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 , are adjusted based on the calculated loss from your dataset. The update for each training step follows the general form:
Here, represents the learning rate, and 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:
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.
3.1 The Mechanics of Full Fine-Tuning
3.2 Architectural Considerations for Full Fine-Tuning
3.3 Managing Computational Resources
3.4 Configuring Training Arguments and Hyperparameters
3.5 Monitoring Training: Loss and Metrics
3.6 Saving and Loading Fine-Tuned Models
3.7 Practice: Full Fine-Tuning on a Small-Scale Model
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