Jeff: Jev-Compatible 0.8B Decision Models with Home Training and 30ms Inference

Jeff is a Jev-compatible decision model framework with 0.8B parameter size, trained locally on consumer hardware, achieving 30ms inference times for zero-shot classification tasks. It enables fast, calibrated probability outputs for arbitrary classification scenarios without cloud infrastructure dependencies.

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