training_args = TrainingArguments(
     output_dir="my_awesome_swag_model",
     evaluation_strategy="epoch",
     save_strategy="epoch",
     load_best_model_at_end=True,
     learning_rate=5e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     num_train_epochs=3,
     weight_decay=0.01,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_swag["train"],
     eval_dataset=tokenized_swag["validation"],
     tokenizer=tokenizer,
     data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!