step 8) and finetuning an LLM to follow instructions (step 9). In the next section, we will discuss these two ways of finetuning in more detail.