Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use. Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use.Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling. Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling.Gain knowledge of the Llama stack, which is a standardized interface for building AI applications. Gain knowledge of the Llama stack, which is a standardized interface for building AI applications._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 Meta’s 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, you’ll learn about the new vision capabilities that Llama 3.2 brings to the Llama family. You’ll 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, you’ll: Learn about the new models, how they were trained, their features, and how they fit into the Llama family.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.Learn different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 family and the prompt format that identifies those roles.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.Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations.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!_Anyone who has basic Python knowledge and wants to learn to quickly build on Llama and Llama stack.

Introducing Multimodal Llama 3.2

Learn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting. 

Prompt vision models with text, coordinates, and bounding boxes, and tune hyper-parameters like guidance scale, strength, and number of inference steps. Prompt vision models with text, coordinates, and bounding boxes, and tune hyper-parameters like guidance scale, strength, and number of inference steps.Replace parts of an image with generated content with in-painting, a technique that combines object detection, image segmentation, and image generation. Replace parts of an image with generated content with in-painting, a technique that combines object detection, image segmentation, and image generation.Fine-tune a diffusion model to have even more control over your image generation to create specific images, including your own, rather than generically generated images. Fine-tune a diffusion model to have even more control over your image generation to create specific images, including your own, rather than generically generated images._Prompt engineering is used not only in text models but also in vision models. Depending on the vision model, they may use text prompts, but can also work with pixel coordinates, bounding boxes, or segmentation masks.In this course, you’ll learn to prompt different vision models like Meta’s Segment Anything Model (SAM), a universal image segmentation model, OWL-ViT, a zero-shot object detection model, and Stable Diffusion 2.0, a widely used diffusion model. You’ll also use a fine-tuning technique called DreamBooth to tune a diffusion model to associate a text label with an object of your preference.In detail, you’ll explore:Image Generation: Prompt with text and by adjusting hyperparameters like strength, guidance scale, and number of inference steps. Image Segmentation: Prompt with positive or negative coordinates, and with bounding box coordinates.Object detection: Prompt with natural language to produce a bounding box to isolate specific objects within images.In-painting: Combine the above techniques to replace objects within an image with generated content.Personalization with Fine-tuning: Generate custom images based on pictures of people or places that you provide, using a fine-tuning technique called DreamBooth.Iterating and Experiment Tracking: Prompting and hyperparameter tuning are iterative processes, and therefore experiment tracking can help to identify the most effective combinations. This course will use Comet, a library to track experiments and optimize visual prompt engineering workflows._Prompt Engineering for Vision Models is a hands-on course that helps you get started with prompting vision models. Python experience is recommended. 