**Typed decisions about text, web pages and screenshots. One forward pass per question with up to 52 options. About 210 ms for an isolated web decision on one H100, with roughly 1,460 prompt tokens and 23 options.** OpenJev is an open-weights **decision model**. You describe the decision in plain words at request time, with your own labels, and it answers with a choice, a yes / no probability or a score. The same model routes a support ticket, flags a policy violation, judges whether an answer is grounded, and tells a browser agent which button to press next. Three questions in one API request, three typed answers, each with a probability for every option. One forward pass per question (up to 52 options).
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View details ↗The FP8 file of OpenJev: the same weights stored as FP8 (e4m3, 128×128 weight blocks, dynamic activations), about 29 GB instead of 54 GB. Everything about the model, the API and the results is in the main card; this page only says how this file was checked and how to run it. A one-pass readout lives on small score differences, so the file was re-run on the same frozen tests as the 16-bit model, with the same helper and the same fixed calibration: About 2% of individual answers change between the two files (219 of 10,000 text answers, 40 of 2,000 screenshot answers), in both directions. The rule fixed before the run was "no more than 0.5 points below the served model"; both tests pass it.
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View details ↗The GGUF files of OpenJev, the open 27B one-pass decision model for browser and desktop agents, quantized with llama.cpp for 24 GB cards, Macs and CPUs. Text only (no vision tower). Built from the merged 16-bit weights (revision 5ec9e5fd) with llama.cpp build b11147; `SHA256SUMS` and `MANIFEST.json` carry every hash and the validation numbers. Ask for a decision the way OpenJev was trained: state, question, lettered options, and read the letter.
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