With a prepared dataset, you are ready to begin the training process. This chapter introduces full parameter fine-tuning, a method where every parameter in the pre-trained model is updated to adapt to your new task. This approach directly modifies the model's entire set of weights.

The core of this process is gradient descent. Model parameters, denoted as $\theta$, are adjusted based on the calculated loss from your dataset. The update for each training step follows the general form:

$\theta_{new} = \theta_{old} - \eta \cdot \nabla L(\theta_{old})$

Here, $\eta$ represents the learning rate, and $\nabla L(\theta_{old})$ is the gradient of the loss function with respect to the model's parameters. Unlike more efficient methods, this update is applied to all of the model's millions or billions of parameters.

Throughout this chapter, we will cover the practical aspects of implementing this technique. You will learn to:

• Select a suitable base model architecture.
• Manage the significant computational load and memory usage.
• Configure training arguments, including learning rate, batch size, and number of epochs.
• Interpret training and validation loss curves to diagnose performance.
• Save the resulting model and load it correctly for inference.

The chapter concludes with a hands-on exercise where you will apply these steps to fine-tune a small-scale model from start to finish.