The full directory

All Jev AI model resources

Entries for TypeSafe AI's flagship System One model — official docs and SDKs alongside community clients, tools, and write-ups.

Entries: 3,739
Categories: 11
Official: 102
Community: 3,637
Source sites: 292
Deep dives: 3,739

By format

3739

Docs169
Repos & sites2980
Videos73
Posts103
Articles344
Playgrounds70

Updated Oct 25, 2026

Browse the Jev AI model by category

11 ways into the ecosystem. Each category page leads with its own shortlist of starting points.

Browse the Jev AI model by use case or industry

The same directory, sliced by what you are building and who you are building it for.

Jev use cases13 ways to put Jev to work, from classification and routing to decision gating and evaluation.

Jev industries14 sectors building with Jev, from software and security to healthcare and robotics.

All 3739 Jev AI model resources

Search the full directory, or filter by category and tag.

Showing 24 of 3739 resources

AutoTrust JEV-35B GGUF

prithivMLmods' GGUF builds of autotrust/JEV-35B, the Qwen3.5-35B-A3B MoE decision model that returns calibrated choice, noul and score answers over text and images; files run from BF16 to Q3_K_L for llama.cpp.

Jev Models & Reimplementations#community#open-models#local

Community

JEV-9B Core AI W8A16

An independent Apple Core AI conversion of the JEV-9B text backbone with 8-bit weights and fp16 activations for on-device generation on Apple silicon; it excludes the System 1 decision head, calibration and vision.

Jev Models & Reimplementations#community#open-models#local

Community

Ara-Jev-Faisal GGUF

mradermacher's static GGUF quants of Omartificial-Intelligence-Space/ara-jev-Faisal, an Arabic intent, policy and routing classifier; Q4_K_S and Q4_K_M are marked fast and recommended, under a non-commercial CC-BY-NC-4.0 licence.

Jev Models & Reimplementations#community#open-models#local

Community

Bonsai-JEV

A calibrated image-capable 27B decision model that fits an 8 GB GPU: the JEV LoRA-and-head recipe trained on Prism ML's ternary Bonsai 2, returning typed probabilities with no generated text.

Jev Models & Reimplementations#community#open-models#local

Community

Decider-0.8B Q8 GGUF

A community Q8_0 GGUF of Mapika/decider-0.8b, the smallest System One typed-decision model: one forward pass returns calibrated Choice, Score and noul answers, reporting 0.405 on JevBench hard-public at 920 MB resident.

Jev Models & Reimplementations#community#open-models#local

Community

JEV-35B AWQ INT4 W4A8

A 4-bit AWQ plus GPTQ build of AutoTrust's JEV-35B from AI Outfitters: INT4 weights with FP8 and INT8 activations, calibrated on all 256 experts, and served by vLLM on a single 24 GB GPU.

Jev Models & Reimplementations#community#open-models#local

Community

JEV-27B-VL AWQ INT4 W4A8

A 4-bit W4A8 AWQ plus GPTQ build of AutoTrust's JEV-27B-VL from AI Outfitters that keeps the rank-32 LoRA decision head and a bf16 vision tower, served by vLLM on a single 24 GB GPU.

Jev Models & Reimplementations#community#open-models#local

Community

Kev-27B Artemis GGUF

TurinTech's independent GGUF conversion of the 27B Kev decision model for llama.cpp: the default IQ4_XS file is 13.91 GB and keeps 97.8% of the BF16 original's answers over 2,000 test decisions, with zero changed confident answers.

Jev Models & Reimplementations#community#open-models#local

Community

Jev-Style-0.8B-Decision-v3 MLX 8-bit

An 8-bit MLX conversion by SirSahOl of chaoliangUNSW/Jev-Style-0.8B-Decision-v3 built for Apple Silicon native GPU inference: about 990 MB of active VRAM, a 262,144-token context and an average of 8.25 bits per weight.

Jev Models & Reimplementations#community#open-models#local

Community

Kev-4B Artemis GGUF

TurinTech's independent GGUF conversion of Jared Palmer's Kev-4B decision model; the default Q4_K_M file is 2.54 GB and returns the same answer as the BF16 original on 93.0% of 2,000 test decisions with no confident-answer changes.

Jev Models & Reimplementations#community#open-models#quantized

Community

Kev-9B Artemis GGUF

TurinTech's independent GGUF conversion of Jared Palmer's Kev-9B decision model; the default IQ3_M file is 4.78 GB and matches the BF16 original on 92.2% of 2,000 test decisions with a minus 1.2-point accuracy change.

