resume from latest checkpoint
trainer.train(resume_from_checkpoint=True)
resume from specific checkpoint saved in output directory
trainer.train(resume_from_checkpoint="your-model/checkpoint-1000")

You can save your checkpoints (the optimizer state is not saved by default) to the Hub by setting push_to_hub=True in [TrainingArguments] to commit and push them. Other options for deciding how your checkpoints are saved are set up in the hub_strategy parameter: