How to train your own Jev for $17

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

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. The amounts are both $19.99 on the same day. I have not changed my plan.

Which listed support intent best matches this customer's message?

A. The customer reports being charged more than once.
B. The customer wants to end or downgrade a subscription.
C. The customer reports a payment that failed or was declined.
D. None of the listed intents matches.
    

In this blog post we’ll cover how to fine-tune and deploy a classification model that can answer these types of questions. By the time we’re done, you’ll have your own model deployed with an API endpoint that’s easy to integrate into any piece of software.

Let’s get started by setting up your computer with everything needed to train the model.