explaingit

meta-llama/llama-cookbook

Analysis updated 2026-05-18

18,331Jupyter NotebookAudience · developerComplexity · 3/5LicenseSetup · moderate

TLDR

Official Meta collection of guides and code examples for building applications with Llama AI models, covering inference, fine-tuning, and retrieval-augmented generation.

Mindmap

mindmap
  root((repo))
    What it does
      Run inference
      Fine-tune models
      Retrieval search
      Build applications
    Use cases
      WhatsApp chatbot
      Paper analysis
      Character mapping
      Custom tasks
    Tech stack
      Jupyter Notebook
      Python
      Llama models
    Learning format
      Interactive notebooks
      Code examples
      Step-by-step guides
    Integrations
      Third-party hosts
      External services
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Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Build a WhatsApp chatbot that answers customer questions using Llama.

USE CASE 2

Fine-tune Llama on your company's documents to answer domain-specific questions.

USE CASE 3

Create a research paper analyzer that summarizes and extracts insights from academic PDFs.

USE CASE 4

Generate character relationship maps and story summaries from novels using retrieval-augmented generation.

What is it built with?

PythonJupyter NotebookLlamaPyTorch

How does it compare?

meta-llama/llama-cookbookgoogle-gemini/gemini-fullstack-langgraph-quickstartmahmoud/awesome-python-applications
Stars18,33118,16117,862
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultymoderatehardeasy
Complexity3/54/51/5
Audiencedeveloperdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires PyTorch and Llama model weights/API access, some examples need GPU or external model serving.

Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

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.

Copy-paste prompts

Prompt 1
Show me how to set up and run inference with a Llama model using the examples in this cookbook.
Prompt 2
Walk me through the fine-tuning notebook to adapt Llama for my custom use case with my own data.
Prompt 3
How do I build a retrieval-augmented generation system using Llama to answer questions from a document library?
Prompt 4
Show me the end-to-end WhatsApp chatbot example and explain each step.
Prompt 5
What integrations are available for hosting Llama models, and how do I use them?

Frequently asked questions

What is llama-cookbook?

Official Meta collection of guides and code examples for building applications with Llama AI models, covering inference, fine-tuning, and retrieval-augmented generation.

What language is llama-cookbook written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, Llama.

What license does llama-cookbook use?

Use freely for any purpose including commercial, as long as you keep the copyright notice.

How hard is llama-cookbook to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is llama-cookbook for?

Mainly developer.

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