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Documentation
Overview
Models
Getting the Models
Running Llama
How-To Guides
Integration Guides
Community Support
Community
Community Stories
Open Innovation AI Research Community
Llama Impact Grants
Resources
Cookbook
Case studies
Videos
AI at Meta Blog
Meta Newsroom
FAQ
Privacy Policy
Terms
Cookies
Llama Protections
Overview
Llama Defenders Program
Developer Use Guide
Documentation
Overview
Models
Getting the Models
Running Llama
How-To Guides
Integration Guides
Community Support
Community
Community Stories
Open Innovation AI Research Community
Llama Impact Grants
Resources
Cookbook
Case studies
Videos
AI at Meta Blog
Meta Newsroom
FAQ
Privacy Policy
Terms
Cookies
Llama Protections
Overview
Llama Defenders Program
Developer Use Guide
Documentation
Overview
Models
Getting the Models
Running Llama
How-To Guides
Integration Guides
Community Support
Community
Community Stories
Open Innovation AI Research Community
Llama Impact Grants
Resources
Cookbook
Case studies
Videos
AI at Meta Blog
Meta Newsroom
FAQ
Privacy Policy
Terms
Cookies
Llama Protections
Overview
Llama Defenders Program
Developer Use Guide

Table Of Contents

Overview
Models
Llama 4
Llama Guard 4 (New)
Llama 3.3
Llama 3.2
Llama 3.1
Llama Guard 3
Llama Prompt Guard 2 (New)
Other models
Getting the Models
Meta
Hugging Face
Kaggle
1B/3B Partners
405B Partners
Running Llama
Linux
Windows
Mac
Cloud
How-To Guides
Fine-tuning
Quantization
Prompting
Validation
Vision Capabilities
Responsible Use
Integration Guides
LangChain
Llamalndex
Community Support
Resources

Overview
Models
Llama 4
Llama Guard 4 (New)
Llama 3.3
Llama 3.2
Llama 3.1
Llama Guard 3
Llama Prompt Guard 2 (New)
Other models
Getting the Models
Meta
Hugging Face
Kaggle
1B/3B Partners
405B Partners
Running Llama
Linux
Windows
Mac
Cloud
How-To Guides
Fine-tuning
Quantization
Prompting
Validation
Vision Capabilities
Responsible Use
Integration Guides
LangChain
Llamalndex
Community Support
Resources

How-to guides

The pages in this section provide an overview of the processes commonly involved in developing any LLM:

  • Fine-tuning
  • Quantization
  • Prompt engineering
  • Model validation
  • Note: The prompt format for Meta Llama models does vary from one model to another, so for prompt guidance specific to a given model, see the Models sections.
    In addition to the above information, this section also contains a collection of responsible-use resources to assist you in enhancing the safety of your models.
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