Workflow (#20181)

docs/optimization_guide/model_optimization_guide.md

@@ -1,43 +0,0 @@
-# Model Optimization Guide {#openvino_docs_model_optimization_guide}
-
-@sphinxdirective
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   ptq_introduction
-   tmo_introduction
-   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>`__:
-
-- :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:`Training-time Optimization <tmo_introduction>`, a suite of advanced methods for training-time model optimization within the DL framework, such as PyTorch and TensorFlow 2.x. It supports methods like Quantization-aware Training, Structured and Unstructured Pruning, etc. 
-
-- :doc:`Weight Compression <weight_compression>`, an easy-to-use method for Large Language Models footprint reduction and inference acceleration.
-
-.. note:: OpenVINO also supports optimized models (for example, quantized) from source frameworks such as PyTorch, TensorFlow, and ONNX (in Q/DQ; Quantize/DeQuantize format). No special steps are required in this case and optimized models can be converted to the OpenVINO Intermediate Representation format (IR) right away.
-
-Post-training Quantization is the fastest way to optimize an arbitrary DL model and should be applied first, but it is limited in terms of achievable accuracy-performance trade-off. The recommended approach to obtain OpenVINO quantized model is to convert a model from original framework to ``ov.Model`` and ensure that the model works correctly in OpenVINO, for example, by calculating the model metrics. Then, ``ov.Model`` can be used as input for the ``nncf.quantize()`` method to get the quantized model (see the diagram below).
-
-In case of unsatisfactory accuracy or performance after Post-training Quantization, Training-time Optimization can be used as an option.
-
-.. image:: _static/images/DEVELOPMENT_FLOW_V3_crunch.svg
-
-Once the model is optimized using the aforementioned methods, it can be used for inference using the regular OpenVINO inference workflow. No changes to the inference code are required.
-
-.. image:: _static/images/WHAT_TO_USE.svg
-
-Additional Resources
-####################
-
-- :doc:`Post-training Quantization <ptq_introduction>`
-- :doc:`Training-time Optimization <tmo_introduction>`
-- :doc:`Weight Compression <weight_compression>`
-- :doc:`Deployment optimization <openvino_docs_deployment_optimization_guide_dldt_optimization_guide>`
-- `HuggingFace Optimum Intel <https://huggingface.co/docs/optimum/intel/optimization_ov>`__
-
-@endsphinxdirective