Having prepared the necessary datasets for adaptation, we now turn to the method of modifying the pre-trained Large Language Model itself. This chapter focuses on full parameter fine-tuning, the approach where every weight ($\theta$) within the original model is updated based on the new task-specific data.

You will learn the core mechanics of this process, including:

• Setting up and configuring the complete training loop for fine-tuning.
• Strategies for selecting appropriate hyperparameters, such as learning rates ($\eta$) and batch sizes.
• Techniques like regularization to prevent overfitting on the target dataset.
• Methods for managing the substantial computational resources required.
• Best practices for checkpointing training progress and resuming jobs.

By the end of this chapter, you will understand the complete workflow for full fine-tuning and gain practical experience through a hands-on implementation, setting the stage for understanding more resource-efficient methods later.