Fine-tuning for language, text, and everything in between

At this point in the book, we’ve already covered a lot of ground. We’ve focused primarily on the pretraining aspect, looking at everything from finding the right use cases and datasets to defining your loss functions, preparing your models and datasets, defining progressively larger experiments, parallelization basics, working with GPUs, finding the right hyperparameters, advanced concepts, and more! Here, we’ll explore how to make your models even more targeted to a specific application: fine-tuning.

Presumably, if you are embarking on a large-scale training project, you might have one of the following goals:

• You might be pretraining your own foundation model
• You might be designing a novel method for autonomous vehicles
• You might be classifying and segmenting 3D data, such as in real estate or manufacturing
• You might be training a large text classification model or designing a novel...