Introducing Multimodal Llama 3.2

DeepLearning.AI via Coursera

Coursera based on 11 ratings

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Overview

Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, to learn all about the latest additions to the Llama models 3.1 and 3.2, from custom tool calling to multimodality and the new Llama stack. Open models are a key building block of AI and a key enabler of AI research. With Metas family of open models, anyone can download, customize, fine-tune, or build new applications on top...

Syllabus

Introducing Multimodal Llama 3.2

Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, to learn all about the latest additions to the Llama models 3.1 and 3.2, from custom tool calling to multimodality and the new Llama stack. Open models are a key building block of AI and a key enabler of AI research. With Metas family of open models, anyone can download, customize, fine-tune, or build new applications on top of them, allowing AI innovation. The Llama model family now ranges from 1B model parameters to its 405B foundation model, allowing for diverse use cases and applications. In this course, youll learn about the new vision capabilities that Llama 3.2 brings to the Llama family. Youll learn how to leverage this along with tool-calling, and Llama Stack, which is an open-source orchestration layer for building on top of the Llama family of models. In detail, youll: 1. Learn about the new models, how they were trained, their features, and how they fit into the Llama family. 2. Understand how to do multimodal prompting with Llama and work on advanced image reasoning use cases such as understanding errors on a car dashboard, adding up the total of three restaurant receipts, grading written math homework, and many more. 3. Learn different rolessystem, user, assistant, ipythonin the Llama 3.1 and 3.2 family and the prompt format that identifies those roles. 4. Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and enables support for seven non-English languages. 5. Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations. 6. Learn about Llama Stack API, which is a standardized interface for canonical toolchain components like fine-tuning or synthetic data generation to customize Llama models and build agentic applications. Start building exciting applications on Llama!