Fix some more links

end-to-end-use-cases/RAFT-Chatbot/README.md

@@ -236,7 +236,7 @@ Once we evaluated and refined our RAFT model, we can deploy it locally to intera
 python recipes/inference/local_inference/inference.py --model_name raft-8b
 ```
 
-For more details,please check [local_inference recipe](../../../quickstart/inference/local_inference/README.md)
+For more details,please check [local_inference recipe](../../getting-started/inference/local_inference/README.md)
 
 ## Acknowledgement
 

end-to-end-use-cases/customerservice_chatbots/messenger_chatbot/messenger_llama3.md

@@ -10,7 +10,7 @@ Messenger from Meta is a messaging service that allows a Facebook business page
 
 The diagram below shows the components and overall data flow of the Llama 3 enabled Messenger chatbot demo we built, using an Amazon EC2 instance as an example for running the web server.
 
-![](../../../../docs/img/messenger_llama_arch.jpg)
+![](../../../src/docs/img/messenger_llama_arch.jpg)
 
 ## Getting Started with Messenger Platform
 
@@ -24,7 +24,7 @@ The diagram below shows the components and overall data flow of the Llama 3 enab
 
 5. Open Messenger's API Settings, as shown in the screenshot below, then in "1. Configure webhooks", set the Callback URL and Verify Token set up in the previous step, and subscribe all message related fields for "Webhook Fields". Finally, in "2. Generate access tokens", connect your Facebook page (see step 1) and copy your page access token for later use.
 
-![](../../../../docs/img/messenger_api_settings.png)
+![](../../../src/docs/img/messenger_api_settings.png)
 
 ## Writing Llama 3 Enabled Web App
 

getting-started/README.md

@@ -3,7 +3,6 @@
 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 [Running_Llama_Anywhere](./Running_Llama3_Anywhere/) notebooks demonstrate how to run Llama inference across Linux, Mac and Windows platforms using the appropriate tooling.
 * 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 [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.

getting-started/finetuning/singlegpu_finetuning.md

@@ -6,7 +6,7 @@ These are the instructions for using the canonical [finetuning script](../../src
 
 ## Requirements
 
-Ensure that you have installed the llama-recipes package ([details](../../../README.md#installing)).
+Ensure that you have installed the llama-recipes package.
 
 To run fine-tuning on a single GPU, we will make use of two packages:
 1. [PEFT](https://github.com/huggingface/peft) to use parameter-efficient finetuning.
@@ -33,7 +33,7 @@ The args used in the command above are:
 
 ### How to run with different datasets?
 
-Currently 3 open source datasets are supported that can be found in [Datasets config file](../../../src/llama_recipes/configs/datasets.py). You can also use your custom dataset (more info [here](./datasets/README.md)).
+Currently 3 open source datasets are supported that can be found in [Datasets config file](../../src/llama_recipes/configs/datasets.py). You can also use your custom dataset (more info [here](./datasets/README.md)).
 
 * `grammar_dataset` : use this [notebook](../../src/llama_recipes/datasets/grammar_dataset/grammar_dataset_process.ipynb) to pull and process the Jfleg and C4 200M datasets for grammar checking.
 

getting-started/inference/README.md

@@ -2,7 +2,5 @@
 
 This folder contains scripts to get you started with inference on Meta Llama models.
 
-* [Code Llama](./code_llama/) contains scripts for tasks relating to code generation using CodeLlama
 * [Local Inference](./local_inference/) contains scripts to do memory efficient inference on servers and local machines
-* [Mobile Inference](./mobile_inference/) has scripts using MLC to serve Llama on Android (h/t to OctoAI for the contribution!)
-* [Model Update Example](./modelUpgradeExample.py) shows an example of replacing a Llama 3 model with a Llama 3.1 model. 
+* [Mobile Inference](./mobile_inference/) has scripts using MLC to serve Llama on Android (h/t to OctoAI for the contribution!)