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

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 small language models (SLMs) - nimble, purpose-built models fine-tuned on first-party data to excel at specific use cases. 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 and clever infrastructure abstractions has made fine-tuning more accessible than ever before.