Analysis updated 2026-05-18
Build a WhatsApp chatbot that answers customer questions using Llama.
Fine-tune Llama on your company's documents to answer domain-specific questions.
Create a research paper analyzer that summarizes and extracts insights from academic PDFs.
Generate character relationship maps and story summaries from novels using retrieval-augmented generation.
| meta-llama/llama-cookbook | google-gemini/gemini-fullstack-langgraph-quickstart | mahmoud/awesome-python-applications | |
|---|---|---|---|
| Stars | 18,331 | 18,161 | 17,862 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Setup difficulty | moderate | hard | easy |
| Complexity | 3/5 | 4/5 | 1/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires PyTorch and Llama model weights/API access, some examples need GPU or external model serving.
The Llama Cookbook is the official collection of guides and example code maintained by Meta for building applications using the Llama family of AI language models. It is primarily organized as a set of Jupyter notebooks (interactive documents that combine code and explanations) and covers three main areas: running inference (getting a model to generate responses), fine-tuning (adapting a pre-trained model to a specific task with new data), and retrieval-augmented generation (a technique that lets a model answer questions by first searching a document library for relevant information). Beyond those fundamentals, the repository contains examples of complete end-to-end applications, such as building a chatbot integrated with WhatsApp, analyzing research papers, or generating character relationship maps from a novel. A separate section covers integrations with third-party hosting providers and services. The repository was previously called llama-recipes and was renamed to its current name. It supports multiple generations of the Llama model family. Each model version has its own license that must be reviewed separately before use.
Official Meta collection of guides and code examples for building applications with Llama AI models, covering inference, fine-tuning, and retrieval-augmented generation.
Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, Llama.
Use freely for any purpose including commercial, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.