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Tatiana Savina 2023-12-01 15:18:56 +01:00 committed by GitHub
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docs/articles_en/openvino_workflow/model_optimization_guide.rst
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@ -13,7 +13,7 @@ Model Optimization Guide
weight_compression weight_compression
Model optimization is an optional offline step of improving the final model performance and reducing the model size by applying special optimization methods, such as 8-bit quantization, pruning, etc. OpenVINO offers two optimization paths implemented in `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__: Model optimization is an optional offline step of improving the final model performance and reducing the model size by applying special optimization methods, such as 8-bit quantization, pruning, etc. OpenVINO offers three optimization paths implemented in `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__:
- :doc:`Post-training Quantization <ptq_introduction>` is designed to optimize the inference of deep learning models by applying the post-training 8-bit integer quantization that does not require model retraining or fine-tuning. - :doc:`Post-training Quantization <ptq_introduction>` is designed to optimize the inference of deep learning models by applying the post-training 8-bit integer quantization that does not require model retraining or fine-tuning.