Speaker
Description
The world is buzzing about the capabilities of large-scale AI models. While proprietary, API-gated systems dominate the headlines, a parallel revolution is empowering developers and enthusiasts worldwide: the open-source AI movement. This is where the true potential for innovation, learning, and building lies for the next generation of engineers. This talk is designed to be your entry point into this exciting world.
In this fast-paced, 20-minute session, we will cut through the noise and provide a practical roadmap for students to start building powerful applications with state-of-the-art open-source models today. We will explore:
The Landscape: A quick tour of the most impactful open-source model families like Meta's Llama, Mistral AI's models, and text-to-image generators like Stable Diffusion. We'll discuss what makes them different and where to find them.
Running Your First Model: We'll demystify the process of running a powerful Large Language Model locally on a consumer-grade laptop, using tools like Hugging Face Transformers and simplified formats like GGUF.
Tuning and Adaptation: Discover how you can make these models your own. We’ll cover the concepts of fine-tuning (training a model on your own data for a specific task) and Retrieval-Augmented Generation (RAG) to build applications like a chatbot that can answer questions about your university coursework.
From Idea to Application: We'll showcase a few compelling, achievable project ideas, such as building a personal code assistant, an intelligent document summarizer, or a unique art generator, to inspire you to start your own AI development journey.
This talk is for anyone who wants to move beyond simply using AI tools and start building with them. You will leave with a clear understanding of the open-source ecosystem, the practical first steps to take, and the inspiration to create your own innovative AI applications.
Prerequisites:
This session is designed for an audience with programming experience (preferably Python) and a basic conceptual understanding of machine learning.
Session author's bio
Lakshay Bandlish is a Senior Software Engineer at Google's Search Platforms team, where he focuses on AI model adaptation for Search Verticals. Since graduating from IIT Kanpur's Computer Science department in 2020, he started his career as a Product Engineer at Sprinklr before joining Google Ads, and later transitioning to his current role in Search. A strong advocate for open-source, Lakshay contributed to The Linux Foundation through two Google Summers of Code stints. Outside of work, he enjoys making shortfilms.
| Level of Difficulty | Intermediate |
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| In Person Attendance | In-person |