[DOCS] Apply Readme comments (#27016)

### Details: - *item1* - *...* ### Tickets: - *ticket-id* --------- Co-authored-by: Ethan Yang <[email protected]> Co-authored-by: Karol Blaszczak <[email protected]> Co-authored-by: Roman Kazantsev <[email protected]>
openvinotoolkit · Jan 2, 2025 · ef5678a · ef5678a
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@@ -1,6 +1,14 @@
 <div align="center">
 <img src="docs/dev/assets/openvino-logo-purple-black.svg" width="400px">
 
+<h3 align="center">
+Open-source software toolkit for optimizing and deploying deep learning models.
+</h3>
+
+<p align="center">
+ <a href="https://docs.openvino.ai/2024/index.html"><b>Documentation</b></a> • <a href="https://blog.openvino.ai"><b>Blog</b></a> • <a href="https://docs.openvino.ai/2024/about-openvino/key-features.html"><b>Key Features</b></a> • <a href="https://docs.openvino.ai/2024/learn-openvino.html"><b>Tutorials</b></a> • <a href="https://docs.openvino.ai/2024/documentation/openvino-ecosystem.html"><b>Integrations</b></a> • <a href="https://docs.openvino.ai/2024/about-openvino/performance-benchmarks.html"><b>Benchmarks</b></a> • <a href="https://github.com/openvinotoolkit/openvino.genai"><b>Generative AI</b></a>
+</p>
+
 [![PyPI Status](https://badge.fury.io/py/openvino.svg)](https://badge.fury.io/py/openvino)
 [![Anaconda Status](https://anaconda.org/conda-forge/openvino/badges/version.svg)](https://anaconda.org/conda-forge/openvino)
 [![brew Status](https://img.shields.io/homebrew/v/openvino)](https://formulae.brew.sh/formula/openvino)
@@ -10,14 +18,14 @@
 [![brew Downloads](https://img.shields.io/homebrew/installs/dy/openvino)](https://formulae.brew.sh/formula/openvino)
  </div>
 
-Welcome to OpenVINO™, an open-source software toolkit for optimizing and deploying deep learning models.
 
 - **Inference Optimization**: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks.
-- **Flexible Model Support**: Use models trained with popular frameworks such as TensorFlow, PyTorch, ONNX, Keras, and PaddlePaddle. Convert and deploy models without original frameworks.
+- **Flexible Model Support**: Use models trained with popular frameworks such as PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX/Flax. Directly integrate models built with transformers and diffusers from the Hugging Face Hub using Optimum Intel. Convert and deploy models without original frameworks.
 - **Broad Platform Compatibility**: Reduce resource demands and efficiently deploy on a range of platforms from edge to cloud. OpenVINO™ supports inference on CPU (x86, ARM), GPU (OpenCL capable, integrated and discrete) and AI accelerators (Intel NPU).
 - **Community and Ecosystem**: Join an active community contributing to the enhancement of deep learning performance across various domains.
 
-Check out the [OpenVINO Cheat Sheet](https://docs.openvino.ai/2024/_static/download/OpenVINO_Quick_Start_Guide.pdf) for a quick reference.
+Check out the [OpenVINO Cheat Sheet](https://docs.openvino.ai/2024/_static/download/OpenVINO_Quick_Start_Guide.pdf) and [Key Features](https://docs.openvino.ai/2024/about-openvino/key-features.html) for a quick reference.
+
 
 ## Installation
 
@@ -40,6 +48,8 @@ Learn how to optimize and deploy popular models with the [OpenVINO Notebooks](ht
 - [Multimodal assistant with LLaVa and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llava-multimodal-chatbot/llava-multimodal-chatbot-genai.ipynb)
 - [Automatic speech recognition using Whisper and OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-asr-genai/whisper-asr-genai.ipynb)
 
+Discover more examples in the [OpenVINO Samples (Python & C++)](https://docs.openvino.ai/2024/learn-openvino/openvino-samples.html) and [Notebooks (Python)](https://docs.openvino.ai/2024/learn-openvino/interactive-tutorials-python.html).
+
 Here are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO:
 
 **PyTorch Model**
@@ -86,25 +96,43 @@ data = np.random.rand(1, 224, 224, 3)
 output = compiled_model({0: data})
 ```
 
-OpenVINO also supports CPU, GPU, and NPU devices and works with models in TensorFlow, PyTorch, ONNX, TensorFlow Lite, PaddlePaddle model formats.
-With OpenVINO you can do automatic performance enhancements at runtime customized to your hardware (preserving model accuracy), including:
-asynchronous execution, batch processing, tensor fusion, load balancing, dynamic inference parallelism, automatic BF16 conversion, and more.
+OpenVINO supports the CPU, GPU, and NPU [devices](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes.html) and works with models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, PaddlePaddle, and JAX/Flax [frameworks](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html). It includes [APIs](https://docs.openvino.ai/2024/api/api_reference.html) in C++, Python, C, NodeJS, and offers the GenAI API for optimized model pipelines and performance.
+
+## Generative AI with OpenVINO
+
+Get started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/genai-guide.html) to explore the capabilities of Generative AI using OpenVINO.
+
+Learn how to run LLMs and GenAI with [Samples](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples) in the [OpenVINO™ GenAI repo](https://github.com/openvinotoolkit/openvino.genai). See GenAI in action with 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).
+
+## Documentation
+
+[User documentation](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
+
+[Developer documentation](./docs/dev/index.md) focuses on the OpenVINO architecture and describes [building](./docs/dev/build.md)  and [contributing](./CONTRIBUTING.md) processes.
 
 ## OpenVINO Ecosystem
 
--   [🤗Optimum Intel](https://github.com/huggingface/optimum-intel) -  a simple interface to optimize Transformers and Diffusers models.
+### OpenVINO Tools
+
 -   [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity.
 -   [GenAI Repository](https://github.com/openvinotoolkit/openvino.genai) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications.
 -   [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving models optimized for Intel architectures.
 -   [Intel® Geti™](https://geti.intel.com/) - an interactive video and image annotation tool for computer vision use cases.
 
-Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!
+### Integrations
 
-## Documentation
+-   [🤗Optimum Intel](https://github.com/huggingface/optimum-intel) - grab and use models leveraging OpenVINO within the Hugging Face API.
+-   [Torch.compile](https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html) - use OpenVINO for Python-native applications by JIT-compiling code into optimized kernels.
+-   [OpenVINO LLMs inference and serving with vLLM](https://docs.vllm.ai/en/stable/getting_started/openvino-installation.html) - enhance vLLM's fast and easy model serving with the OpenVINO backend.
+-   [OpenVINO Execution Provider for ONNX Runtime](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html) - use OpenVINO as a backend with your existing ONNX Runtime code.
+-   [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/openvino/) - build context-augmented GenAI applications with the LlamaIndex framework and enhance runtime performance with OpenVINO.
+-   [LangChain](https://python.langchain.com/docs/integrations/llms/openvino/) - integrate OpenVINO with the LangChain framework to enhance runtime performance for GenAI applications.
 
-[User documentation](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
+Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!
 
-[Developer documentation](./docs/dev/index.md) focuses on how OpenVINO [components](./docs/dev/index.md#openvino-components) work and describes [building](./docs/dev/build.md)  and [contributing](./CONTRIBUTING.md) processes.
+## Performance
+
+Explore [OpenVINO Performance Benchmarks](https://docs.openvino.ai/2024/about-openvino/performance-benchmarks.html) to discover the optimal hardware configurations and plan your AI deployment based on verified data.
 
 ## Contribution and Support
 
@@ -118,9 +146,8 @@ You can ask questions and get support on:
 * The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on Stack Overflow\*.
 
 
-## Additional Resources
+## Resources
 
-* [Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
 * [Release Notes](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino.html)
 * [OpenVINO Blog](https://blog.openvino.ai/)
 * [OpenVINO™ toolkit on Medium](https://medium.com/@openvino)
@@ -145,4 +172,3 @@ By contributing to the project, you agree to the license and copyright terms the
 
 ---
 \* Other names and brands may be claimed as the property of others.
-