Build a Large Language Model (From Scratch)

6 Fine-tuning for classification

This chapter covers

• Introducing different LLM fine-tuning approaches
• Preparing a dataset for text classification
• Modifying a pretrained LLM for fine-tuning
• Fine-tuning an LLM to identify spam messages
• Evaluating the accuracy of a fine-tuned LLM classifier
• Using a fine-tuned LLM to classify new data

So far, we have coded the LLM architecture, pretrained it, and learned how to import pretrained weights from an external source, such as OpenAI, into our model. Now we will reap the fruits of our labor by fine-tuning the LLM on a specific target task, such as classifying text. The concrete example we examine is classifying text messages as “spam” or “not spam.” Figure 6.1 highlights the two main ways of fine-tuning ...