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---
license: apache-2.0
---
[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).
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.
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).
## Wav2Vec2 model HPU configuration
This model only contains the `GaudiConfig` file for running the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model on Habana's Gaudi processors (HPU).
**This model contains no model weights, only a GaudiConfig.**
This enables to specify:
- `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
- `disable_autocast`: whether to disable autocast; this parameter takes precedence over --bf16 flag and is temporary as some scripts produce nan values.
In those cases this parameter is already present in huggingface topology Habana gaudi_config.json.
## 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/audio-classification/run_audio_classification.py) is an audio classification example script to fine-tune a model. You can run it with Wav2Vec2 with the following command:
```bash
python run_audio_classification.py \
--model_name_or_path facebook/wav2vec2-base \
--dataset_name superb \
--dataset_config_name ks \
--output_dir /tmp/wav2vec2-base-ft-keyword-spotting \
--overwrite_output_dir \
--remove_unused_columns False \
--do_train \
--do_eval \
--learning_rate 3e-5 \
--max_length_seconds 1 \
--attention_mask False \
--warmup_ratio 0.1 \
--num_train_epochs 5 \
--per_device_train_batch_size 256 \
--per_device_eval_batch_size 256 \
--dataloader_num_workers 4 \
--seed 27 \
--use_habana \
--use_lazy_mode \
--gaudi_config_name Habana/wav2vec2 \
--throughput_warmup_steps 2 \
--bf16
```
Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
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