40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production...

Summary We just launched our own Jev-like classifier, together/Tev1-4B-experimental , on top of Qwen3.5 4B on Together’s serverless platform.

In this blog post we’ll show you how to fine-tune your own version!

Jev has quickly become one of the most talked about model releases in the AI space.

It’s a powerful classification model that’s both fast and incredibly cheap to run.

Give Jev a piece of state plus predefined questions and it will quickly give back a result in the form of a score, boolean value, or multiple choice answer.

This sort of classification model has many real-world applications, such as an e-commerce site evaluating automated customer returns, categorizing ML papers, or even providing a sentiment rating for a piece of text.

Today we’re going to fine-tune our own Jev-like classification model that takes state and returns an answer.

Our goal is to create a model that can quickly and efficiently answer questions like: Customer message: Hi, I checked my statement and your company charged my card twice for the October subscription.

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