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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).
## CLIP model HPU configuration
This model only contains the `GaudiConfig` file for running CLIP-like models (e.g. [this one](https://huggingface.co/openai/clip-vit-large-patch14)) 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
- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
## 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.\
It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/contrastive-image-text) is an example script to fine-tune a model on COCO.
Use it as follows:
1. You first need to download the dataset:
```bash
mkdir data
cd data
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
wget http://images.cocodataset.org/annotations/image_info_test2017.zip
cd ..
```
2. Then, you can create a model from pretrained vision and text decoder models:
```python
from transformers import (
VisionTextDualEncoderModel,
VisionTextDualEncoderProcessor,
AutoTokenizer,
AutoImageProcessor
)
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
"openai/clip-vit-large-patch14", "roberta-large"
)
tokenizer = AutoTokenizer.from_pretrained("roberta-large")
image_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
processor = VisionTextDualEncoderProcessor(image_processor, tokenizer)
# save the model and processor
model.save_pretrained("clip-roberta")
processor.save_pretrained("clip-roberta")
```
3. Finally, you can run it with the following command:
```bash
python run_clip.py \
--output_dir ./clip-roberta-finetuned \
--model_name_or_path ./clip-roberta \
--data_dir $PWD/data \
--dataset_name ydshieh/coco_dataset_script \
--dataset_config_name=2017 \
--image_column image_path \
--caption_column caption \
--remove_unused_columns=False \
--do_train --do_eval \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \
--overwrite_output_dir \
--save_strategy epoch \
--use_habana \
--use_lazy_mode \
--use_hpu_graphs \
--gaudi_config_name Habana/clip \
--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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