This session aims to provide hands-on, engaging content that gives developers and researchers a basic understanding of the Llama 3 models, how to access and use them, and build an agentic app using Llama Stack. The audience will also learn core concepts around Prompt Engineering and Fine-Tuning and programmatically implement them using Responsible AI principles. Lastly, we will conclude the talk explaining how they can leverage this powerful tech, different use-cases and what the future looks like.

Understanding Llama 3 and its usage Familiarize yourself with Llama 2 models, how to download, install and access them, and basic use-cases it can accomplish. Additionally, we will also review basic completion, system prompts and responses in different formats.

Generative AI Application Architecture We will walk through the basic Gen AI & Chatbot architecture including implementing chat requests, responses, prompt engineering concepts to get the best out of Llama, Hallucinations and how to prevent them, augmenting external data using Retrieval Augment Generation (RAG). We will also review advanced concepts around Fine-Tuning.

Llama Stack Llama Stack is an attempt to provide standard interfaces (APIs) to streamline innovation in the highly fractured OSS ecosystem by providing, for the first time, a credible alternative developer experience to the closed source models available via simple APIs. We will explore Llama stack to build an agentic application using our Llama model.

Background Knowledge: Attendees will have a deeper understanding of Llama 3 and Llama Stack. They will be able to access Llama 3 and use it in their day-to-day Generative AI projects and applications. Basic knowledge of LLMs and Python

Technology: Large large models (open source)

Live Action: We will provide hands-on, engaging content that gives developers and researchers a basic understanding of our Llama 3 models, and leverage Llama Stack to build an agentic app.