A training-free layer that turns an open LLM into a decision model. It reads typed choice, yes/no and score answers from one prefill, removes option-order bias with no labels and, from a few hundred labels, calibrates or fits a closed-form head. Weights stay untouched.
An open System One model from Interfaze AI. A rank-32 LoRA on Qwen3.5-4B reads yes/no, choice and score answers from the logits of one forward pass, returns calibrated probabilities and serves the /v1/systemone API.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
anyjev
lev
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.5-4b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
77.1%
68.9%
Calibration error
0.034
0.115
Valid action rate
—
—
Median latency
—
69 ms
p95 latency
—
—
Evaluation suite
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 anyjev and lev?
anyjev is from Nokia Applied Research and lev from Interfaze AI. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. lev is the smaller model, at 4.0B parameters to 8.0B.
Which is more accurate, anyjev or lev?
They report on different suites — anyjev 77.1% on LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question, lev 68.9% on S1Bench (13 public subsets, macro) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, anyjev or lev?
anyjev: Free (open weights). lev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run anyjev or lev locally?
Yes, both: systemone pull nokia-applied-research/anyjev and systemone pull interfaze-ai/lev download the weights.
LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question
