This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning.
The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions.
The codebase covers a broad range of technical capabilities, including long-context document analysis, distributed GPU training, and quantization-based inference. It also details deployment strategies across cloud and on-premises environments, as well as methods for model checkpoint conversion and performance benchmarking.
The implementation examples are provided primarily through Jupyter Notebooks.
