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 MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.

Learn to evaluate programs utilizing LLMs as well as generative image models using platform-independent tools Learn to evaluate programs utilizing LLMs as well as generative image models using platform-independent toolsInstrument a training notebook, and add tracking, versioning, and logging Instrument a training notebook, and add tracking, versioning, and loggingImplement monitoring and tracing of LLMs over time in complex interactions Implement monitoring and tracing of LLMs over time in complex interactions_Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.This course will teach you to:Instrument a Jupyter notebookManage hyperparameter configLog run metricsCollect artifacts for dataset and model versioningLog experiment resultsTrace prompts and responses to LLMs over time in complex interactionsWhen you complete this course, you will have a systematic workflow at your disposal to boost your productivity and accelerate your journey toward breakthrough results._Anyone who has familiarity with Python and PyTorch or similar framework and an interest in managing, versioning, and debugging their machine learning workflow.

Evaluating and Debugging Generative AI

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. 

Prompt Engineering for Vision Models

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Understand development steps, from evaluation, through prompting, self-reflection, and fine-tuning, to improve your model’s reliability and accuracy. Understand development steps, from evaluation, through prompting, self-reflection, and fine-tuning, to improve your model’s reliability and accuracy.Learn how memory tuning can increase your model performance by embedding facts into your model to reduce hallucination. Learn how memory tuning can increase your model performance by embedding facts into your model to reduce hallucination.Use the Llama 3-8b model to build an LLM application that converts text to SQL with a custom schema. Use the Llama 3-8b model to build an LLM application that converts text to SQL with a custom schema._Join our new short course, Improving Accuracy of LLM Applications with Lamini and Meta. Learn from Sharon Zhou, Co-founder & CEO of Lamini, and Amit Sangani, Senior Director of Partner Engineering, Meta.Many developers have experienced frustration with inconsistent results when working with LLM applications. This course offers a systematic approach to enhance the accuracy and reliability of your LLM applications.You will build an SQL agent, add evaluation metrics to measure performance, and use prompt engineering and self-reflection to make the model perform better. Finally, you will fine-tune the model with techniques like LoRA and memory tuning that embeds facts in model weights to reduce hallucinations.In this course, you’ll use Llama’s family of open-source models.What you’ll do: Build a text to SQL agent and simulate situations where it hallucinates to begin the evaluation process.Build an evaluation framework to systematically measure performance, including criteria for good evaluations, best practices, and how to develop an evaluation score.Learn how instruction fine-tuning enhances pre-trained LLMs to follow instructions, and how memory fine-tuning embeds facts to reduce hallucinations. Break fine-tuning myths and see how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation(LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further. Go through an iterative process of generating training data and fine-tuning, learning practical tips such as adding examples, generating variations, and filtering generated data to increase model accuracy.Start improving the accuracy of LLM applications today! _This course is ideal for anyone with intermediate Python knowledge and familiarity with large language models (LLMs) looking to build more factual and precise LLM applications. 