fix broken links

Author: eaidova

notebooks/deepseek-r1/README.md

@@ -11,7 +11,7 @@ The tutorial supports different models, you can select one from the provided opt
 * **DeepSeek-R1-Distill-Llama-8B** is a distilled model based on [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), that prioritizes high performance and advanced reasoning capabilities, particularly excelling in tasks requiring mathematical and factual precision. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) for more info.
 * **DeepSeek-R1-Distill-Qwen-1.5B** is the smallest DeepSeek-R1 distilled model based on [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B). Despite its compact size, the model demonstrates strong capabilities in solving basic mathematical tasks, at the same time its programming capabilities are limited. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for more info.
 * **DeepSeek-R1-Distill-Qwen-7B** is a distilled model based on [Qwen-2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). The model demonstrates a good balance between mathematical and factual reasoning and can be less suited for complex coding tasks. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) for more info.
-* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-15B) for more info.
+* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more info.
 
 ## Notebook Contents
 

notebooks/deepseek-r1/deepseek-r1.ipynb

@@ -109,7 +109,7 @@
     "* **DeepSeek-R1-Distill-Llama-8B** is a distilled model based on [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), that prioritizes high performance and advanced reasoning capabilities, particularly excelling in tasks requiring mathematical and factual precision. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) for more info.\n",
     "* **DeepSeek-R1-Distill-Qwen-1.5B** is the smallest DeepSeek-R1 distilled model based on [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B). Despite its compact size, the model demonstrates strong capabilities in solving basic mathematical tasks, at the same time its programming capabilities are limited. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for more info.\n",
     "* **DeepSeek-R1-Distill-Qwen-7B** is a distilled model based on [Qwen-2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). The model demonstrates a good balance between mathematical and factual reasoning and can be less suited for complex coding tasks. Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) for more info.\n",
-    "* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-15B) for more info.\n",
+    "* **DeepSeek-R1-Distil-Qwen-14B** is a distilled model based on [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) that has great competence in factual reasoning and solving complex mathematical tasks.  Check [model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more info.\n",
     "\n",
     "[Weight compression](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html) is a technique for enhancing the efficiency of models, especially those with large memory requirements. This method reduces the model’s memory footprint, a crucial factor for Large Language Models (LLMs). We provide several options for model weight compression:\n",
     "\n",

notebooks/hugging-face-hub/README.md

@@ -5,7 +5,7 @@
 The Hugging Face (HF) Model Hub is a central repository for pre-trained deep learning models. It allows exploration and provides access to thousands of models for a wide range of tasks, including text classification, question answering, and image classification.
 Hugging Face provides Python packages that serve as APIs and tools to easily download and fine tune state-of-the-art pretrained models, namely [transformers] and [diffusers] packages.
 
-![](https://github.com/huggingface/optimum-intel/raw/main/readme_logo.png)
+![](https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/logo/hf_intel_logo.png)
 
 ## Contents: 
 Throughout this notebook we will learn:

notebooks/hugging-face-hub/hugging-face-hub.ipynb

@@ -10,7 +10,7 @@
     "The Hugging Face (HF) [Model Hub](https://huggingface.co/models) is a central repository for pre-trained deep learning models. It allows exploration and provides access to thousands of models for a wide range of tasks, including text classification, question answering, and image classification.\n",
     "Hugging Face provides Python packages that serve as APIs and tools to easily download and fine tune state-of-the-art pretrained models, namely [transformers](https://github.com/huggingface/transformers) and [diffusers](https://github.com/huggingface/diffusers) packages.\n",
     "\n",
-    "![](https://github.com/huggingface/optimum-intel/raw/main/readme_logo.png)\n",
+    "![](https://huggingface.co/datasets/optimum/documentation-images/resolve/main/intel/logo/hf_intel_logo.png)\n",
     "\n",
     "Throughout this notebook we will learn:\n",
     "1. How to load a HF pipeline using the `transformers` package and then convert it to OpenVINO.\n",

notebooks/outetts-text-to-speech/outetts-text-to-speech.ipynb

@@ -66,8 +66,23 @@
     "\n",
     "from pip_helper import pip_install\n",
     "\n",
-    "pip_install(\"torch>=2.1\", \"torchaudio\", \"einops\", \"transformers>=4.46.1\", \"loguru\", \"inflect\", \"pesq\", \"torchcrepe\",\n",
-    "             \"natsort\" \"polars\" \"uroman\", \"mecab-python3\" \"unidic-lite\", \"--extra-index-url\", \"https://download.pytorch.org/whl/cpu\")\n",
+    "pip_install(\n",
+    "    \"torch>=2.1\",\n",
+    "    \"torchaudio\",\n",
+    "    \"einops\",\n",
+    "    \"transformers>=4.46.1\",\n",
+    "    \"loguru\",\n",
+    "    \"inflect\",\n",
+    "    \"pesq\",\n",
+    "    \"torchcrepe\",\n",
+    "    \"natsort\",\n",
+    "    \"polars\",\n",
+    "    \"uroman\",\n",
+    "    \"mecab-python3\",\n",
+    "    \"unidic-lite\",\n",
+    "    \"--extra-index-url\",\n",
+    "    \"https://download.pytorch.org/whl/cpu\",\n",
+    ")\n",
     "pip_install(\"gradio>=4.19\", \"openvino>=2024.4.0\", \"tqdm\", \"pyyaml\", \"librosa\", \"soundfile\")\n",
     "pip_install(\"git+https://github.com/huggingface/optimum-intel.git\", \"--extra-index-url\", \"https://download.pytorch.org/whl/cpu\")\n",
     "\n",