Working with OSS LLMs (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 2)

This is a 6 video series interactive guided tour to LLMs, RAG, & Fine-Tuning. The next part is here: https://youtu.be/L7T8Ycog0-U The playlist is here: https://youtube.com/playlist?list=PLUsOvkBBnJBcZglk6QQyKGZsgEzClGnv-&si=66stnfv3-HXa60m9 You can also watch the full workshop here: https://youtu.be/uDBGwQ7JAzQ In this workshop, attendees will learn about methods for working with LLMs. Our stories will be guided by examples you can run on your laptop or in a (free) hosted cloud environment provided to attendees. Developers will expand their awareness of how researchers and product designers are working with LLMs, with emphasis on connecting high-level concepts such as fine-tuning and vector databases to the fundamental math and APIs data scientists should understand. Business-minded executives can either get hands-on or follow the higher-level stories to deepen their sense of what is possible with LLMs, the technicalities behind risks they introduce, and how they fit into the arc of ML. The primary value of this workshop will be as a guide to help teams set reasonable goals in the complex and fast-moving world of LLMs, and understand what you need to successfully support your team’s next LLM projects. What You’ll Learn: There are cheap (e.g., APIs) and expensive (e.g., fine-tuning, training) ways to build on top of LLMs. The methods you choose have consequences in apps you can build and how your dev team works. We will learn how to think about these choices as we develop basic apps you can use as templates for future genAI projects. Learners have the option to follow along in a provided dev environment where we will unpack these choices and make the tradeoffs and decision space concrete. The Github repository is here: https://github.com/outerbounds/generative-ai-summit-austin-2023