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license: apache-2.0 |
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[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). |
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It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. |
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Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). |
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## T5 model HPU configuration |
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This model only contains the `GaudiConfig` file for running the [T5](https://huggingface.co/t5-base) model on Habana's Gaudi processors (HPU). |
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**This model contains no model weights, only a GaudiConfig.** |
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This enables to specify: |
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- `use_fused_adam`: whether to use Habana's custom AdamW implementation |
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- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator |
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## Usage |
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The model is instantiated the same way as in the Transformers library. |
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The only difference is that there are a few new training arguments specific to HPUs. |
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[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: |
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```bash |
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python run_summarization.py \ |
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--model_name_or_path t5-small \ |
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--do_train \ |
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--do_eval \ |
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--dataset_name cnn_dailymail \ |
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--dataset_config "3.0.0" \ |
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--source_prefix "summarize: " \ |
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--output_dir /tmp/tst-summarization \ |
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--per_device_train_batch_size 4 \ |
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--per_device_eval_batch_size 4 \ |
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--overwrite_output_dir \ |
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--predict_with_generate \ |
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--use_habana \ |
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--use_lazy_mode \ |
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--gaudi_config_name Habana/t5 \ |
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--ignore_pad_token_for_loss False \ |
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--pad_to_max_length \ |
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--save_strategy epoch \ |
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--throughput_warmup_steps 3 |
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``` |
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Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. |
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