An independent Jev-shaped reproduction on DeBERTa-v3-large. One state and any number of choice, score and yes/no questions go in, and a calibrated distribution per question comes out of one forward pass. Public gold labels only.
A local, Jev-compatible decision model from Rizzo AI Academy. XHToken's Spark-X2.5-4B with a merged typed-decisions LoRA, run on llama.cpp; yes/no, choice and score questions share one prefill of the state and are read from the answer-letter logits. No text is generated.
Decides
choice, score, noul, classify
choice, score, noul, classify, route
Architecture
open-jev
rizzo-flow
Fine-tuned from
microsoft/deberta-v3-large
xhtoken/spark-x2.5-4b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
85.4%
64.8%
Calibration error
0.022
0.112
Valid action rate
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Median latency
28 ms
195 ms
p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between open-jev-deberta-v3-large and rizzo-flow?
open-jev-deberta-v3-large is from Kotoba Labs and rizzo-flow from Rizzo AI Academy. Both have open weights you can download and run. Both answer choice, score, noul and classify questions. Only rizzo-flow answers route. open-jev-deberta-v3-large is the smaller model, at 434M parameters to 4.0B.
Which is more accurate, open-jev-deberta-v3-large or rizzo-flow?
They report on different suites — open-jev-deberta-v3-large 85.4% on Kotoba held-out test, seen question types (banking77, SST-5, BoolQ), rizzo-flow 64.8% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), Q8_0 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, open-jev-deberta-v3-large or rizzo-flow?
open-jev-deberta-v3-large: Free (open weights). rizzo-flow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, open-jev-deberta-v3-large or rizzo-flow?
By their publishers’ figures, open-jev-deberta-v3-large answers in about 28 ms at the median and rizzo-flow in about 195 ms — measured on different hardware, so treat it as a rough guide.
Can I run open-jev-deberta-v3-large or rizzo-flow locally?
Yes, both: systemone pull kotoba-labs/open-jev-deberta-v3-large and systemone pull rizzo-ai-academy/rizzo-flow download the weights.
Evaluation suite
Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), Q8_0
