fix links

end-to-end-use-cases/Multi-Modal-RAG/README.md

@@ -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_Preparation.ipynb) and [Script for Step 1](./scripts/label_script.py)
+[Notebook for Step 1](./notebooks/Part_1_Data_Preperation.ipynb) and [Script for Step 1](./scripts/label_script.py)
 
 To run the script (remember to set n):
 ```

getting-started/README.md

@@ -3,7 +3,7 @@
 If you are new to developing with Meta Llama models, this is where you should start. This folder contains introductory-level notebooks across different techniques relating to Meta Llama.
 
 * The [Build_with_Llama 3.2](./build_with_Llama_3_2.ipynb) notebook showcases a comprehensive walkthrough of the new capabilities of Llama 3.2 models, including multimodal use cases, function/tool calling, Llama Stack, and Llama on edge.
-* The [Prompt_Engineering_with_Llama](./Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
+* The [Prompt_Engineering_with_Llama](./Prompt_Engineering_with_Llama.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters.
 * The [inference](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [3p_integrations/vllm](../3p-integrations/vllm/) and [3p_integrations/tgi](../3p-integrations/tgi/) for hosting Llama on open-source model servers.
 * The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama.
 * The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in [finetuning.py](../src/llama_recipes/finetuning.py) which supports these features:

getting-started/finetuning/finetune_vision_model.md

@@ -28,7 +28,7 @@ For **finetuning with LLM freeze using FSDP**, we can run the following code:
 
 For more details about the finetuning configurations, please read the [finetuning readme](./README.md).
 
-For more details about local inference with the fine-tuned checkpoint, please read [Inference with FSDP checkpoints section](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/inference/local_inference#inference-with-fsdp-checkpoints) to learn how to convert the FSDP weights into a consolidated Hugging Face formatted model for local inference.
+For more details about local inference with the fine-tuned checkpoint, please read [Inference with FSDP checkpoints section](../../getting-started/inference/local_inference/#inference-with-fsdp-checkpoints) to learn how to convert the FSDP weights into a consolidated Hugging Face formatted model for local inference.
 
 ### How to use a custom dataset to fine-tune vision model