Understanding Training Steps and Optimal Epoch Selection in Fine-Tuning

Below is some clarification on your questions:

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What Does “Step” Mean and Can I Customize It?

• Step Definition:
  A "step" represents an iteration in the training process where a mini‑batch is processed. It’s computed as the number of training samples divided by the batch size. When a checkpoint is created, it logs the step number indicating how far into training it was generated.

• Customization:
  You cannot directly customize the step number. It is determined by the size of your dataset and the batch size and is inherent to the training loop.

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How to Pick the Best Epoch and Do I Need to Retrain with That Many Epochs?

• Selecting the Best Epoch:
  After training (e.g., 15 epochs), you can evaluate checkpoints (or sample outputs) on your validation/test set to compare performance. The metric you care about (accuracy, quality, etc.) will help you decide which epoch delivered the best results. Typically, you examine the checkpoints’ metrics—each associated with a training step and epoch—to choose the most effective one.

• Re-training with the Best Epoch Count:
  No, you do not need to re-train the model with a hyperparameter value equal to the ‘best’ epoch number. The epochs hyperparameter sets the total number of epochs for training. Instead, you can use the checkpoint corresponding to the best-performing epoch for your deployment.

  • If you decide to run another fine-tuning job, you might consider adjusting the total number of epochs based on your previous observations. But for using the model now, simply select the checkpoint (e.g., from epoch 8) that worked best.

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Summary

• Step: A built-in training iteration count; not customizable by the user.
• Choosing the Best Epoch: Use your validation set to compare checkpoints. Once you determine, say, epoch 8 is optimal, you can deploy that checkpoint without needing to re-run training for 8 epochs.

This approach ensures you’re leveraging the best-performing model state without the additional expense of retraining.