[DOCS] content updates

docs/articles_en/about-openvino/key-features.rst

@@ -14,7 +14,7 @@ Easy Integration
       OpenVINO optimizations to your PyTorch models directly with a single line of code.
 
 | :doc:`GenAI Out Of The Box <../openvino-workflow-generative/inference-with-genai>`
-|     With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
+|     With the OpenVINO GenAI, you can run generative models with just a few lines of code.
       Check out the GenAI guide for instructions on how to do it.
 
 | `Python / C++ / C / NodeJS APIs <https://docs.openvino.ai/2024/api/api_reference.html>`__

docs/articles_en/about-openvino/release-notes-openvino.rst

@@ -26,10 +26,9 @@ OpenVINO Release Notes
 What's new
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* OpenVINO 2024.6 release includes updates for enhanced stability and improved LLM performance.
-* Introduced support for Intel® Arc™ B-Series Graphics (formerly known as Battlemage).
-* Implemented optimizations to improve the inference time and LLM performance on NPUs.
-* Improved LLM performance with GenAI API optimizations and bug fixes.
+* .
+
+
 
 
 
@@ -47,11 +46,8 @@ CPU Device Plugin
 GPU Device Plugin
 -----------------------------------------------------------------------------------------------
 
-* Device memory copy optimizations have been introduced for inference with **Intel® Arc™ B-Series
-  Graphics** (formerly known as Battlemage). Since it does not utilize L2 cache for copying memory
-  between the device and host, a dedicated `copy` operation is used, if inputs or results are
-  not expected in the device memory.
-* ChatGLM4 inference on GPU has been optimized.
+* .
+
 
 NPU Device Plugin
 -----------------------------------------------------------------------------------------------
@@ -76,10 +72,6 @@ Other Changes and Known Issues
 Jupyter Notebooks
 -----------------------------
 
-* `Visual-language assistant with GLM-Edge-V and OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/glm-edge-v/glm-edge-v.ipynb>`__
-* `Local AI and OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/localai/localai.ipynb>`__
-* `Multimodal understanding and generation with Janus and OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/janus-multimodal-generation/janus-multimodal-generation.ipynb>`__
-
 
 
 
@@ -131,39 +123,19 @@ Discontinued in 2024
 
 * Runtime components:
 
-  * Intel® Gaussian & Neural Accelerator (Intel® GNA). Consider using the Neural Processing
-    Unit (NPU) for low-powered systems like Intel® Core™ Ultra or 14th generation and beyond.
-  * OpenVINO C++/C/Python 1.0 APIs (see
-    `2023.3 API transition guide <https://docs.openvino.ai/2023.3/openvino_2_0_transition_guide.html>`__
-    for reference).
-  * All ONNX Frontend legacy API (known as ONNX_IMPORTER_API).
-  * ``PerfomanceMode.UNDEFINED`` property as part of the OpenVINO Python API.
+  * The OpenVINO property of Affinity API will is no longer available. It has been replaced with CPU
+    binding configurations (``ov::hint::enable_cpu_pinning``).
 
 * Tools:
 
