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meta-llama/llama-cookbook

📈 Trending18,330Jupyter NotebookAudience · developerComplexity · 3/5ActiveLicenseSetup · 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

Things people build with this

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.

Tech stack

PythonJupyter NotebookLlamaPyTorch

Getting 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?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.