Philipp Schmid • 6/7/2022

Static Quantization with Hugging Face `optimum` for ~3x latency improvements

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This technical tutorial demonstrates post-training static quantization on a Hugging Face Transformers model using the Optimum library and ONNX Runtime. It provides a step-by-step guide to quantize a DistilBERT model for CPU inference, covering conversion to ONNX, calibration, quantization, performance evaluation, and sharing the model on the Hugging Face Hub, resulting in significant latency gains with minimal accuracy loss.

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#Quantization #Onnx Runtime #Model Optimization
Static Quantization with Hugging Face `optimum` for ~3x latency improvements

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