minor fixes in notebooks

notebooks/ct-segmentation-quantize/README.md

@@ -8,7 +8,7 @@
 
 ## Notebook Contents
 
-This folder contains five notebooks that show how to train,
+This folder contains four notebooks that show how to train,
 optimize, quantize and show live inference on a [MONAI](https://monai.io/) segmentation model with
 [PyTorch Lightning](https://lightning.ai/) and OpenVINO:
 

notebooks/llava-next-multimodal-chatbot/llava-next-multimodal-chatbot.ipynb

@@ -642,7 +642,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "IMAGE_ENCODER_PATH_INT8 = IMAGE_ENCODER_PATH.parent / IMAGE_ENCODER_PATH.name.replace(\".xml\", \"-int4.xml\")\n",
+    "IMAGE_ENCODER_PATH_INT8 = IMAGE_ENCODER_PATH.parent / IMAGE_ENCODER_PATH.name.replace(\".xml\", \"-int8.xml\")\n",
     "\n",
     "\n",
     "import requests\n",
@@ -1307,7 +1307,7 @@
    "source": [
     "use_int8_image_encoder = widgets.Checkbox(\n",
     "    value=IMAGE_ENCODER_PATH_INT8.exists(),\n",
-    "    description=\"INT4 language model\",\n",
+    "    description=\"INT8 image encoder\",\n",
     "    disabled=not IMAGE_ENCODER_PATH_INT8.exists(),\n",
     ")\n",
     "\n",

notebooks/llm-chatbot/README.md

@@ -27,7 +27,7 @@ The available options are:
 
 * **llama-3-8b-instruct** - Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. More details about model can be found in [Meta blog post](https://ai.meta.com/blog/meta-llama-3/), [model website](https://llama.meta.com/llama3) and [model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
 >**Note**: run model with demo, you will need to accept license agreement. 
->You must be a registered user in 🤗 Hugging Face Hub. Please visit [HuggingFace model card](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), carefully read terms of usage and click accept button.  You will need to use an access token for the code below to run. For more information on access tokens, refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
+>You must be a registered user in 🤗 Hugging Face Hub. Please visit [HuggingFace model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), carefully read terms of usage and click accept button.  You will need to use an access token for the code below to run. For more information on access tokens, refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
 * **qwen1.5-0.5b-chat/qwen1.5-1.8b-chat/qwen1.5-7b-chat** - Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. Qwen1.5 is a language model series including decoder language models of different model sizes. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention. You can find more details about model in the [model repository](https://huggingface.co/Qwen).
 * **qwen-7b-chat** - Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. For more details about Qwen, please refer to the [GitHub](https://github.com/QwenLM/Qwen) code repository.
 * **mpt-7b-chat** - MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT-7B-chat is a chatbot-like model for dialogue generation. It was built by finetuning MPT-7B on the ShareGPT-Vicuna, [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and Evol-Instruct datasets. More details about the model can be found in [blog post](https://www.mosaicml.com/blog/mpt-7b), [repository](https://github.com/mosaicml/llm-foundry/) and [HuggingFace model card](https://huggingface.co/mosaicml/mpt-7b-chat).

notebooks/mobileclip-video-search/mobileclip-video-search.ipynb

@@ -813,7 +813,7 @@
     "## Interactive Demo\n",
     "[back to top ⬆️](#Table-of-contents:)\n",
     "\n",
-    "In this part, you can try different supported by tutorial models in searching frames in the video by text query or image. Upload v"
+    "In this part, you can try different supported by tutorial models in searching frames in the video by text query or image. Upload video and provide text query or reference image for search and model will find the most relevant frames according to provided query. Please note, different models can require different optimal threshold for search."
    ]
   },
   {