Beat GPT 4 and Build the Best AI For You: Mobilizing First-Party Data with Fine-Tuning

Location: Central Room (Updated)

In late 2022, there was an alluring promise that large models can do everything - just prompt a sufficiently large model to solve your AI problem. But after two years of GenAI experimentation, it's clear that even the largest models still fall short for many use cases on quality, speed, cost, or reliability. Enter fine-tuning, small language models, automatic distillation, and continuous feedback loops that propagate behavior into the models. In this talk, we'll give a concrete framework for thinking about fine-tuning and when it's necessary. Then, we will show how combining modern open-source fine-tuning libraries has made fine-tuning more accessible than ever before and the platform that we can create to enable it.