| Other options for deciding how your checkpoints are saved are set up in the hub_strategy parameter: | |
| hub_strategy="checkpoint" pushes the latest checkpoint to a subfolder named "last-checkpoint" from which you can resume training | |
| hug_strategy="all_checkpoints" pushes all checkpoints to the directory defined in output_dir (you'll see one checkpoint per folder in your model repository) | |
| When you resume training from a checkpoint, the [Trainer] tries to keep the Python, NumPy, and PyTorch RNG states the same as they were when the checkpoint was saved. |