MODEL PROFILE

JevK5

alibiserikbay · JevK5

JevK5 is an independent, Apache-2.0 open-source alternative to TypeSafe's Jev for typed decisions. It reads a state and a yes/no (`noul`), choice, or score question and returns a **probability for every option in one forward pass, with zero generated tokens**. The open weights can be self-hosted using the JevK5 runtime, which serves a TypeSafe-style `/v1/systemone` endpoint. This is not Jev's model or architecture and is not affiliated with TypeSafe AI. Use the JevK5 runtime shown below to read option probabilities. Generic text-generation examples on the Hub call `generate()` and do not perform JevK5's decision readout. - **Base:** Qwen3.5-4B, with a LoRA (rank 16, attention projections) merged into the weights - **Readout:** SemIf's protocol (TheoLeeCJ/SemIf, MIT): a softmax over the answer letters' next-token logits, divided by one calibration temperature. Questions with more than 16 options are read in several passes and combined with a second temperature. Both are in `jevk5_config.json`: `temperature` 1.22 and `knockout_temperature` 0.93 - **Runtime:** github.com/allebee/jevk5, 0.3.0 or later. One CUDA graph per padded input length: ~13 ms per decision on an H100 - **License:** Apache-2.0 - **Previous version:** v0.2 stays available under the Hub tag `v0.2` (see Use)

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/29 18:28:32
OVERVIEW

Model overview

JevK5 is an independent, Apache-2.0 open-source alternative to TypeSafe's Jev for typed decisions. It reads a state and a yes/no (`noul`), choice, or score question and returns a **probability for every option in one forward pass, with zero generated tokens**. The open weights can be self-hosted using the JevK5 runtime, which serves a TypeSafe-style `/v1/systemone` endpoint. This is not Jev's model or architecture and is not affiliated with TypeSafe AI. Use the JevK5 runtime shown below to read option probabilities. Generic text-generation examples on the Hub call `generate()` and do not perform JevK5's decision readout. - **Base:** Qwen3.5-4B, with a LoRA (rank 16, attention projections) merged into the weights - **Readout:** SemIf's protocol (TheoLeeCJ/SemIf, MIT): a softmax over the answer letters' next-token logits, divided by one calibration temperature. Questions with more than 16 options are read in several passes and combined with a second temperature. Both are in `jevk5_config.json`: `temperature` 1.22 and `knockout_temperature` 0.93 - **Runtime:** github.com/allebee/jevk5, 0.3.0 or later. One CUDA graph per padded input length: ~13 ms per decision on an H100 - **License:** Apache-2.0 - **Previous version:** v0.2 stays available under the Hub tag `v0.2` (see Use)

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。