Optimum documentation


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🤗 Optimum provides an integration with Better Transformer, a fast path of standard PyTorch Transformer APIs to benefit from interesting speedups on CPU & GPU through sparsity and fused kernels. For now, it supports Transformer encoders, basically fast path of nn.TransformerEncoderLayer, support for decoders and training path is coming soon.


Since its 1.13 version, PyTorch released the stable version of a fast path for its standard Transformer APIs that provides out of the box performance improvements for transformer-based models. You can benefit from interesting speedup on most consumer-type devices, including CPUs, older and newer versions of NIVIDIA GPUs. You can now use this feature in 🤗 Optimum together with Transformers and use it for major models in the Hugging Face ecosystem.

Supported models

The list of supported model below:

Note that for encoder-decoder models, only the encoder part is supported by PyTorch’s BetterTransformer for now.

Let us know by opening an issue in 🤗 Optimum if you want more models to be supported, or check out the contribution guideline if you want to add it by yourself!

Quick usage

In order to use the BetterTransformer API just run the following commands:

>>> from transformers import AutoModelForSequenceClassification
>>> from optimum.bettertransformer import BetterTransformer
>>> model_hf = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
>>> model = BetterTransformer.transform(model_hf, keep_original_model=True)

You can leave keep_original_model=False in case you want to overwrite the current model with its BetterTransformer version.

More details on tutorials section to deeply understand how to use it, or check the Google colab demo!