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  license: apache-2.0
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  license: apache-2.0
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+
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+ [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Transformers library and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading and fine-tuning 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 Transformers models at [hf.co/Habana](https://huggingface.co/Habana).
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+
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+ ## Bert Base model HPU configuration
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+
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+ This model contains just the `GaudiConfig` file for running the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on Habana's Gaudi processors (HPU).
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+
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+ **This model contains no model weights, only a GaudiConfig.**
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+
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+ This enables to specify:
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+ - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
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+ - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_User_Guide/PT_Mixed_Precision.html#configuration-options) for a detailed explanation
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+ - `hmp_bf16_ops`: list of operators that should run in bf16
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+ - `hmp_fp32_ops`: list of operators that should run in fp32
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+ - `hmp_is_verbose`: verbosity
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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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+
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+ ## Usage
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+
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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 the Gaudi configuration has to be loaded and provided to the trainer:
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+
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+ ```
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+ from optimum.habana import GaudiConfig, GaudiTrainer, GaudiTrainingArguments
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+ from transformers import BertTokenizer, BertModel
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+
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+ tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
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+ model = BertModel.from_pretrained("bert-base-uncased")
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+ gaudi_config = GaudiConfig.from_pretrained("Habana/bert-base-uncased")
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+ args = GaudiTrainingArguments(
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+ output_dir="/tmp/output_dir",
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+ use_habana=True,
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+ use_lazy_mode=True,
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+ )
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+
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+ trainer = GaudiTrainer(
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+ model=model,
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+ gaudi_config=gaudi_config,
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+ args=args,
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+ tokenizer=tokenizer,
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+ )
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+ trainer.train()
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+ ```