Getting Started

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 4 notebook showcases a comprehensive walkthrough of the new capabilities of Llama 4 Scout models, including long context, multi-images and function calling.
The Build_with_Llama API notebook highlights some of the features of Llama API.
The inference folder contains scripts to deploy Llama for inference on server and mobile. See also 3p_integrations/vllm and 3p_integrations/tgi for hosting Llama on open-source model servers.
The RAG folder contains a simple Retrieval-Augmented Generation application using Llama.
The 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-cookbook finetuning code found in finetuning.py which supports these features.
The llama-tools folder contains resources to help you use Llama tools, such as llama-prompt-ops.