Instruction Fine-Tuning

In the previous chapter, we learned how to adapt a base model to a specific domain using full fine-tuning. Now, we will teach the model a more general and powerful skill: how to read, understand, and execute any instruction we provide with precision and accuracy. This is the Instruction Fine-Tuning (IFT) process. It is about transforming a base language model—often one trained simply to predict the next word—into a responsive and helpful assistant capable of effectively tackling diverse commands, questions, and prompts, even those it has not encountered before.