Kev-0.8B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is the smallest Kev: a LoRA adapter and a pointer head on Qwen3.5-0.8B-Base that runs on a laptop or a 4 GB GPU. It is intended where memory or cost rules out the larger sizes and the task resembles what it was trained on; out of domain it is markedly less accurate than Kev-4B. This card describes the checkpoint in Kev 1.0, first published on 2026-09-24. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.

Text analysis

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kev-27b

jaredpalmer

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Text analysis

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kev-4b

jaredpalmer

Kev-4B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It is intended for developers who classify, route, triage or check documents and who need probabilities that can be thresholded, for example to send uncertain cases to human review. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is a LoRA adapter and a pointer head on Qwen3.5-4B-Base, small enough for one 24 GB GPU or a 32 GB Apple Silicon Mac. This card describes the checkpoint in Kev 1.0, first published on 2026-09-24. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.

Text analysis

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kev-9b

jaredpalmer

Kev-9B is a decision model. It reads one document (the *state*) and a set of typed questions about it, and returns a calibrated probability distribution over the options supplied with each question, in a single forward pass and without generating text. It is intended for developers who classify, route, triage or check documents and who need probabilities that can be thresholded, for example to send uncertain cases to human review. It implements TypeSafe's public System One API (`POST /v1/systemone`), so the TypeSafe SDK works against it unchanged. It is a LoRA adapter and a pointer head on Qwen3.5-9B-Base and fits one 24 GB-class GPU. This card describes version 2, released on 2026-09-30 and included in Kev 1.0. **Input.** A state (text, or a JSON object or array rendered as labelled text) and any number of named questions, each of one of three types: Each question is answered as its own row that continues from the shared state, so questions cannot influence one another; the state is computed once and cached.

Text analysis

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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