DeepLearning.AI

Amit Sangani

Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, about the latest additions to the Llama models 3.1 and 3.2. Discover the new vision capabilities, tool-calling, and Llama Stack, an open-source orchestration layer for building on top of the Llama family of models. Start building exciting applications on Llama!

Introducing Multimodal Llama 3.2

Learn about the new models, how they were trained, their features, and how they fit into the Llama family.

Introduction to Llama 3.2

Understand how to do multimodal prompting with Llama and work on advanced image reasoning use cases.

Multimodal Prompting

Learn different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 family and the prompt format that identifies those roles.

Roles and Prompt Format

Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size.

Tiktoken Tokenizer

Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations.

Tool Calling

Learn about ‘Llama Stack API’, a standardized interface for canonical toolchain components to customize Llama models.

Llama Stack API

Explore the features of the new Llama 3.2 model, including image classification, vision reasoning, and tool use. Learn about Llama 3.2 prompting, tokenization, and the Llama stack to build AI applications.

Anyone interested in exploring the features of the new Llama 3.2 model.

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Introducing Multimodal Llama 3.2

Overview

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