An open family of System One models. A LoRA adapter plus a pointer head on a frozen Qwen base returns a distribution per typed question in one forward pass, serves TypeSafe's /v1/systemone contract, and ships a fitted temperature with every checkpoint.
Laya fine-tune that scans untrusted text (extracted files, knowledge-base documents, agent skills, tool descriptions) for prompt injection and data exfiltration in English and German, as a noul question answered in one forward pass. 322M parameters, PyTorch and ONNX.
Decides
choice, score, noul, classify, route
noul, classify
Architecture
kev
laya
Fine-tuned from
qwen/qwen3.5-4b-base
convaiinnovations/laya-multilingual
License
apache-2.0
Unspecified; training data includes CC BY-NC-SA 4.0 material (non-commercial), not relicensed
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
83.8%
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Calibration error
0.042
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Valid action rate
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Median latency
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95.4 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 kev and laya-cybersec?
kev is from Jared Palmer and laya-cybersec from TextCortex. Both have open weights you can download and run. Both answer noul and classify questions. Only kev answers choice, score and route. kev reads up to 8K tokens of state, against 1K tokens for laya-cybersec. laya-cybersec is the smaller model, at 322M parameters to 4.0B. kev is licensed apache-2.0; laya-cybersec, other.
Which is more accurate, kev or laya-cybersec?
Only kev publishes an accuracy figure (83.8% on transfer-v4 (locked, out of domain)); laya-cybersec does not, so there is no comparison to make without your own test.
Which is cheaper, kev or laya-cybersec?
kev: Free (open weights). laya-cybersec: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run kev or laya-cybersec locally?
Yes, both: systemone pull jared-palmer/kev and systemone pull textcortex/laya-cybersec download the weights.
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149 ms
Evaluation suite
transfer-v4 (locked, out of domain)
TextCortex saved run: 279 single-window inputs, PyTorch batch one
