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Model Checkpoint
A saved snapshot of a model's state taken during or after training.
Definition
A model checkpoint is a saved snapshot of a model's state, usually its weights and sometimes its optimizer state, captured at a point during or after training. Checkpoints let a run resume after interruption, allow comparison of versions, and provide the artifact that is loaded for inference. They are how training progress is preserved and shared.