Logits are the raw numerical scores that language models compute for each token in their vocabulary before making a selection. These scores are typically converted to probabilities and used internally for token selection. Accessing and analyzing them directly opens up possibilities for controlling and understanding model behavior.

Logits provide insights into model uncertainty, help detect potential hallucinations, and enable fine-grained control over generation without modifying prompts.

In this session, attendees will learn how to access logits through local models, visualize confidence patterns, and implement practical techniques like uncertainty detection and generation steering. You can get practical insight which you can apply to your own projects.