Having gathered and processed massive text datasets, the next step involves deciding how to effectively feed this data to the model during the long training process. Simply shuffling all data together might not be optimal. The composition and order in which data are presented can significantly influence the model's learning process and final capabilities.

This chapter introduces techniques for sampling data during training. You will learn about:

• The significance of mixing data from different sources and how to compose these mixtures.
• Methods for assigning weights to different data sources to emphasize or de-emphasize certain types of text.
• Using temperature-based sampling to control the distribution from which data batches are drawn.
• The concept of curriculum learning, where data might be presented in a specific order, potentially from easier to more complex examples.
• Strategies like data pacing and annealing, which involve dynamically changing the data mixture over the training duration.

Understanding these sampling strategies is key to guiding the training process and shaping the abilities of the resulting large language model.