training_args = TrainingArguments(
     output_dir="my_awesome_food_model",
     remove_unused_columns=False,
     evaluation_strategy="epoch",
     save_strategy="epoch",
     learning_rate=5e-5,
     per_device_train_batch_size=16,
     gradient_accumulation_steps=4,
     per_device_eval_batch_size=16,
     num_train_epochs=3,
     warmup_ratio=0.1,
     logging_steps=10,
     load_best_model_at_end=True,
     metric_for_best_model="accuracy",
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     data_collator=data_collator,
     train_dataset=food["train"],
     eval_dataset=food["test"],
     tokenizer=image_processor,
     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 are unfamiliar with fine-tuning a model with Keras, check out the basic tutorial first!