Chapter 5: The Training Process

With the data correctly formatted, the environment configured, and the parameter-efficient adapters initialized, the infrastructure for fine-tuning is fully assembled. The next phase involves executing the training loop where the model learns from the instruction dataset. This process requires balancing the computational limits of your hardware while ensuring the model converges efficiently.

In this section, you will define the training arguments that dictate how the optimization process runs. You will configure batch sizes and accumulation steps to control memory usage. Next, you will set up learning rates and schedulers. Setting a proper learning rate schedule ensures the weight updates become smaller as training progresses. A standard approach applies a decay formula to stabilize learning over time:

$\alpha_{t} = \alpha_{initial} \cdot \beta^{t}$

Here, $\alpha_{t}$ is the learning rate at a given step, $t$ represents the current training step, and $\beta$ is the decay factor.

You will also implement checkpointing mechanisms to save intermediate model states. Saving progress at regular intervals prevents data loss if a hardware fault occurs or a process is interrupted. Alongside state management, you will track validation metrics and training loss. Monitoring these values allows you to detect when the model stops learning or begins memorizing the training data instead of generalizing. Finally, you will bring these components together to execute the complete training loop on a subset of your data, generating a set of fine-tuned weights ready for evaluation.