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.
A Thai-and-English System One model. Qwen3.5-0.8B continued-pretrained on about 5B Thai tokens, its language-model head replaced by a 256-way slot head (slot 255 abstains), trained on 2–3M decision examples and temperature-calibrated per question type.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
anyjev
openthai
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.5-0.8b-base
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
77.1%
74.3%
Calibration error
0.034
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Valid action rate
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Median latency
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40 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 anyjev and openthai-systemone?
anyjev is from Nokia Applied Research and openthai-systemone from iApp Technology. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. openthai-systemone is the smaller model, at 800M parameters to 8.0B.
Which is more accurate, anyjev or openthai-systemone?
They report on different suites — anyjev 77.1% on LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question, openthai-systemone 74.3% on Bespoke public subsets (13, macro average) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, anyjev or openthai-systemone?
anyjev: Free (open weights). openthai-systemone: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run anyjev or openthai-systemone locally?
Yes, both: systemone pull nokia-applied-research/anyjev and systemone pull iapp-technology/openthai-systemone download the weights.
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Evaluation suite
LocalLLaMA/typed-decisions (2,000 held-out decisions), L2 with 300 labels per question
