Jev directory
Jev for Scoring
726 curated Jev resources. Rate content on ordered levels and combine judgments into composite scores.
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All 726 Jev AI model resources for Scoring
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SiliconANGLE reports TypeSafe closed $870M at a $7.5B valuation and explains the model's pitch: three request types, yes/no answers, picking from a list, or a score, return structured output and a confidence number instead of text.
Jev Media & TalksArticle#community#press#funding
Community
Forbes' Rashi Shrivastava profiles TypeSafe's $40 million DCVC-led seed at a $200 million valuation, and CEO Diogo Almeida's argument that software needs models reporting calibrated confidence rather than confident-sounding paragraphs.
Jev Media & TalksArticle#community#press#funding
Community
Braintrust's provider page covers two ways to use TypeSafe Jev: instrumenting System One calls as decision spans, and running Jev as an LLM judge that turns typed choices into scores or labels.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Langfuse's guide to decision-model evaluators runs OpenAI's gpt-6-luna or TypeSafe Jev over observations, where each Choice, Score or Yes/no question becomes one Langfuse score.
Jev Guides & ArticlesArticle#community#press#explainer
Community
The desk's index of every Jev measurement it has published, keeping each figure's original caveat: JevBench boards, Vercel gateway reach, Celeris and Artificial Analysis results, and the desk's own Jev Day probes which it says it did not rerun.
Jev Guides & ArticlesArticle#community#press#calibration
Community
A LoRA plus 24-slot decision head over Qwen3.8-27B from Ming Xu's train-jev project: it serves TypeSafe's System One API with 0.851 Kev transfer-v4 accuracy and ECE 0.053, and needs an 80 GB GPU.
Jev Models & Reimplementations#community#open-models#local
Community
A 24 GB-GPU alternative from the train-jev project: Qwen3.5-4B plus a rank-16 LoRA and 24-slot decision head scoring 0.647 PK accuracy, 40.4 Decision Index and 43 ms p50 latency, ahead of Kev-4B at the same size.
Jev Models & Reimplementations#community#open-models#local
Community
The original OSS Jev Qwen2.5-0.5B decision checkpoint, trained 2,000 steps at learning rate 2e-05; the 10k sibling card warns that this overwrite tip may be the worse noul-fix variant and recommends sized siblings.
Jev Models & Reimplementations#community#open-models#local
Community
A small distilled sibling of Perplexity's pplx-decider-v1.2: a Qwen3.5-4B decider that reads a state plus typed choice, noul and score questions in one pass, keeping most of the 27B model's Decision Index.
Jev Models & Reimplementations#community#open-models#multimodal
Community
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
A Laya fine-tune for coding decisions: roughly 9k rows from SWE-Gym teach keep/drop file relevance, priority scoring and injection detection, reporting 0.833 accuracy on 1,150 held-out rows at 126 ms p50.
Jev Models & Reimplementations#community#open-models#local
Community
Perplexity's 27B decision model rebuilt on Qwen3.8-27B with a joint request format that answers a whole request's questions in one sequence, reporting 68.09 on Decision Index 0.3 and 77.43 on vision.
Jev Models & Reimplementations#community#open-models#multimodal
Community
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
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
A Laya fine-tune from Convai Innovations on the 1,200-case typed-decisions benchmark: it reports 0.777 accuracy on the official 400-case test set, above TypeSafe Jev 1.13.0 at 0.727 and the teacher ceiling at 0.735.
Jev Models & Reimplementations#community#open-models#local
Community
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
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
A 9B bilingual Turkish and English Jev-class decision model from Berkay Hasret: a 173 MB LoRA adapter on Qwen/Qwen3.5-9B that answers up to 16 typed questions about one text in a single forward pass and reports 0.783 macro accuracy on full JevBench.
Jev Models & Reimplementations#community#open-models#local
Community
A bilingual Turkish and English 4B Jev-class decision model from Berkay Hasret: a roughly 130 MB LoRA adapter on Qwen/Qwen3.5-4B that answers up to 16 typed questions per text in one pass and reports 0.774 JevBench macro accuracy.
Jev Models & Reimplementations#community#open-models#local
Community
A specialist full fine-tune of mistralai/Ministral-3-3B-Reasoning-2512 by rony for the LocalLLaMA/typed-decisions benchmark: it reads a case, question and lettered options and softmaxes next-token logits, reporting 0.808 accuracy and 0.0773 KL on all/test.