-  * Deployment Manager. See :doc:`installation <../get-started/install-openvino>` and
-    :doc:`deployment <../get-started/install-openvino>` guides for current distribution
-    options.
-  * `Accuracy Checker <https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/README.md>`__.
-  * `Post-Training Optimization Tool <https://docs.openvino.ai/2023.3/pot_introduction.html>`__
-    (POT). Neural Network Compression Framework (NNCF) should be used instead.
-  * A `Git patch <https://github.com/openvinotoolkit/nncf/tree/release_v281/third_party_integration/huggingface_transformers>`__
-    for NNCF integration with `huggingface/transformers <https://github.com/huggingface/transformers>`__.
-    The recommended approach is to use `huggingface/optimum-intel <https://github.com/huggingface/optimum-intel>`__
-    for applying NNCF optimization on top of models from Hugging Face.
-  * Support for Apache MXNet, Caffe, and Kaldi model formats. Conversion to ONNX may be used
-    as a solution.
-  * The macOS x86_64 debug bins are no longer provided with the OpenVINO toolkit, starting
-    with OpenVINO 2024.5.
-  * Python 3.8 is no longer supported, starting with OpenVINO 2024.5.
-
-    * As MxNet doesn't support Python version higher than 3.8, according to the
-      `MxNet PyPI project <https://pypi.org/project/mxnet/>`__,
-      it is no longer supported by OpenVINO, either.
-
-  * Discrete Keem Bay support is no longer supported, starting with OpenVINO 2024.5.
-  * Support for discrete devices (formerly codenamed Raptor Lake) is no longer available for
-    NPU.
+  * The OpenVINO™ Development Tools package (pip install openvino-dev) is no longer available
+    for OpenVINO releases in 2025.
+  * Model Optimizer is no longer available. Consider using the
+    :doc:`new conversion methods <../openvino-workflow/model-preparation/convert-model-to-ir>`
+    instead. For more details, see the
+    `model conversion transition guide <https://docs.openvino.ai/2024/documentation/legacy-features/transition-legacy-conversion-api.html>`__.
+  * Intel® Streaming SIMD Extensions (Intel® SSE) are currently not enabled in the binary
+    package by default. They are still supported in the source code form.
 
 
 Deprecated and to be removed in the future
@@ -175,26 +147,18 @@ Deprecated and to be removed in the future
   standard support.
 * The openvino-nightly PyPI module will soon be discontinued. End-users should proceed with the
   Simple PyPI nightly repo instead. More information in
-  `Release Policy <https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/release-policy.html#nightly-releases>`__.
-* The OpenVINO™ Development Tools package (pip install openvino-dev) will be removed from
-  installation options and distribution channels beginning with OpenVINO 2025.0.
-* Model Optimizer will be discontinued with OpenVINO 2025.0. Consider using the
-  :doc:`new conversion methods <../openvino-workflow/model-preparation/convert-model-to-ir>`
-  instead. For more details, see the
-  `model conversion transition guide <https://docs.openvino.ai/2024/documentation/legacy-features/transition-legacy-conversion-api.html>`__.
-* OpenVINO property Affinity API will be discontinued with OpenVINO 2025.0.
-  It will be replaced with CPU binding configurations (``ov::hint::enable_cpu_pinning``).
-
+  `Release Policy <https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/release-policy.html#nightly-releases>`__.
+* “auto shape” and “auto batch size” (reshaping a model in runtime) will be removed in the
+  future. OpenVINO's dynamic shape models are recommended instead.
+* MacOS x86 is no longer recommended for use due to the discontinuation of validation.
+  Full support will be removed later in 2025.
+* The `openvino` namespace of the OpenVINO Python API has been redesigned, removing the nested
+  `openvino.runtime` module. The old namespace is now considered deprecated and will be
+  discontinued in 2026.0.
 
 
 
 
-  * “auto shape” and “auto batch size” (reshaping a model in runtime) will be removed in the
-    future. OpenVINO's dynamic shape models are recommended instead.
-
-* Starting with 2025.0 MacOS x86 is no longer recommended for use due to the discontinuation
-  of validation. Full support will be removed later in 2025.
-
 
 
 
@@ -203,17 +167,13 @@ Legal Information
 +++++++++++++++++++++++++++++++++++++++++++++
 
 You may not use or facilitate the use of this document in connection with any infringement
-or other legal analysis concerning Intel products described herein.
-
-You agree to grant Intel a non-exclusive, royalty-free license to any patent claim
-thereafter drafted which includes subject matter disclosed herein.
+or other legal analysis concerning Intel products described herein. All information provided
+here is subject to change without notice. Contact your Intel representative to obtain the
+latest Intel product specifications and roadmaps.
 
 No license (express or implied, by estoppel or otherwise) to any intellectual property
 rights is granted by this document.
 
