We used our distillation platform & a Kaggle dataset to produce a tiny (270M Gemma base) model to classify text into "AI slop"/not classes.

It's fun to play with and was fun to build, too.

Annoyingly formal, human-written text (e.g. an ML paper I wrote back in 2015) tends to get misclassified (try "Manipulated images lose believability if the user's edits fail to account for shadows. We propose a method that makes removal and editing of soft shadows easy. Soft shadows are ubiquitous, but remain notoriously difficult to extract and manipulate. We posit that soft shadows can be segmented, and therefore edited, by learning a mapping function for image patches that generates shadow mattes. We validate this premise by removing soft shadows from photographs with only a small amount of user input").