Jev Models & Reimplementations#community#open-models#local
Community
Singularity Compute's fine-tune of Google's diffusiongemma-26B-A4B-it, a discrete-diffusion mixture-of-experts, served from an 18 GB NVFP4 checkpoint that answers choice, noul and score questions with calibrated distributions on one 48 GB GPU.
Jev Models & Reimplementations#community#open-models#local
Community
WorkMagic's self-distilled churn decision model for Slack customer conversations: a LoRA on Qwen/Qwen3-8B trained with no human labels that returns churn-likelihood option probabilities through the AnyJev Tacit interface.
Jev Models & Reimplementations#community#open-models#local
Community
A LoRA fine-tune of Strands Decider 2B that rates jokes on seven comedy traits, answering each as a typed yes/no question with a probability; an unofficial fan project that cannot write jokes and is not endorsed by Conan O'Brien.
Jev Models & Reimplementations#community#open-models#humor
Community
A 6.5M-parameter independent chess policy model built from Jev's public description: it returns a calibrated distribution over legal moves in one forward pass, with a self-reported held-out top-1 of 36.5%, ECE 0.036 and Elo 1239 plus or minus 51.
Jev Models & Reimplementations#community#open-models#chess
Community
jjshin's full fine-tune of Laya, the ModernBERT-large System-1 decision model, with a 421M encoder and a decision head trained by a hand-written 404-line PyTorch RLCD loop and LBFGS temperature calibration.
Jev Models & Reimplementations#community#open-models#rlcd
Community
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
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
StartLux Labs' 27B AutoThink decision model reads a state (text, JSON or up to 8 images) and typed questions, answers in one pass and only writes a thought when the top probability falls below the 0.6 gate; self-reported Decision Index 0.3 public score 73.37.
Jev Models & Reimplementations#community#open-models#local
Community
StartLux Labs' 4B Instant decision model returns a probability for every option of every typed question from one forward pass with no generated text; it self-reports Decision Index 0.3 public 67.88 and DecisionPivot pair accuracy 41.5.
Jev Models & Reimplementations#community#open-models#local
Community
Standard Thinking's 3B decision model with a joint schema head: it scores every option of every question in a scenario in one forward pass through POST /v1/systemone, and v3 self-scores 51.53 on the Decision Index 0.3 public index, up from v2's 42.90.
Jev Models & Reimplementations#community#open-models#local
Community
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
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
Standard Thinking's 8B decision model with a joint schema head that reads the scenario once and scores every option of every question; v4 self-scores 57.40 on the Decision Index 0.3 public index, up from v3's 48.34, with a 70.6M-parameter head.
Jev Models & Reimplementations#community#open-models#local
Community
A 2B decision model from Strands Agents for agentic workflows: a LoRA adapter on Qwen/Qwen3.5-2B-Base with a readout head that answers noul, choice and score questions with calibrated confidence, reporting 0.7619 on JevBench at 4096 context.
Jev Models & Reimplementations#community#open-models#local
Community
Perplexity AI's 27B decision checkpoint, a fine-tune of Qwen/Qwen3.8-27B with a BF16 decision head and saved calibration temperature; the card reports a Decision Index of 61.56, up from 56.4 in v1 and above Jev by more than 3.5 points.
Jev Models & Reimplementations#community#open-models#multimodal
Community
A 27B decision model from torchcast-ai that answers choice, noul and score questions together in one forward pass over the TypeSafe-compatible /v1/systemone format; the card reports Decision Index 0.2.1 of 65.00, +7.09 over Jev 1.13, at 33 ms per yes/no question.
Jev Models & Reimplementations#community#open-models#local
Community
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
FRIDA-Decisions answers Russian text with structured decisions in a single encoder pass (choice, ordinal score, yes/no or ranking) and scores 0.893 on the 735-item razvilka benchmark, effectively level with TypeSafe Jev's 0.897 (McNemar p = 0.84).
Jev Models & Reimplementations#community#open-models#russian
Community
Torchcast AI's 12B decision checkpoint: a LoRA fine-tune of google/gemma-4-12B-it that answers noul, choice and score questions in one forward pass, with a card-reported, author-run 203/231 (87.88%) on the JevBench v1.4 public items.
Jev Models & Reimplementations#community#open-models#local
Community
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
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
Perplexity AI's 27B decision model fine-tuned from Qwen/Qwen3.8-27B, producing option probabilities through a custom head instead of generated text; the card reports 85.71% overall across 11 benchmarks against Jev's 84.51%.
