Edit model card

Optimum Habana is the interface between the Transformers library and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading and fine-tuning 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 Transformers models at hf.co/hardware/habana.

Swin Transformer model HPU configuration

This model only contains the GaudiConfig file for running the Swin Transformer model on Habana's Gaudi processors (HPU).

This model contains no model weights, only a GaudiConfig.

This enables to specify:

  • use_habana_mixed_precision: whether to use Habana Mixed Precision (HMP)
    • hmp_opt_level: optimization level for HMP, see here for a detailed explanation
    • hmp_bf16_ops: list of operators that should run in bf16
    • hmp_fp32_ops: list of operators that should run in fp32
    • hmp_is_verbose: verbosity
  • 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


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 is an image classification example script to fine-tune a model. You can run it with Swin with the following command:

python run_image_classification.py \
    --model_name_or_path microsoft/swin-base-patch4-window7-224 \
    --dataset_name cifar10 \
    --output_dir /tmp/outputs/ \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --learning_rate 2e-5 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 32 \
    --per_device_eval_batch_size 32 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337 \
    --use_habana \
    --use_lazy_mode \
    --gaudi_config_name Habana/swin \
    --throughput_warmup_steps 2

Check the documentation out for more advanced usage and examples.

Downloads last month
Hosted inference API

Unable to determine this model’s pipeline type. Check the docs .