This talk explores the use of logits - the raw confidence scores that language models generate before selecting each token. Working directly with logits enables finer control over model behavior.

The session covers practical techniques for accessing and utilizing these scores through local models. Topics include detecting model uncertainty, implementing custom stopping conditions, and steering generation without prompt modifications.

You will learn how to analyze model confidence patterns and apply this knowledge to real-life use cases.