[DOCS] relnotes 24.3 first pass fix (#25799)

Author: kblaszczak-intel

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

@@ -1,9 +1,10 @@
+OpenVINO Release Notes
+=============================
+
 .. meta::
    :description: See what has changed in OpenVINO with the latest release, as well as all
                  previous releases in this year's cycle.
 
-OpenVINO Release Notes
-=============================
 
 .. toctree::
    :maxdepth: 1
@@ -14,7 +15,7 @@ OpenVINO Release Notes
 
 
 
-2024.3 - 30 July 2024
+2024.3 - 31 July 2024
 #############################
 
 :doc:`System Requirements <./release-notes-openvino/system-requirements>` | :doc:`Release policy <./release-notes-openvino/release-policy>` | :doc:`Installation Guides <./../get-started/install-openvino>`
@@ -23,21 +24,21 @@ OpenVINO Release Notes
 What's new
 +++++++++++++++++++++++++++++
 
-More Gen AI coverage and framework integrations to minimize code changes.
+* More Gen AI coverage and framework integrations to minimize code changes.
 
-* OpenVINO pre-optimized models are now available in Hugging Face making it easier for developers
-  to get started with these models.
+  * OpenVINO pre-optimized models are now available in Hugging Face making it easier for developers
+    to get started with these models.
 
-Broader Large Language Model (LLM) support and more model compression techniques.
+* Broader Large Language Model (LLM) support and more model compression techniques.
 
-* Significant improvement in LLM performance on Intel built-in and discrete GPUs with the addition
-  of dynamic quantization, Multi-Head Attention (MHA), and OneDNN enhancements.
+  * Significant improvement in LLM performance on Intel discrete GPUs with the addition of
+    Multi-Head Attention (MHA) and OneDNN enhancements.
 
-More portability and performance to run AI at the edge, in the cloud, or locally.
+* More portability and performance to run AI at the edge, in the cloud, or locally.
 
-* Improved CPU performance when serving LLMs with the inclusion of vLLM and continuous batching
-  in the OpenVINO Model Server (OVMS). vLLM is an easy-to-use open-source library that supports
-  efficient LLM inferencing and model serving.
+  * Improved CPU performance when serving LLMs with the inclusion of vLLM and continuous batching
+    in the OpenVINO Model Server (OVMS). vLLM is an easy-to-use open-source library that supports
+    efficient LLM inferencing and model serving.
 
 
 
@@ -59,7 +60,7 @@ Common
 
   * Increasing support for models like YoloV10 or PixArt-XL-2, thanks to enabling Squeeze and
     Concat layers.
-  * Performance of precision conversion fp16/bf16 -> fp32.
+  * Performance of precision conversion FP16/BF16 -> FP32.
 
 
 
@@ -97,9 +98,6 @@ GPU Device Plugin
 
   * LLMs and Stable Diffusion on discrete GPUs, due to latency decrease, through optimizations
     such as Multi-Head Attention (MHA) and oneDNN improvements.
-  * First token latency of LLMs for large input cases on Core Ultra integrated GPU. It can be
-    further improved with dynamic quantization enabled with an application
-    `interface <https://docs.openvino.ai/2024/api/c_cpp_api/group__ov__dev__exec__model.html#_CPPv4N2ov4hint31dynamic_quantization_group_sizeE>`__.
   * Whisper models on discrete GPU.
 
 
@@ -191,7 +189,7 @@ Neural Network Compression Framework
   Act->MatMul and Act->MUltiply->MatMul to cover the Phi family models.
 * The representation of symmetrically quantized weights has been updated to a signed data type
   with no zero point. This allows NPU to support compressed LLMs with the symmetric mode.
-* bf16 models in Post-Training Quantization are now supported; nncf.quantize().
+* BF16 models in Post-Training Quantization are now supported; nncf.quantize().
 * `Activation Sparsity <https://arxiv.org/abs/2310.17157>`__ (Contextual Sparsity) algorithm in
   the Weight Compression method is now supported (preview), speeding up LLM inference.
   The algorithm is enabled by setting the ``target_sparsity_by_scope`` option in
@@ -431,7 +429,7 @@ Previous 2024 releases
      compression of LLMs. Enabled by `gptq=True`` in nncf.compress_weights().
    * Scale Estimation algorithm for more accurate 4-bit compressed LLMs. Enabled by
      `scale_estimation=True`` in nncf.compress_weights().
-   * Added support for models with bf16 weights in nncf.compress_weights().
+   * Added support for models with BF16 weights in nncf.compress_weights().
    * nncf.quantize() method is now the recommended path for quantization initialization of
      PyTorch models in Quantization-Aware Training. See example for more details.
    * compressed_model.nncf.get_config() and nncf.torch.load_from_config() API have been added to

docs/sphinx_setup/index.rst

@@ -5,11 +5,11 @@ OpenVINO 2024.3
 .. meta::
    :google-site-verification: _YqumYQ98cmXUTwtzM_0WIIadtDc6r_TMYGbmGgNvrk
 
-**OpenVINO is an open-source toolkit** for optimizing and deploying deep learning models from cloud
-to edge. It accelerates deep learning inference across various use cases, such as generative AI, video,
-audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more.
-Convert and optimize models, and deploy across a mix of Intel® hardware and environments, on-premises
-and on-device, in the browser or in the cloud.
+**OpenVINO is an open-source toolkit** for optimizing and deploying deep learning models from
+cloud to edge. It accelerates deep learning inference across various use cases, such as
+generative AI, video, audio, and language with models from popular frameworks like PyTorch,
+TensorFlow, ONNX, and more. Convert and optimize models, and deploy across a mix of Intel®
+hardware and environments, on-premises and on-device, in the browser or in the cloud.
 
 Check out the `OpenVINO Cheat Sheet. <https://docs.openvino.ai/2024/_static/download/OpenVINO_Quick_Start_Guide.pdf>`__
 
@@ -26,16 +26,21 @@ Check out the `OpenVINO Cheat Sheet. <https://docs.openvino.ai/2024/_static/down
            <div class="splide__track">
          		<ul class="splide__list">
                   <li id="ov-homepage-slide1" class="splide__slide">
+                  <p class="ov-homepage-slide-title">OpenVINO models on Hugging Face!</p>
+                  <p class="ov-homepage-slide-subtitle">Get pre-optimized OpenVINO models, no need to convert!</p>
+                  <a class="ov-homepage-banner-btn" href="https://huggingface.co/OpenVINO">Visit Hugging Face</a>
+                  </li>
+                  <li id="ov-homepage-slide2" class="splide__slide">
                   <p class="ov-homepage-slide-title">New Generative AI API</p>
                   <p class="ov-homepage-slide-subtitle">Generate text with LLMs in only a few lines of code!</p>
                   <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/genai-guide.html">Check out our guide</a>
                   </li>
-                  <li id="ov-homepage-slide2" class="splide__slide">
+                  <li id="ov-homepage-slide3" class="splide__slide">
                   <p class="ov-homepage-slide-title">Improved model serving</p>
                   <p class="ov-homepage-slide-subtitle">OpenVINO Model Server has improved parallel inferencing!</p>
                   <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/ovms_what_is_openvino_model_server.html">Learn more</a>
                   </li>
-                  <li id="ov-homepage-slide3" class="splide__slide">
+                  <li id="ov-homepage-slide4" class="splide__slide">
                   <p class="ov-homepage-slide-title">OpenVINO via PyTorch 2.0 torch.compile()</p>
                   <p class="ov-homepage-slide-subtitle">Use OpenVINO directly in PyTorch-native applications!</p>
                   <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html">Learn more</a>