Running open-Jev in SQL on Databricks

Over the weekend, “System One” decision models such as Jev have launched, which are a growing class of foundation models that are able to produce well-calibrated decisions from a discrete set of options. These models are usually extremely fast and also cheap, allowing a broad range of applications on large data. Potential use cases range from analyzing customer support transcripts, to applying complex decisions at scale across your data.

The open source community has also been busy launching open weight versions of these decision models, from Smelf-open-jev, to Laya, to Kev. But the power of these models are only unlocked when they can touch large amounts of data. Today we are excited to share how you can serve open weight decision models on Databricks, and run them directly on your governed data. You can even access these models directly from your SQL console or production jobs in Lakeflow via ai_query. Moreover, products like AI Runtime enable you to customize or post-train these models to tailor them to your specific enterprise context.

In this post, we’ll show how you can use such a model, SemIf-OpenJev, directly in Databricks, to classify hotel reviews as good or bad.

We’ve packaged the workflow into an importable Databricks Notebook.

The notebook uses Databricks AI Runtime to provide serverless, on-demand GPU compute without requiring you to set up or manage GPU infrastructure.