-All information provided here is subject to change without notice. Contact your Intel
-representative to obtain the latest Intel product specifications and roadmaps.
-
 The products described may contain design defects or errors known as errata which may
 cause the product to deviate from published specifications. Current characterized errata
 are available on request.
@@ -225,10 +185,9 @@ or from the OEM or retailer.
 
 No computer system can be absolutely secure.
 
-Intel, Atom, Core, Xeon, OpenVINO, and the Intel logo are trademarks
-of Intel Corporation in the U.S. and/or other countries.
-
-Other names and brands may be claimed as the property of others.
+Intel, Atom, Core, Xeon, OpenVINO, and the Intel logo are trademarks of Intel Corporation in
+the U.S. and/or other countries. Other names and brands may be claimed as the property of
+others.
 
 Copyright © 2025, Intel Corporation. All rights reserved.
 

docs/articles_en/documentation/openvino-ecosystem.rst

@@ -24,7 +24,7 @@ you an overview of a whole ecosystem of tools and solutions under the OpenVINO u
 
 | **GenAI**
 | :bdg-link-dark:`Github <https://github.com/openvinotoolkit/openvino.genai>`
-  :bdg-link-success:`User Guide <https://docs.openvino.ai/2024/openvino-workflow-generative/inference-with-genai.html>`
+  :bdg-link-success:`User Guide <https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html>`
 
 OpenVINO™ GenAI Library aims to simplify running inference of generative AI
 models. Check the LLM-powered Chatbot Jupyter notebook to see how GenAI works.
@@ -113,7 +113,7 @@ generative AI and vision models directly on your computer or edge device using O
 
 | **Tokenizers**
 | :bdg-link-dark:`Github <https://github.com/openvinotoolkit/openvino_tokenizers>`
-  :bdg-link-success:`User Guide <https://docs.openvino.ai/2024/openvino-workflow-generative/ov-tokenizers.html>`
+  :bdg-link-success:`User Guide <https://docs.openvino.ai/2025/openvino-workflow-generative/ov-tokenizers.html>`
 
 OpenVINO Tokenizers add text processing operations to OpenVINO.
 

docs/articles_en/get-started/configurations.rst

@@ -32,8 +32,9 @@ potential of OpenVINO™. Check the following list for components used in your w
     for details.
 
 | **OpenVINO GenAI Dependencies**
-|   OpenVINO GenAI is a flavor of OpenVINO, aiming to simplify running generative
-    AI models. For information on the dependencies required to use OpenVINO GenAI, see the
+|   OpenVINO GenAI is a tool based on the OpenVNO Runtime but simplifying the process of
+    running generative AI models. For information on the dependencies required to use
+    OpenVINO GenAI, see the
     :doc:`guide on OpenVINO GenAI Dependencies <configurations/genai-dependencies>`.
 
 | **Open Computer Vision Library**

docs/articles_en/get-started/install-openvino.rst

@@ -11,11 +11,11 @@ Install OpenVINO™ 2024.6
    :maxdepth: 3
    :hidden:
 
+   OpenVINO GenAI <install-openvino/install-openvino-genai>
    OpenVINO Runtime on Linux <install-openvino/install-openvino-linux>
    OpenVINO Runtime on Windows <install-openvino/install-openvino-windows>
    OpenVINO Runtime on macOS <install-openvino/install-openvino-macos>
    Create an OpenVINO Yocto Image <install-openvino/install-openvino-yocto>
-   OpenVINO GenAI Flavor <install-openvino/install-openvino-genai>
 
 .. raw:: html
 
@@ -30,13 +30,13 @@ All currently supported versions are:
 * 2023.3 (LTS)
 
 
-.. dropdown:: Effortless GenAI integration with OpenVINO GenAI Flavor
+.. dropdown:: Effortless GenAI integration with OpenVINO GenAI
 
