Training checkpoints may include optimizer values, scheduler state, random seeds, and progress counters so a run can resume. Deployment checkpoints often retain only the weights and configuration needed for inference.
A model checkpoint is a saved snapshot of parameters and related training state.
Training checkpoints may include optimizer values, scheduler state, random seeds, and progress counters so a run can resume. Deployment checkpoints often retain only the weights and configuration needed for inference.