Calibration & Research

Jev-style replica: frozen Qwen3-1.7B plus a ~0.8M-parameter trained head returns calibrated Choice, Boolean and Score distributions.

Browser lab and TypeSafe-shaped API for asking typed questions of a Nemotron diffusion model and inspecting the distributions.

nev-lite: 11.8M-parameter static-embedding decision head that scores fixed option sets with no transformer pass at inference.

OmniJev: omni-modal 4B decision model answering typed questions about images, video, screens and robot scenes in one pass.

Open 150M encoder that answers noul, choice and score questions in one pass, trainable on a free Colab T4 in about 30 minutes.

Two frozen open models answer yes/no per exam option; their agreement drives an auto-release gate and a conformal candidate set.

spark-s1: local Jev-inspired decision models on Qwen3.5-4B for NVIDIA DGX Spark, trained on code- and solver-labelled decision rows.

Verdict: 151M ModernBERT/GLiClass non-autoregressive decision models with calibration heads, reporting wins over Jev and Laya.

Research toolkit that encodes an image once and answers several typed questions from a shared distribution, plus OpenJev-0.6B text models.

Research engine for local System One decisions via llama.cpp logits projection with Qwen, running sub-second on a 4 GB laptop GPU.

Hugging Face Space for parallel constrained decoding on Apple Silicon: fills multi-field JSON schemas in parallel with field confidence.

Phocinae Largha: 144.3M bilingual English/Chinese decision model on mmBERT-small with calibrated Choice, Score and Noul in one pass.

Open decision models fine-tuned from Qwen at 0.8B to 9B that return a choice and per-option probabilities for Choice, Noul and Score.

Prototype Jev-style decision model on Qwen3.5-0.8B using prefix-fork execution to answer typed questions in one pass without generation.

Reproducible MLX and Colab recipes that turn Qwen decoders into Jev-like typed decision models, from 0.5B on a laptop to a noncausal 27B.

Open Jev-style text-and-image decision models built by a self-improving AI research loop, with an effort setting that picks depth.

StartLux's open typed decision models from 0.8B to 27B plus a 35B-A3B MoE, reading text, JSON or images up to 256K tokens.

Open System One model and MLX runtime returning calibrated choice, score and noul probabilities via a Jev-compatible API.

Gemma 4 12B with its LM head replaced by a small answer head, serving Jev-style noul, choice and score decisions with custom code.

Together AI's experimental Qwen3.5 fine-tunes (4B and 0.8B) that pick one lettered option from a state, question and option list.

Open 1.9B typed-decision model that returns an option index and calibrated probability in one forward pass, with a paper and API.

Open Jev-style decision models with vision (Qwen3.5 4B/9B LoRA) serving Choice, Score and Noul on /v1/systemone.

SemIf (formerly OpenJev): reads typed option probabilities directly from open models like Qwen3.5-4B, including image inputs, on a home GPU.

Open Apache-2.0 4B decision model (Wald-Q4B) served via a Jev-compatible API, thinking only when unsure, with JevBench 210/231.

Apache-2.0 Gemma 4 12B fine-tune for local typed decisions, chat and image input, served via /v1/systemone and chat completions.

Static GGUF quantizations by mradermacher of SargeDev's JEV-27B-Uncensored, a Jev 1.13-distilled decision model.

Static GGUF quantizations by mradermacher of SargeDev's JEV-27B-VL-Uncensored vision decision model.

122M multilingual local decision model answering Choice, Noul and Score in milliseconds on CPU behind a /v1/systemone server.

OpenJev: 151M ModernBERT decision model with RLCD-style calibration, a JevBench audit and an in-browser WebGPU playground.

Jev-style medical QA on Qwen3.5-4B that picks the answer option in one forward pass instead of generating text.