-   A new OpenVINO GenAI Flavor streamlines application development by providing
-   LLM-specific interfaces for easy integration of language models, handling tokenization and
-   text generation. For installation and usage instructions, proceed to
-   :doc:`Install OpenVINO GenAI Flavor <../openvino-workflow-generative>` and
-   :doc:`Run LLMs with OpenVINO GenAI Flavor <../openvino-workflow-generative/inference-with-genai>`.
+   OpenVINO GenAI streamlines application development by providing LLM-specific interfaces for
+   easy integration of language models, handling tokenization and text generation.
+   For installation and usage instructions, check
+   :doc:`OpenVINO GenAI installation <../openvino-workflow-generative>` and
+   :doc:`inference with OpenVINO GenAI <../openvino-workflow-generative/inference-with-genai>`.
 
 .. dropdown:: Building OpenVINO from Source
 

docs/articles_en/get-started/install-openvino/install-openvino-genai.rst

@@ -1,24 +1,26 @@
 Install OpenVINO™ GenAI
 ====================================
 
-OpenVINO GenAI is a new flavor of OpenVINO, aiming to simplify running inference of generative AI models.
-It hides the complexity of the generation process and minimizes the amount of code required.
-You can now provide a model and input context directly to OpenVINO, which performs tokenization of the
-input text, executes the generation loop on the selected device, and returns the generated text.
-For a quickstart guide, refer to the :doc:`GenAI API Guide <../../openvino-workflow-generative/inference-with-genai>`.
-
-To see GenAI in action, check the Jupyter notebooks:
-`LLM-powered Chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/README.md>`__ and
+OpenVINO GenAI is a tool, simplifying generative AI model inference. It is based on the
+OpenVINO Runtime, hiding the complexity of the generation process and minimizing the amount of
+code required. You provide a model and the input context directly to the tool, while it
+performs tokenization of the input text, executes the generation loop on the selected device,
+and returns the generated content. For a quickstart guide, refer to the
+:doc:`GenAI API Guide <../../openvino-workflow-generative/inference-with-genai>`.
+
+To see OpenVINO GenAI in action, check these Jupyter notebooks:
+`LLM-powered Chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/README.md>`__
+and
 `LLM Instruction-following pipeline <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-question-answering/README.md>`__.
 
-The OpenVINO GenAI flavor is available for installation via PyPI and Archive distributions.
+OpenVINO GenAI is available for installation via PyPI and Archive distributions.
 A `detailed guide <https://github.com/openvinotoolkit/openvino.genai/blob/releases/2024/3/src/docs/BUILD.md>`__
 on how to build OpenVINO GenAI is available in the OpenVINO GenAI repository.
 
 PyPI Installation
 ###############################
 
-To install the GenAI flavor of OpenVINO via PyPI, follow the standard :doc:`installation steps <install-openvino-pip>`,
+To install the GenAI package via PyPI, follow the standard :doc:`installation steps <install-openvino-pip>`,
 but use the *openvino-genai* package instead of *openvino*:
 
 .. code-block:: python
@@ -28,9 +30,9 @@ but use the *openvino-genai* package instead of *openvino*:
 Archive Installation
 ###############################
 
-The OpenVINO GenAI archive package includes the OpenVINO™ Runtime and :doc:`Tokenizers <../../openvino-workflow-generative/ov-tokenizers>`.
-To install the GenAI flavor of OpenVINO from an archive file, follow the standard installation steps for your system
-but instead of using the vanilla package file, download the one with OpenVINO GenAI:
+The OpenVINO GenAI archive package includes the OpenVINO™ Runtime, as well as :doc:`Tokenizers <../../openvino-workflow-generative/ov-tokenizers>`.
+It installs the same way as the standard OpenVINO Runtime, so follow its installation steps,
+just use the OpenVINO GenAI package instead:
 
 Linux
 ++++++++++++++++++++++++++

docs/articles_en/openvino-workflow-generative.rst

@@ -90,8 +90,8 @@ The advantages of using OpenVINO for generative model deployment:
 
 Proceed to guides on:
 
