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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).
## Llama model HPU configuration
This model only contains the `GaudiConfig` file for running [Falcon models](https://huggingface.co/tiiuae) 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 Falcon with the following command:
```bash
LOWER_LIST=ops_bf16.txt python3 run_lora_clm.py \
--model_name_or_path tiiuae/falcon-40b \
--dataset_name timdettmers/openassistant-guanaco \
--bf16 True \
--output_dir ./model_lora_falcon \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 16 \
--evaluation_strategy "no" \
--save_strategy "no" \
--learning_rate 3e-4 \
--max_grad_norm 0.3 \
--warmup_ratio 0.03 \
--lr_scheduler_type "constant" \
--logging_steps 1 \
--do_train \
--use_habana \
--use_lazy_mode \
--pipelining_fwd_bwd \
--throughput_warmup_steps 3 \
--lora_rank=64 \
--lora_alpha=16 \
--lora_dropout=0.1 \
--lora_target_modules "query_key_value" "dense" "dense_h_to_4h" "dense_4h_to_h" \
--dataset_concatenation \
--max_seq_length 256 \
--low_cpu_mem_usage True \
--adam_epsilon 1e-08 \
--do_eval \
--validation_split_percentage 5
```
You will need to install the [PEFT](https://huggingface.co/docs/peft/index) library with `pip install peft` to run the command above.
Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
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