Jev Models & Reimplementations#community#open-models#multimodal
Community
StartLux's 35B-A3B mixture-of-experts decision model, answering typed questions through the TypeSafe /v1/systemone format with a probability per option; text, JSON or images up to 262,144 tokens, and a card-reported 210/231 on public JevBench.
Jev Models & Reimplementations#community#open-models#multimodal
Community
The Ollama build of iapp's OpenThai-SystemOne: a 0.8B Thai and English decision model whose v0.3 weights are fine-tuned for Ollama's /v1/systemone letter readout, reporting a 74.3 macro across 13 public subsets for Q8_0.
Jev Models & Reimplementations#community#open-models#local
Community
The 68M member of hotchpotch's Bekko System One v0 family: a compact English encoder for choice, noul and score decisions that returns candidate probabilities in parallel, with a ~196 MB browser ONNX export and no text generation.
Jev Models & Reimplementations#community#open-models#local
Community
The smallest Bekko System One v0 model: a 17M-parameter English encoder (3.90M excluding lookup-only embeddings, about 29 MB as browser ONNX) that answers choice, noul and score decisions without generating text.
Jev Models & Reimplementations#community#open-models#local
Community
The 9B entry in vLLM Semantic Router's Decision 2.0 family: a 7.94B decision model with a 16,384-token context that answers choice, yes/no and score questions in one pass, reporting 68.1 JevArena and 18.4 ms median single-question latency.
Jev Models & Reimplementations#community#open-models#local
Community
The 4B model in vLLM Semantic Router's Decision 2.0 family: a 4.21B fine-tune of Qwen3.5-4B-Base that answers choice, yes/no and score questions in one forward pass, with a self-reported JevArena 63.6 and a 12.9 ms median per request.
Jev Models & Reimplementations#community#open-models#local
Community
Start here with the Jev AI model
Featured entries for scoring.
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
Real-time geopolitical intelligence dashboard that uses TypeSafe Jev questions to score news headline severity into five threat levels and categorize events across 14 conflict, cyber and infrastructure domains, alongside news aggregation and a Country Instability Index.
Jev Integrations & Adapters#community#osint#geopolitics
Community
koala73/worldmonitorTypeScript
koala73/worldmonitorTypeScriptAutonomous agent platform with 187,504 stars for building and running agents from plain English or a visual builder. Its platform features first-class TypeSafe Jev decision blocks for typed routing, filtering, scoring and confidence-gated next-action dispatching.
Jev Integrations & Adapters#community#autonomous-agents#platform
Community
Significant-Gravitas/AutoGPTPython
Significant-Gravitas/AutoGPTPythonJev AI model articles
Original guides that go deeper on Jev scoring.
Voice agents need decisions in milliseconds. How turn detection, intent, and escalation work in real time, and where a fast decision layer fits.
Jev Practices & Patterns#voice#realtime#agents
Read article
Synthetic labels can train a decision model, but they can also bake in a teacher's errors. How to generate and audit them.
Jev Practices & Patterns#synthetic-data#training#distillation
Read article
Model deprecations and price changes are operational risks. How to pin versions, read contracts, and plan a migration before you need one.
Jev Guides & Articles#operations#vendor-risk#sla
Read article
From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: A podcast segment on TypeSafe AI, its $40M raise, and the Jevons paradox behind the name.
YouTube: Gary Explains the catch: Jev understands natural language but answers with structured values and confidence.
YouTube: Sam Witteveen on System 1 thinking, then Choice, Score, and Noul demos, and chained actions.
Hacker News: Pipeable onesie CLI with shell-safety exit codes and a calibrate mode over labelled commands.
frodikarlsson3 pointsShow HN: onesie – An expressive Unix-pipeable CLI for System One models like JevRead the thread on Hacker NewsHacker News: Spoken-language learning with video scenes branching on TypeSafe Jev.
j_rcht1 pointShow HN: Worklaude – learn by speaking; video scenes branch on TypeSafe JEVRead the thread on Hacker NewsHacker News: OpenAI's Luna Decisions API framed as a fast follow to TypeSafe's Jev wave.
matt_d6 pointsOpenAI Answers TypeSafe's Jev with a Decision API Built on LunaRead the thread on Hacker NewsHacker News: Nikola Balić on what made the Jev launch easy to try and easy to share.
nkko2 pointsWhat TypeSafe Got Right with the Jev LaunchRead the thread on Hacker News