Documentation
How-to guides
A collection of hands-on guides covering using Llama models.
Develop with Llama
This collection of guides provides a comprehensive overview of best practices, techniques, and tools for developing with Llama. Learn how to optimize, evaluate, and responsibly deploy Llama-based large language models, covering topics from prompt engineering and fine-tuning to quantization, distillation, validation, and ethical considerations.
Prompt engineering
Prompt engineering is one of the easiest ways for developers to improve models output quality and relevance, without needing to retrain or fine-tune the model.
Master techniques like few-shot, chain-of-thought, and role-based prompting to get more accurate, relevant, and creative outputs from your models.
Fine-tuning
Fine-tuning allows you to customize Llama for your specific domain or task, dramatically improving accuracy, consistency, and efficiency.
Learn when and how to use full-parameter fine-tuning, LoRA, QLoRA, and RLHF, and discover the best libraries and workflows for custom model training.
Quantization
Quantization is a powerful optimization technique that enables Llama models to run faster and use less memory, making them suitable for edge devices and large-scale deployments.
Learn practical methods and tools for quantizing Llama models, and how to balance memory, speed, and accuracy for efficient deployment.
Distillation
Model distillation is a key technique for transferring the capabilities of large Llama models into smaller, faster ones, making high-quality AI accessible in resource-constrained environments.
Discover how to generate synthetic data, apply advanced distillation signals, and evaluate student models for efficient, high-performance AI.
Validation
Rigorous validation is the foundation of reliable AI systems, ensuring your Llama models meet performance expectations before deployment.
Discover quantitative and qualitative techniques—including loss/perplexity tracking, cross-validation, and manual review—to ensure your fine-tuned Llama models perform reliably in real-world scenarios.
Vision Capabilities
Llama’s multimodal models empower developers to build applications that understand both images and text, opening up new possibilities for natural, flexible AI interactions.
Learn how to leverage document OCR, visual reasoning, and creative image-based tasks using Llama3.2’s advanced vision features.
Responsible Use Guide
Responsible development with Llama requires a thoughtful approach to safety, ethics, and compliance, especially as AI systems become more widely adopted.
Explore best practices for data annotation, model alignment, safety evaluation, and transparency to ensure your applications meet ethical and regulatory standards.