Jev Models & Reimplementations#community#open-models#quantized

Community

StartLux-Decision v1.1 9B Instant GGUF

StartLux's 9B Instant decision model as GGUF for llama.cpp: Q8_0 (9.53 GB) measures DecisionPivot 53.2 against 50.6 for Q4_K_M and 53.2 for BF16 on vLLM, returning one-forward-pass typed-question probabilities.

Jev Models & Reimplementations#community#open-models#quantized

Community

StartLux-Decision v1.1 2B Instant GGUF

GGUF quantizations of StartLux Labs' 2B Instant decision model for llama.cpp: Q8_0 (2.01 GB) and Q4_K_M (1.27 GB), plus a 668 MB F16 vision encoder, with Q8_0 scoring 23.7 DecisionPivot against 23.5 for BF16 on vLLM.

Jev Models & Reimplementations#community#open-models#local

Community

StartLux-Decision v1.1 0.8B Instant GGUF

GGUF quantizations of StartLux Labs' 0.8B Instant decision model for llama.cpp: Q8_0 (0.81 GB, DecisionPivot 9.7) and Q4_K_M (0.53 GB, 8.7), plus a 205 MB F16 vision encoder, against 9.5 for BF16 on vLLM.

Jev Models & Reimplementations#community#open-models#local

Community

edceleste-decider 0.8B GGUF

mradermacher's static GGUF quantizations of IqSantiagow's 0.8B edceleste decider for llama.cpp, from Q2_K (0.5 GB) to f16 (1.6 GB), with Q4_K_M and Q4_K_S marked fast and recommended; no imatrix quants are planned by this quanter.

Jev Models & Reimplementations#community#open-models#local

Community

Decider-31b

Mapika's NVFP4 fine-tune of Gemma-4-31B-it answers typed Choice, Noul and Score questions with calibrated probabilities and no generated text, self-reporting a 64.27 chance-corrected public Decision Index and 39 ms median latency on a B300.

Jev Models & Reimplementations#community#open-models#local

Community

Gemma 4 12B Unified System One GGUF

A Q8_0 GGUF of knowlet's Gemma-4-12B-Unified-System-One with a native System One decision runtime and an independently fitted GGUF temperature, shipped with an F16 mmproj for the model's native image and audio projections.

Jev Models & Reimplementations#community#open-models#quantized

Community

PPLX Decider v1 27B EXL3

An EXL3 4.00 bpw conversion of Perplexity AI's pplx-decider-v1-27b by ramgpt: a 15.35 GB artifact that keeps the custom readout.safetensors decision head, with card-reported RTX 4090 medians from 135 ms at 128 tokens to 2.777 s at 8,192.

Jev Models & Reimplementations#community#open-models#quantized

Community

StartLux-Decision 35B-A3B GGUF

mradermacher's static GGUF quants of startlux-models/StartLux-Decision-v1-35B-A3B, the typed-decision mixture-of-experts: thirteen files from Q2_K at 13.3 GB to Q8_0 at 37.9 GB, plus mmproj supplements for the multimodal base.

Jev Models & Reimplementations#community#open-models#quantized

Community

Strands-Decider 2B Hobson v19 ONNX

A browser-ready ONNX conversion of Strands Labs' strands-decider-2B-hobson-v19 by onnx-community: q8 (1.80 GB) and q4f16 (1.09 GB) builds that return calibrated option probabilities, scoring a card-reported 59.4% on typed-decisions via WebGPU.

Jev Models & Reimplementations#community#open-models#quantized

Community

StartLux-Decision v1 27B Q8_0 GGUF

The Q8_0 8-bit GGUF build of StartLux-Decision-v1-27B, 28.60 GB for llama.cpp, served through a decision wrapper that the card measures at 100% same decisions as the original and 209 of 231 on JevBench public.

Jev Models & Reimplementations#community#open-models#quantized

Community

StartLux-Decision v1 9B Q8_0 GGUF

The Q8_0 8-bit GGUF build of StartLux-Decision-v1-9B, 9.53 GB for llama.cpp, with the card reporting 100.0% same decisions as the original weights and 200 of 231 correct (86.6%) on JevBench public.

Jev Models & Reimplementations#community#open-models#quantized

Community

GLiNER2.5-Decide Core ML

Fluid Inference's fixed-shape Core ML conversion of the classification path of Fastino's GLiNER2.5-Decide for Apple silicon: the DeBERTa-v3-large encoder and shared label classifier score up to four decision heads per call, in fp16, 8-bit or 6-bit packages.

Jev Models & Reimplementations#community#open-models#local

Community

open-bonsai-jev

An independent reimplementation of the OpenJev mechanism on PrismML's 1.75-bit Bonsai weights: typed decisions read straight off the next-token distribution of a 5.95 GB 27B model in one forward pass.

