Once you have done trainer.save_model(any_folder), you can access your model either:

using the pipeline API
or re-instantiate it with model.from_pretrained
Just use the name of your local folder as an identifier.

If you do trainer.push_to_hub(a_repo_name) you can do the same from any machine, and even use the widget inference API on the model hub.