 Usage  The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.  [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/summarization/run_summarization.py) is a summarization example script to fine-tune a model. You can run it with T5-small with the following command: ```bash python run_summarization.py \     --model_name_or_path t5-small \     --do_train \     --do_eval \     --dataset_name cnn_dailymail \     --dataset_config "3.0.0" \     --source_prefix "summarize: " \     --output_dir /tmp/tst-summarization \     --per_device_train_batch_size 4 \     --per_device_eval_batch_size 4 \     --overwrite_output_dir \     --predict_with_generate \     --use_habana \     --use_lazy_mode \     --gaudi_config_name Habana/t5 \     --ignore_pad_token_for_loss False \     --pad_to_max_length \     --save_strategy epoch \     --throughput_warmup_steps 2 ```  Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.  