FrontiersMind's decision model on a base it pre-trained from scratch. Each question reads the state and its own tokens once and a 256-dimension pointer head scores the options; Choice and Score take up to 255 entries, and nothing is generated.
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.
Decides
choice, score, noul
choice, score, noul, classify
Architecture
lumma-fev
open-jev
Fine-tuned from
frontiersmind/lumma-0.6b-base
microsoft/deberta-v3-large
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
64.0%
85.4%
Calibration error
—
0.022
Valid action rate
—
—
Median latency
45.8 ms
28 ms
p95 latency
—
—
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 lumma-fev and open-jev-deberta-v3-large?
lumma-fev is from FrontiersMind and open-jev-deberta-v3-large from Kotoba Labs. Both have open weights you can download and run. Both answer choice, score and noul questions. Only open-jev-deberta-v3-large answers classify. lumma-fev reads up to 8K tokens of state, against 512 tokens for open-jev-deberta-v3-large. open-jev-deberta-v3-large is the smaller model, at 434M parameters to 649M.
Which is more accurate, lumma-fev or open-jev-deberta-v3-large?
They report on different suites — lumma-fev 64.0% on typed-decisions (maker's table; split not stated), open-jev-deberta-v3-large 85.4% on Kotoba held-out test, seen question types (banking77, SST-5, BoolQ) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lumma-fev or open-jev-deberta-v3-large?
lumma-fev: Free (open weights). open-jev-deberta-v3-large: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, lumma-fev or open-jev-deberta-v3-large?
By their publishers’ figures, open-jev-deberta-v3-large answers in about 28 ms at the median and lumma-fev in about 45.8 ms — measured on different hardware, so treat it as a rough guide.
Can I run lumma-fev or open-jev-deberta-v3-large locally?
Yes, both: systemone pull frontiersmind/lumma-fev and systemone pull kotoba-labs/open-jev-deberta-v3-large download the weights.
Evaluation suite
typed-decisions (maker's table; split not stated)
Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)
