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[N] Test a Distilled GPT-2’s generative capabilities

At Hugging Face, we recently started distilling models starting with DistilBERT – a distilled version of BERT. We recently distilled the small version of GPT-2, which has the following parameters:

81,9M parameters vs 124M for GPT-2/small (66% parameters)

Weighs 336Mb vs 523Mb for GPT-2/small (64% disk size)

On CPU and GPU, the average forward pass of DistilGPT-2 is 51% that of GPT-2/small (twice as fast).

The absolute increase in perplexity on WikiText-103 is 3.5 points (15.0 -> 18.5).

We have added it to our app write with transformer, as well as our two repos transformers (along with a tutorial on how to distill transformers and example scripts!) and swift-coreml-transformers. We have successfully run it on an iPhone 7 and it is 38% faster than GPT-2 on an iPhone X with neural engine.

submitted by /u/jikkii
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.