How to train your own Jev for $17

Together AI describes how to build and deploy a Jev-like classification model by fine-tuning Qwen3.5 4B on roughly 38,000 normalized examples drawn from datasets covering natural-language inference, yes/no questions, banking intents, news topics, sentiment, policy decisions, routing, and research-paper taxonomy. The process involves cloning the Tev1 repository, configuring a Together API key, downloading and preparing source datasets, launching a fine-tuning job through Together AI’s service, and deploying the resulting model to a dedicated endpoint, with training estimated to cost about $17 and take roughly 25 minutes. The deployed model accepts structured JSON containing a state, question, and labeled answer options, then returns a selected option in a constrained format, illustrated by correctly identifying a duplicate subscription charge as a support intent. The post also provides inference settings and a system prompt intended to ensure deterministic, instruction-resistant classification behavior, and notes that endpoints can be stopped when no longer needed or replaced by Together’s hosted experimental Tev1 model.

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