readme: ---
  license: apache-2.0
  ---
  
  [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.
  Learn more about how to take advantage of the power of Habana HPUs to train Transformers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana).
  
  ## RoBERTa Large model HPU configuration
  
  This model only contains the `GaudiConfig` file for running the [roberta-large](https://huggingface.co/roberta-large) model on Habana's Gaudi processors (HPU).
  
  **This model contains no model weights, only a GaudiConfig.**
  
  This enables to specify:
  - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
    - `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
    - `hmp_bf16_ops`: list of operators that should run in bf16
    - `hmp_fp32_ops`: list of operators that should run in fp32
    - `hmp_is_verbose`: verbosity
  - `use_fused_adam`: whether to use Habana's custom AdamW implementation
  - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
  
  ## 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/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with RoBERTa Large with the following command:
  ```bash
  python run_qa.py \
    --model_name_or_path roberta-large \
    --gaudi_config_name Habana/roberta-large \
    --dataset_name squad \
    --do_train \
    --do_eval \
    --per_device_train_batch_size 12 \
    --per_device_eval_batch_size 8 \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --max_seq_length 384 \
    --output_dir /tmp/squad/ \
    --use_habana \
    --use_lazy_mode \
    --throughput_warmup_steps 2
  ```
  
  Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. 
  