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jev-vision-27b
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jev-vision-27b is an open-weights decision model from e12ex2. Its published size is 27.78B parameters. Licence: apache-2.0.
Catalogue identity notes
Exact Decision Index tested engine jev-vision-27b-r2; reported model jev-vision-27b. Type: LoRA; technique: autoregressive. Comparison entry; no current run command, API price or speed inferred from the benchmark. Current repository metadata checked separately; an unpinned alias is not evidence that today’s weights reproduce this historical run.
Model information sources
· Weights licence ↗ · Parameter count ↗
Publisher parameter count; includes the served backbone, not just adapter size.
$ how it compares
The decision models ranked either side of jev-vision-27b, out of 357 with a score.
| # | model | score | where |
|---|---|---|---|
| 69 | 64±4 | model files | |
| 70 | KnowLine 35B-A3B Gen1 | 64±4 | model files |
| 71 | jev-vision-27b | 64±4 | model files |
| 72 | 63±1 | model files | |
| 73 | Senova-4B v2 | 63±1 | model files |
see all decision models · find the ones that fit your machine
$ what it's good at
vision▁·▃▂▃▃▇▄▄█▄▃ #29 of 79 · 95% of its score · 12 tests
Beats StartLux-Decision v1.1 9B, RSI-Jev v6.1 VL 27B (effort auto), StartLux-Decision v1.1 4B here, though they rank higher overall. Trails Clef-flash, Perplexity Decider Flash v1.2 (4B), Mercury Decide (free endpoint) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Decision Index Vision 0.4 · Mind2Web (chance-corrected points) submitted | 84.1000 points ✎ | ▁·▁▁▃▁▄▄▃▅█▂ beats 49/61 | 12% |
| Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted | 38.0100 points ✎ | ▁▁▅▁▄▁▄█▄▃▇▇ beats 39/61 | 11% |
| Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted | 83.5000 points ✎ | ▁·▁▁▁▁·▂▁▃▅█ beats 41/61 | 10% |
| Decision Index Vision 0.4: private same skills (points) | 64.3200 points ✎ | ▁▁▃▃▁▂▄▆▃▃▅█ beats 45/61 | 10% |
| Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted | 52.5100 points ✎ | ▆▃▃▆▄▄▇██▁▆▆ beats 53/61 | 8.8% |
| Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted | 72.8100 points ✎ | ▁·▂▂▁▃▃▅▅▃▂█ beats 51/61 | 8.0% |
| Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted | 78.7000 points ✎ | ▂▁▁▁▁▃▁▅▅▆██ beats 44/61 | 7.8% |
| Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted | 97.2700 points ✎ | ▁·▁▂▁▁▃▁▂▂▅█ beats 47/61 | 6.2% |
4 more tests
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Decision Index Vision 0.4 · R-Bench-M (chance-corrected points) submitted | 24.3800 points ✎ | ▂▄▇▆█▄▄▇▂··▁ beats 32/61 | 6.2% |
| Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted | 53.9300 points ✎ | ▁···▁▂▂▂█▄▂▃ beats 35/61 | 5.9% |
| Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted | 76.7700 points ✎ | ▁▁▁▁▂▁▁▁▄█▃▃ beats 46/61 | 4.7% |
| Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted | 91.1800 points ✎ | ▁··▁▁▁▁▁▂▄█▁ beats 29/61 | 3.4% |
$ where to run it
Download model files ↗
Published model files verified. Includes adapters or readout weights; follow the model documentation for the required base model. This is a verified repository snapshot; the benchmark does not establish an identical weight revision. A local run command and hardware requirements have not been verified for this entry. Source · checked 2026-10-31.
Model repository and documentation: huggingface.co/e12ex2 ↗
$ how the score was worked out · Decision Index Vision 0.4 95%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
| 12 | 95% | |
| prior · pull toward the middle | 5% | |
| weighted average | = 63.5 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json