Validation
Validation is evaluating the model on a held-out dataset during training to monitor generalization and tune hyperparameters. It prevents overfitting and guides early stopping.
What is Validation?
Validation is evaluating the model on a held-out dataset during training to monitor generalization and tune hyperparameters. It prevents overfitting and guides early stopping.
Validation is evaluating the model on a held-out dataset during training to monitor generalization and tune hyperparameters. It prevents overfitting and guides early stopping.
Where is it used?
LLM developers hold out evaluation sets (MMLU, HumanEval, GSM8K) to track model capability across training checkpoints; validation perplexity guides learning rate schedules.
How to build it
After each epoch, switch model to `model.eval()` and `torch.no_grad()`, compute loss on the validation DataLoader, and log it to compare with training loss.