| 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). | |
| ## GPT2 model HPU configuration | |
| This model only contains the `GaudiConfig` file for running the [GPT2](https://huggingface.co/gpt2) 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 PyTorch's autocast 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. | |
| [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/language-modeling/run_clm.py) is a causal language modeling example script to pre-train/fine-tune a model. You can run it with GPT2 with the following command: | |
| ```bash | |
| python run_clm.py \ | |
| --model_name_or_path gpt2 \ | |
| --dataset_name wikitext \ | |
| --dataset_config_name wikitext-2-raw-v1 \ | |
| --per_device_train_batch_size 4 \ | |
| --per_device_eval_batch_size 4 \ | |
| --do_train \ | |
| --do_eval \ | |
| --output_dir /tmp/test-clm \ | |
| --gaudi_config_name Habana/gpt2 \ | |
| --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. | |