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@@ -13,18 +13,15 @@ This model only contains the `GaudiConfig` file for running the [bert-large-unca
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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_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_Mixed_Precision/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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  ## 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/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with BERT Large with the following command:
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  ```bash
@@ -43,7 +40,8 @@ python run_qa.py \
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  --output_dir /tmp/squad/ \
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  --use_habana \
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  --use_lazy_mode \
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- --throughput_warmup_steps 2
 
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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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  **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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+ - `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
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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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+ It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
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  [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with BERT Large with the following command:
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  ```bash
 
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  --output_dir /tmp/squad/ \
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  --use_habana \
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  --use_lazy_mode \
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+ --throughput_warmup_steps 2 \
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+ --bf16
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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.