Jev Repos & SDKs#community#catalog-import#cobanov

Community

nicolailassen/open-bonsai-jevPython

Start here with the Jev AI model

Hand-picked entries the maintainers recommend first, from official primitives to community tools.

Jev: For the Builders AND Dreamers (Jev 1.14)

The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features — composable images on both models, the box question type on 1.14 only, and opt-in 262k context.

Jev Guides & ArticlesArticle#official#release#vision

Official

Box Questions (Dreamer-Ready)

The box question type: one bounding box per question, returned as four edges that are fractions of the image, plus confidence, with no criteria field and no not-found value.

Jev Practices & PatternsDocs#official#docs#vision

Official

Jev 1.14 Day 0 Support in LiteLLM

LiteLLM's Day 0 post for TypeSafe Jev 1.14: the proxy adds box questions, image input and long-context mode on both POST /v1/systemone and POST /v1/decisions, forwarding TypeSafe-Danger-Zone headers only to TypeSafe deployments.

Jev Integrations & AdaptersArticle#community#litellm#gateway

Community

Newest Jev AI model resources

The latest additions to the directory, newest first.

From the Jev community

Watch walkthroughs, read the threads, and see what builders are posting.

Watch6Discussions6Posts9

YouTube: KodeKloud's three-minute primer: what a System One model decides, and why it answers in milliseconds.

YouTube: Caleb Writes Code explains what Jev is and how to call it, in seven minutes.

YouTube: Greg Isenberg and Ryan Vogel on the businesses Jev unlocks, and a path in through the Vercel AI Gateway.

YouTube: CJ from Syntax demos browser use, classification, code review, a model router, and a chat bot with no LLM.

YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.

YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.

Hacker News: Show HN: Ad Spend Guard gates budget before the auction, not the creative after.

Show HN: Gating ad budget with a decision modelRead the thread on Hacker News

Reddit: Reranking rebooking options with a decision model, ranked with reasons.

Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.

Show HN: Verifying robot picks with a decision modelRead the thread on Hacker News

Hacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.

jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker News

Hacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.

ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker News

Hacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.

vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker News

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How entries are chosen

Every entry is reviewed against the same bar, whether it comes from TypeSafe or the community.

Public and working — no paywalls, sign-up walls, or dead links.
Concrete — it has to be about Jev and the System One API, not generic LLM content.
Useful today — something a developer can learn from or use right now.
Distinct — if a similar entry exists, the description has to say what makes this one different: a different runtime, a cheaper path, a measured result, or a narrower focus.
Documented — repos state their license, and docs are in English or carry an English summary.

Spam, affiliate links, and thin AI-generated content are rejected. New entries are expected to ship with a deep dive: two to five highlights, caveats, a quickstart snippet where one helps, and a verification date. Star counts are refreshed when an entry is touched.

Common questions

Short answers about Jev and how this directory works.

What is the Jev AI model?

Jev is TypeSafe AI's first System One model — an AI model that takes a state plus typed questions and returns one structured answer per question: a choice, an ordered score, or the probability of yes, with calibrated confidence on Choice and Score answers. Code can branch on the result without parsing generated text.

What is the difference between Choice, Score, and Noul?

They are the three question primitives. Choice picks one of up to 255 options, Score rates on 2 to 10 ordered levels, and Noul answers yes or no. Choice and Score answers carry a confidence between 0 and 1; a Noul answer is the probability of yes.

Which Jev SDKs and client libraries are available?

TypeSafe maintains official JavaScript/TypeScript and Python SDKs plus a Python adapter. The community has built clients for Go, .NET, PHP, Laravel, Rust, Elixir, Ruby, and Scala, along with framework integrations such as DSPy, LlamaIndex, and Home Assistant. All of them are listed in the Repos & SDKs category.

Is the directory free, and is it affiliated with TypeSafe AI?

The directory is free to browse: every entry is reviewed, and spam, affiliate links, and sign-up walls are rejected. JevDirectory.org is community-run and not affiliated with TypeSafe AI — Jev and System One are TypeSafe AI trademarks.

How often is the directory updated?

Entries are added as new resources appear and re-checked when they are touched. The directory currently holds 3739 entries from 292 source sites, last updated Oct 25, 2026.

What is a verified deep dive?

Most entries include a deep dive: two to five highlight bullets someone would only know from using the resource, a caveats note about limits and requirements, an optional quickstart snippet, and the date the link was last checked. 3739 of the 3739 entries carry one today.

For the canonical details, read the official TypeSafe docs and the API reference.