Get Started Post-Training Dynamic Quantization | AI Model Optimization with Intel® Neural Compressor

Опубликовано 28 июня 2023, 13:00

Learn the basics of dynamic quantization. Then see how it’s applied to a GPT2-based headline generation application using Intel Neural Compressor.

Dynamic quantization is the simplest method for quantizing AI models for efficient deployment. This technique quantizes the model weights of a pre-trained model and inserts functions into the model to quantize activations during inference. While this adds runtime overhead, it can also adapt the scale factor dynamically as the input ranges change.

Intel® Neural Compressor works across PyTorch*, TensorFlow*, and ONNX* Runtime. Learn how to implement dynamic quantization using a GPT2-based headline generation AI Reference Kit. The demonstration discusses options to customize the dynamic quantization process and shows the resulting speedup for this application.

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About the AI Model Optimization with Intel® Neural Compressor Series:
Learn how to choose and get started with AI model optimization techniques. Get started with examples using Intel® Neural Compressor, which works within PyTorch*, TensorFlow*, and ONNX* Runtime

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Get Started Post-Training Dynamic Quantization | AI Model Optimization with Intel® Neural Compressor