llama-cookbook
Llama Cookbook is Meta's official guide for building applications with Llama models, providing Jupyter notebooks and code examples for inference, fine-tuning, and RAG workflows. It covers practical recipes across multiple domains and integrates with popular tools like LangChain and vLLM.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | meta-llama/llama-cookbook |
| Owner | meta-llama |
| Primary language | Jupyter Notebook |
| License | MIT — OSI-approved |
| Stars | 18.4k |
| Forks | 2.7k |
| Open issues | 86 |
| Latest release | v0.0.5 (2025-01-22) |
| Last updated | 2026-05-19 |
| Source | https://github.com/meta-llama/llama-cookbook |
What llama-cookbook is
MIT-licensed collection of Jupyter notebooks demonstrating Llama model deployment via inference APIs, parameter-efficient fine-tuning, retrieval-augmented generation, and end-to-end use cases. Supports Llama 2, 3.x, 3.3, and 4 variants with integrations for LangChain, PyTorch, and third-party inference providers.
Get the llama-cookbook source
Clone the repository and explore it locally.
git clone https://github.com/meta-llama/llama-cookbook.gitcd llama-cookbook# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Notebooks are educational; plan for refactoring into production code (modularization, error handling, logging). Not a pluggable library.
- Fine-tuning examples assume GPU access (PyTorch). Verify hardware requirements and cost before scaling to team or production workloads.
- Recipes depend on third-party integrations (LangChain, vLLM, Hugging Face). Pin dependency versions; breaking changes can affect reproduced examples.
- Model licenses (Llama 2, 3.x, 4) vary. Commercial use requires explicit licensing agreement per model version; internal use policies may differ from public deployment.
- Examples reference Llama API (closed-access inference). Switching to self-hosted inference (vLLM, Ollama) requires independent validation of performance and cost.
When to avoid it — and what to weigh
- Seeking Production Infrastructure as Code — This is a recipe/tutorial collection, not a deployable framework. No Terraform, Kubernetes manifests, or containerized deployment pipelines are provided. Requires separate DevOps effort.
- Needing Enterprise Security Hardening Guidance — Repository focuses on functional examples, not security best practices (encryption, access control, audit logging, threat modeling). Treat as educational starting point only.
- Requiring Guaranteed Model License Clarity for Commercial Use — Repository references separate model licenses (Llama 2, 3.x, 4) in external repos. Acceptable Use Policies apply per model. Requires legal review before production deployment.
- Building on Rapidly Evolving Model Versions Without Maintenance Commitment — Last push was 2026-05-19, but notebook examples may lag behind latest model releases. Fine-tuning code patterns and API examples require continuous updates as Llama versions change.
License & commercial use
Llama Cookbook repository itself is MIT Licensed (permissive, allows commercial use). However, underlying Llama model weights (Llama 2, 3.x, 3.3, 4) are subject to separate proprietary licenses with Acceptable Use Policies. Model licenses must be reviewed independently; not all versions permit commercial deployment.
The code/cookbook content is MIT-licensed and commercially usable. However, the Llama model weights themselves are not freely commercializable without explicit licensing from Meta. Any production deployment requires: (1) acceptance of the target Llama model's license and Acceptable Use Policy, (2) verification that your use case is permitted (e.g., no illegal content, NSFW restrictions). Internal/non-public use may have different terms than public APIs. Legal review mandatory before commercial release.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Repository examples are not hardened for production. Considerations include: API keys/credentials are often shown inline (replace with environment variables/secret management before production); no guidance on prompt injection prevention or model output filtering; inference infrastructure security (whether using Llama API or self-hosted) is not covered. Teams must implement separate validation, rate limiting, and access controls.
Alternatives to consider
Hugging Face Transformers + Example Scripts
Direct access to model architectures and training scripts; more flexible but requires deeper expertise. Covers Llama models but broader scope (all transformer models). Less Meta-specific guidance.
LlamaIndex + LangChain Official Examples
Focused on RAG and chaining patterns; complements cookbook but emphasizes framework-specific patterns over foundational Llama recipes. Better for production RAG applications.
Ollama Documentation + Examples
Simpler entry point for local Llama inference; no notebooks or fine-tuning focus. Lighter weight than Llama Cookbook, better for quick prototyping; weaker on fine-tuning and advanced recipes.
Build on llama-cookbook with DEV.co software developers
Start with the Llama Cookbook's getting-started notebooks for inference or fine-tuning. Review model licenses and Acceptable Use Policies, then fork or clone the repository to adapt recipes for your use case.
Talk to DEV.coRelated open-source tools
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llama-cookbook FAQ
Can I use Llama Cookbook code in production without modification?
Do I need a Llama API key to use all the recipes?
Are the model weights themselves covered by MIT license?
How do I know if a recipe is compatible with the latest Llama version?
Software developers & web developers for hire
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If llama-cookbook is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Build with Llama?
Start with the Llama Cookbook's getting-started notebooks for inference or fine-tuning. Review model licenses and Acceptable Use Policies, then fork or clone the repository to adapt recipes for your use case.