Validation Data

Validation data is a subset of examples held back from training to evaluate how well a model generalizes to unseen inputs. During training, the model never sees validation examples directly; instead, after each epoch (or periodically), the runtime computes a loss or accuracy metric on the validation set. This guides hyperparameter tuning (e.g., learning rate, layer count) and detects overfitting: if training loss drops but validation loss rises, the model is memorizing rather than learning. Operators encounter validation data when fine-tuning models with tools like Hugging Face Transformers or Axolotl, where they split their dataset into training, validation, and test splits. The validation split is used to pick the best checkpoint before final evaluation.

When fine-tuning Llama 3.1 8B on a custom instruction dataset using Hugging Face's Trainer, you might set train_test_split=0.1 to hold out 10% of examples as validation data. The trainer evaluates on this split every eval_steps (e.g., 100 steps) and logs validation loss. If validation loss starts increasing after 3 epochs while training loss keeps dropping, you stop early and revert to the checkpoint from epoch 2.

In a typical fine-tuning workflow with transformers.Trainer, you pass eval_dataset=val_data to the TrainingArguments. The trainer runs evaluation on that split and saves the best model based on validation loss. In Axolotl, you set val_set_size: 0.1 in the config; the tool splits the dataset and uses the validation set for early stopping and checkpoint selection.