DeepLearning.AI

Amit Sangani

In this course, you will explore the Llama 4 models, learning how to utilize their Mixture-of-Experts architecture for efficient application development. You'll gain practical experience with image reasoning, image grounding, and long-context text processing. The course also covers prompt optimization and synthetic data creation, equipping you with the skills to build production-ready features that span text, images, and massive context. By the end, you'll be able to confidently choose and implement the right Llama 4 model for your projects.

Building with Llama 4

An overview of the course and its objectives.

Introduction

Learn about the Llama 4 models and their Mixture-of-Experts architecture.

Overview of Llama 4

Get started with Llama 4 using the official API and explore its capabilities.

Quickstart with Llama 4 and API

Understand image grounding and how to identify elements within specific image regions.

Image Grounding

Learn about the special token and raw prompt format for text-only and multimodal prompts.

Llama 4 Prompt Format

Work with long contexts of up to 10 million tokens and explore large text files.

Long-Context Understanding

Automatically improve system prompts in sentiment analysis and categorization use cases.

Prompt Optimization Tool

Create, curate, and save high-quality datasets for training and fine-tuning.

Synthetic Data Kit

Summarize the course learnings and next steps.

Conclusion

Quiz

Additional resources and code examples for further learning.

Appendix - Tips, Help, and Download

This course offers a comprehensive introduction to the Llama 4 family of models, focusing on their Mixture-of-Experts architecture and application development using the official API. Gain hands-on experience with image reasoning, prompt optimization, and synthetic data creation, empowering you to build advanced AI applications.

Individuals looking to gain hands-on experience with the Llama 4 family of models.

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Building with Llama 4

Overview

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