diff --git a/end-to-end-use-cases/RAFT-Chatbot/README.md b/end-to-end-use-cases/RAFT-Chatbot/README.md index b500944a205197e3e6398e77048d575aa086ce84..2f5160da6eba813204583f4934ab6a4b1af4c2be 100644 --- a/end-to-end-use-cases/RAFT-Chatbot/README.md +++ b/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 diff --git a/end-to-end-use-cases/customerservice_chatbots/messenger_chatbot/messenger_llama3.md b/end-to-end-use-cases/customerservice_chatbots/messenger_chatbot/messenger_llama3.md index a410a5647c2615ef041eadb865a2a0e1e22859d2..f085a9911a7918064cf92c1569eb924271ad0707 100644 --- a/end-to-end-use-cases/customerservice_chatbots/messenger_chatbot/messenger_llama3.md +++ b/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. - + ## 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. - + ## Writing Llama 3 Enabled Web App diff --git a/getting-started/README.md b/getting-started/README.md index c09ac3d532bbb5fa1503d4e9d989e4291e46764c..bfde987b8cd705726bf3e6b6584c9e4ae395d25e 100644 --- a/getting-started/README.md +++ b/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. diff --git a/getting-started/finetuning/singlegpu_finetuning.md b/getting-started/finetuning/singlegpu_finetuning.md index 8ab3d8a9833431eb2fde85d54eea231c4c4bfd50..80689d4eaf5c66bc116e822909ec5033d45666ad 100644 --- a/getting-started/finetuning/singlegpu_finetuning.md +++ b/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. diff --git a/getting-started/inference/README.md b/getting-started/inference/README.md index 7f48aba701a00b703b4349cfecdcaa6baa7992e4..afa6ffdf387e02e15d1f3001c3426175a00c60ad 100644 --- a/getting-started/inference/README.md +++ b/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. \ No newline at end of file +* [Mobile Inference](./mobile_inference/) has scripts using MLC to serve Llama on Android (h/t to OctoAI for the contribution!) \ No newline at end of file