StartLuxLabs/StartLux-Decision
Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.
A star delta has been computed for 62,252 of 63,106 tracked repositories in this period, and only those can be ranked in it.
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Star counts measured between 06:57 and 09:51 (BKK).
Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.
斑海豹 — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. 小海豹，大决断。
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)
Jev-compatible API server for open decision models. Ollama for decision models: pull and serve 29 model families (Laya, pplx-decider, Clef, Winnow, coba...) locally or self-hosted behind /v1/systemone. The TypeSafe SDK works unchanged: set its base URL and pick an open model. CLI, desktop app, Docker, MCP server, public bench.
🚀🚀 A multimodal System One decision model that gives calibrated answers to typed questions about screens, photos, video and text in one forward pass.
Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)
Open-source alternative to TypeSafe's Jev: a System One style model layer that gives typed, calibrated decisions from any open-weights LLM in one forward pass (HF + vLLM), with honest benchmarks
JevK5: open-weight alternative to TypeSafe Jev. Typed decisions with probabilities in one forward pass; Apache-2.0 weights and code.
Control monitor settings using DDC/CI and USB
A generic and robust calibration toolbox for multi-camera systems
Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground
Open reproduction of TypeSafe Jev: a 150M typed decision engine (noul/choice/score in one non-autoregressive pass, calibrated confidence). 0.697 vs Jev's 0.727, 2.5x better calibrated, 4x faster, free. Trains on a Colab T4 in 30 min.
Calibrate Jev questions against your own labels: tune criteria on labelled examples, confirm on a held-out set, get a verdict per question. Unofficial.
Steam Controller Sticks Calibration Tool
High-performance quantitative finance in Rust and Python — 130+ stochastic processes, option pricing, calibration, fixed income, risk & copulas, with SIMD/GPU acceleration.
Fatrop is a high performance nonlinear optimization solver for optimal control and robotics
Full-weight Qwen decision model with calibrated probabilities, training code, REST API, and playground.
Innovative externally-scored agentic ML benchmark for next evolution of AI — real competition ground truth, two-submission feedback, attacked rewards
[Public preview] Externally scored agentic ML research benchmark: 60 tasks, real competition ground truth. Open protocol, operated evaluation.
Ask an open vision-language model typed questions about an image and get probabilities back, on your own machine.
Proteomics search software with integrated calibration, PTM discovery, bottom-up, top-down and LFQ capabilities
Open, calibrated, single-pass typed-decision models (4B & 27B) for finance and trading
High-performance, high-precision, locally-fast-deployed Jev-class medical decision service on national medical exams — four readings, one routed decision, zero training.
ASAM XCP in Python
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
That's all 27 repositories in this period.
