Jev-27B-VL: Apache-2.0 Vision-Language Model With Calibrated Decision Outputs

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Autotrust has released JEV-27B-VL, an Apache-2.0 vision-language model with 27 billion parameters built on autotrust/JEV-27B by adding image input. It offers two modes, with System 1 returning typed decisions and calibrated probabilities over supplied options in a single forward pass. Reported evaluations cover cold-start video cover ranking, web agent task completion, and multimodal answer comparison, with results varying by benchmark. The supplied materials do not specify the vision encoder, training data, or hardware requirements. The model's developers and the source describe these results as offline evaluations rather than proof of production performance.

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