-* :doc:`OpenVINO GenAI Flavor <./openvino-workflow-generative/inference-with-genai>`
+* :doc:`OpenVINO GenAI <./openvino-workflow-generative/inference-with-genai>`
 * :doc:`Hugging Face and Optimum Intel <./openvino-workflow-generative/inference-with-optimum-intel>`
-* `Generative AI with Base OpenVINO <https://docs.openvino.ai/2024/openvino-workflow-generative/llm-inference-native-ov.html>`__
+* `Generative AI with Base OpenVINO <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-native-ov>`__
 
 

docs/articles_en/openvino-workflow-generative/inference-with-genai.rst

@@ -2,13 +2,13 @@ Inference with OpenVINO GenAI
 ===============================================================================================
 
 .. meta::
-   :description: Learn how to use the OpenVINO GenAI flavor to execute LLM models.
+   :description: Learn how to use OpenVINO GenAI to execute LLM models.
 
 .. toctree::
    :maxdepth: 1
    :hidden:
 
-   NPU inference of LLMs <inference-with-genai-on-npu>
+   NPU inference of LLMs <inference-with-genai/inference-with-genai-on-npu>
 
 
 OpenVINO™ GenAI is a library of pipelines and methods, extending the OpenVINO runtime to work

docs/articles_en/openvino-workflow-generative/inference-with-genai-on-npu.rst → docs/articles_en/openvino-workflow-generative/inference-with-genai/inference-with-genai-on-npu.rst

@@ -2,9 +2,10 @@ Inference with OpenVINO GenAI
 ==========================================
 
 .. meta::
-   :description: Learn how to use the OpenVINO GenAI flavor to execute LLM models on NPU.
+   :description: Learn how to use OpenVINO GenAI to execute LLM models on NPU.
 
-This guide will give you extra details on how to utilize NPU with the GenAI flavor.
+
+This guide will give you extra details on how to utilize NPU with OpenVINO GenAI.
 :doc:`See the installation guide <../../get-started/install-openvino/install-openvino-genai>`
 for information on how to start.
 
@@ -24,6 +25,10 @@ Note that for systems based on Intel® Core™ Ultra Processors Series 2, more t
 may be required to run prompts over 1024 tokens on models exceeding 7B parameters,
 such as Llama-2-7B, Mistral-0.2-7B, and Qwen-2-7B.
 
+Make sure your model works with NPU. Some models may not be supported, for example,
+**the FLUX.1 pipeline is currently not supported by the device**.
+
+
 Export an LLM model via Hugging Face Optimum-Intel
 ##################################################
 

docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst

@@ -22,7 +22,7 @@ for more streamlined resource management.
 NPU Plugin is now available through all relevant OpenVINO distribution channels.
 
 | **Supported Platforms:**
-|   Host: Intel® Core™ Ultra (former Meteor Lake)
+|   Host: Intel® Core™ Ultra series
 |   NPU device: NPU 3720
 |   OS: Ubuntu* 22.04 64-bit (with Linux kernel 6.6+), MS Windows* 11 64-bit (22H2, 23H2)
 
@@ -33,10 +33,10 @@ Follow the instructions below to install the latest NPU drivers:
 * `Linux driver <https://github.com/intel/linux-npu-driver/releases>`__
 
 
-The plugin uses the graph extension API exposed by the driver to convert the OpenVINO specific representation
-of the model into a proprietary format. The compiler included in the user mode driver (UMD) performs
-platform specific optimizations in order to efficiently schedule the execution of network layers and
-memory transactions on various NPU hardware submodules.
+The plugin uses the graph extension API exposed by the driver to convert the OpenVINO specific
+representation of the model into a proprietary format. The compiler included in the user mode
+driver (UMD) performs platform specific optimizations in order to efficiently schedule the
+execution of network layers and memory transactions on various NPU hardware submodules.
 
 To use NPU for inference, pass the device name to the ``ov::Core::compile_model()`` method: