[DOCS] Apply Readme comments

README.md

@@ -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, and PaddlePaddle. 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
 
@@ -38,6 +46,8 @@ Learn how to optimize and deploy popular models with the [OpenVINO Notebooks](ht
 - [YOLOv8 Optimization](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/quantizing-model-with-accuracy-control/yolov8-quantization-with-accuracy-control.ipynb)
 - [Text-to-Image Generation](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/controlnet-stable-diffusion/controlnet-stable-diffusion.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**
@@ -84,25 +94,52 @@ 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, and PaddlePaddle [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 vLMM​](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.
+<details> <summary> More Integrations </summary>
+
+-   [OpenVINO model serving with Triton Inference Server​](https://github.com/triton-inference-server/openvino_backend)
+-   [OpenVINO model serving with TorchServe​](https://github.com/pytorch/serve/tree/master/examples/pt2/torch_compile_openvino)
+-   [OpenVINO LLMs with Qwen-Agent​​](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-agent-functioncall/llm-agent-functioncall-qwen.ipynb)
+-   [OpenVINO Natural Language Processing with Spark NLP​​​](https://medium.com/openvino-toolkit/the-need-for-speed-accelerating-nlp-inferencing-in-spark-nlp-with-openvino-runtime-327638fcec80)
+
+</details>
+
+Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!
+
+## Performance
 
-[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.
+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
 
@@ -116,9 +153,8 @@ 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)