File name updates to address the Lint changes

Author: pia-papanna

.github/scripts/spellcheck_conf/wordlist.txt

@@ -1390,4 +1390,7 @@ chatbot's
 Lamini
 lamini
 nba
-sqlite
+sqlite
+customerservice
+fn
+ExecuTorch

recipes/responsible_ai/README.md

@@ -8,4 +8,4 @@ Meta Llama Guard and Meta Llama Guard 2 are new models that provide input and ou
 The [llama_guard](llama_guard) folder contains the inference script to run Meta Llama Guard locally. Add test prompts directly to the [inference script](llama_guard/inference.py) before running it.
 
 ### Running on the cloud
-The notebooks [Purple_Llama_Anyscale](purple_llama_anyscale.ipynb) & [Purple_Llama_OctoAI](purple_llama_octoai.ipynb) contain examples for running Meta Llama Guard on cloud hosted endpoints.
+The notebooks [Purple_Llama_Anyscale](Purple_Llama_Anyscale.ipynb) & [Purple_Llama_OctoAI](Purple_Llama_Octoai.ipynb) contain examples for running Meta Llama Guard on cloud hosted endpoints.

recipes/use_cases/README.md

@@ -13,11 +13,11 @@ This step-by-step tutorial shows how to use the [WhatsApp Business API](https://
 ## [Messenger Chatbot](./customerservice_chatbots/messenger_llama/messenger_llama3.md): Building a Llama 3 Enabled Messenger Chatbot
 This step-by-step tutorial shows how to use the [Messenger Platform](https://developers.facebook.com/docs/messenger-platform/overview) to build a Llama 3 enabled Messenger chatbot.
 
-### RAG Chatbot Example (running [locally](./customerservice_chatbots/RAG_chatbot/RAG_chatbot_example.ipynb) or on [OctoAI](../3p_integration/octoai/RAG_chatbot_example/RAG_chatbot_example.ipynb))
+### RAG Chatbot Example (running [locally](./customerservice_chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb) or on [OctoAI](../3p_integration/octoai/RAG_chatbot_example/RAG_chatbot_example.ipynb))
 A complete example of how to build a Llama 3 chatbot hosted on your browser that can answer questions based on your own data using retrieval augmented generation (RAG). You can run Llama2 locally if you have a good enough GPU or on OctoAI if you follow the note [here](../README.md#octoai_note).
 
 ## [Sales Bot](./customerservice_chatbots/sales_bot/SalesBot.ipynb): Sales Bot with Llama3 - A Summarization and RAG Use Case
 An summarization + RAG use case built around the Amazon product review Kaggle dataset to build a helpful Music Store Sales Bot. The summarization and RAG are built on top of Llama models hosted on OctoAI, and the vector database is hosted on Weaviate Cloud Services.
 
-## [Media Generation](./mediagen.ipynb): Building a Video Generation Pipeline with Llama3
+## [Media Generation](./MediaGen.ipynb): Building a Video Generation Pipeline with Llama3
 This step-by-step tutorial shows how to use leverage Llama 3 to drive the generation of animated videos using SDXL and SVD. More specifically it relies on JSON formatting to produce a scene-by-scene story board of a recipe video. The user provides the name of a dish, then Llama 3 describes a step by step guide to reproduce the said dish. This step by step guide is brought to life with models like SDXL and SVD.

recipes/use_cases/multilingual/README.md

@@ -118,7 +118,7 @@ phase2_ds.save_to_disk("data/phase2")
 ```
 
 ### Train
-Finally, we can start finetuning Llama2 on these datasets by following the [finetuning recipes](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/finetuning). Remember to pass the new tokenizer path as an argument to the script: `--tokenizer_name=./extended_tokenizer`.
+Finally, we can start finetuning Llama2 on these datasets by following the [finetuning recipes](../../quickstart/finetuning/). Remember to pass the new tokenizer path as an argument to the script: `--tokenizer_name=./extended_tokenizer`.
 
 OpenHathi was trained on 64 A100 80GB GPUs. Here are the hyperparameters used and other training details:
 - maximum learning rate: 2e-4