diff --git a/3p-integrations/togetherai/README.md b/3p-integrations/togetherai/README.md
index d0e6373a4..296dc6c95 100644
--- a/3p-integrations/togetherai/README.md
+++ b/3p-integrations/togetherai/README.md
@@ -14,7 +14,7 @@ While the code examples are primarily written in Python/JS, the concepts can be
 | -------- | ----------- | ---- |
 | [MultiModal RAG with Nvidia Investor Slide Deck](https://github.com/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/togetherai/multimodal_RAG_with_nvidia_investor_slide_deck.ipynb) | Multimodal RAG using Nvidia investor slides. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/multimodal_RAG_with_nvidia_investor_slide_deck.ipynb) [![](https://uohmivykqgnnbiouffke.supabase.co/storage/v1/object/public/landingpage/youtubebadge.svg)](https://youtu.be/IluARWPYAUc?si=gG90hqpboQgNOAYG)|
 | [Llama Contextual RAG](./llama_contextual_RAG.ipynb) | Implementation of Contextual Retrieval using Llama models. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/llama_contextual_RAG.ipynb) |
-| [Llama PDF to podcast](./pdf_to_podcast_using_llama_on_together.ipynb) | Generate a podcast from PDF content using Llama. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.comgithub/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb) |
+| [Llama PDF to podcast](./pdf_to_podcast_using_llama_on_together.ipynb) | Generate a podcast from PDF content using Llama. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb) |
 | [Knowledge Graphs with Structured Outputs](./knowledge_graphs_with_structured_outputs.ipynb) | Get Llama to generate knowledge graphs. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb) |
 | [Structured Text Extraction from Images](./structured_text_extraction_from_images.ipynb) | Extract structured text from images. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/structured_text_extraction_from_images.ipynb) |
 | [Text RAG](./text_RAG_using_llama_on_together.ipynb) | Implement text-based Retrieval-Augmented Generation. | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/text_RAG_using_llama_on_together.ipynb) |
diff --git a/end-to-end-use-cases/Multi-Modal-RAG/README.md b/end-to-end-use-cases/Multi-Modal-RAG/README.md
index cc4a0dcd5..c941ebf4b 100644
--- a/end-to-end-use-cases/Multi-Modal-RAG/README.md
+++ b/end-to-end-use-cases/Multi-Modal-RAG/README.md
@@ -13,7 +13,7 @@ This is a complete workshop on how to label images using the new Llama 3.2-Visio
 Before we start:
 
 1. Please grab your HF CLI Token from [here](https://huggingface.co/settings/tokens)
-2. Git clone [this dataset](https://huggingface.co/datasets/Sanyam/MM-Demo) inside the Multi-Modal-RAG folder: `git clone https://huggingface.co/datasets/Sanyam/MM-Demo`
+2. Git clone [this dataset](https://huggingface.co/datasets/Sanyam/MM-Demo) inside the Multi-Modal-RAG folder: `git clone https://huggingface.co/datasets/Sanyam/MM-Demo` (Remember to thank the original author by upvoting [Kaggle Dataset](https://www.kaggle.com/datasets/agrigorev/clothing-dataset-full))
 3. Make sure you grab a together.ai token [here](https://www.together.ai)
 
 ## Detailed Outline for running:
@@ -32,7 +32,7 @@ Here's the detailed outline:
 
 In this step we start with an unlabeled dataset and use the image captioning capability of the model to write a description of the image and categorize it.
 
-[Notebook for Step 1](./notebooks/Part_1_Data_Preperation.ipynb) and [Script for Step 1](./scripts/label_script.py)
+[Notebook for Step 1](./notebooks/Part_1_Data_Preparation.ipynb) and [Script for Step 1](./scripts/label_script.py)
 
 To run the script (remember to set n):
 ```
diff --git a/end-to-end-use-cases/Multi-Modal-RAG/notebooks/Part_1_Data_Preperation.ipynb b/end-to-end-use-cases/Multi-Modal-RAG/notebooks/Part_1_Data_Preparation.ipynb
similarity index 99%
rename from end-to-end-use-cases/Multi-Modal-RAG/notebooks/Part_1_Data_Preperation.ipynb
rename to end-to-end-use-cases/Multi-Modal-RAG/notebooks/Part_1_Data_Preparation.ipynb
index 2d346d6e2..80b22139a 100644
--- a/end-to-end-use-cases/Multi-Modal-RAG/notebooks/Part_1_Data_Preperation.ipynb
+++ b/end-to-end-use-cases/Multi-Modal-RAG/notebooks/Part_1_Data_Preparation.ipynb
@@ -5,9 +5,9 @@
    "id": "01af3b74-b3b9-4c1f-b41d-2911e7f19ffe",
    "metadata": {},
    "source": [
-    "## Data Preperation Notebook\n",
+    "## Data Preparation Notebook\n",
     "\n",
-    "To make the experience consistent, we will use [this link]() for getting access to our dataset. To credit, thanks to the author [here]() for making it available. \n",
+    "To make the experience consistent, we will use [this link](https://huggingface.co/datasets/Sanyam/MM-Demo) for getting access to our dataset. To credit, thanks to the author [here](https://www.kaggle.com/datasets/agrigorev/clothing-dataset-full) for making it available. \n",
     "\n",
     "As thanks to original author-Please upvote the dataset version on Kaggle if you enjoy this course."
    ]