File size: 2,190 Bytes
578c3d0 9a302a2 c0afbde 9a302a2 84bbd47 9a302a2 5870307 9a302a2 d0b8563 9a302a2 64740b8 98d2604 ac03d4b 488c8fa 9a302a2 ac03d4b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
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).
## DistilBERT Base model HPU configuration
This model only contains the `GaudiConfig` file for running the [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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
- `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/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with DistilBERT with the following command:
```bash
python run_qa.py \
--model_name_or_path distilbert-base-uncased \
--gaudi_config_name Habana/distilbert-base-uncased \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--max_seq_length 384 \
--output_dir /tmp/squad/ \
--use_habana \
--use_lazy_mode \
--throughput_warmup_steps 2 \
--bf16
```
Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
|