~ / open-implementations
##Open Implementations
Train or run one yourself.
Same request shape on open weights. Not TypeSafe's architecture.
### Trained replicas 23
AgentJev-0.6B: a small trained decision model that takes unstructured state (diffs, traces, logs) and returns calibrated distributions in one ~50 ms forward pass. Apache 2.0.
Qwen3.5 fine-tunes (0.8B up to a 35B MoE, plus a vision build), Apache 2.0 weights on Hugging Face. The vision build ranks #2 on Image JevBench. Serves TypeSafe
/v1/systemone, so the official SDK works by repointingTYPESAFE_BASE_URL.Nace.AI's open decision models: v1.5 on a distilled Qwen3.5-9B and DLM on an Efficient-DLM-8B diffusion backbone, each serving
/v1/systemonethree ways: Python/vLLM, a llama.cpp GGUF fork, or Ollama.SberAI's Russian-first encoder decision model:
choice/score/noulplus arankingquestion type, all questions scored in one packed encoder pass with an exact state cache. Torch, int8 ONNX, MLX, or vLLM.HIT-TMG's recipe for turning an embedding model into a decision model:
choice/score/noulheads over your pick of embedder, with a Hugging Face model collection and dataset. Apache 2.0.Open-weight Jev alternative (4B and 9B, Apache 2.0) serving
/v1/systemoneat about 13 ms on an H100. JevBench v1.4 ranked v0.2 second of 76 systems and first open entrant, though the project itself reports Jev edging it on 308 fresh sealed items. Two-teacher distillation, pre-registered epoch sweeps.Train a small one-pass scorer over a changing list of text options. Each option queries the context; a shared head returns one probability per option. Byte encoder from scratch, or a frozen Hugging Face encoder.
MiniCPM5-1B cut to 17 layers with a one-logit head, quantized to GGUF and run on CPU via llama.cpp. Serves TypeSafe
/v1/systemonefornoul(yes/no) questions only, so the official SDK works by repointingTYPESAFE_BASE_URL. MIT.Small trained decision models on Qwen3.5 (0.8B and 4B) serving
/v1/systemoneat ~50 ms on one H100. jiwo-0.8b ranks #1 of 27 under 1B on the Decision Index board; both models are on the board. Apache 2.0.LoRA plus a pointer readout on Qwen3.5 (0.8B / 4B / 9B), Apache 2.0, with training code and frozen eval suites. TypeSafe
/v1/systemonedrop-in, calibrated by default via a fitted temperature. Frozen evals put Kev-9B about 3.5 points behind Jev on unseen sources; MLX, ROCm, CUDA, and a one-command Modal deploy. Also the only open-weights route on OpenRouter's Decisions API.0.6B replica trained from scratch, MIT. Ships the weights, the dataset, and the end-to-end training pipeline, plus a side-by-side demo against Jev.
Qwen3.5-9B LoRA from Bespoke Labs. Contrastive recipe, not distilled from Jev. 90% vs Jev 93% on a 324-example holdout.
150M ModernBERT encoder that answers per-request
noul/choice/scorequestions in one pass. Trains on a Colab T4 in about 30 minutes. Not the same project as SemIf, which was also once called OpenJev.151M ModernBERT decision engine, non-autoregressive, with published calibration numbers. Claims the top spot over Jev and Laya on the LocalLLaMA typed-decisions benchmark.
Largha-150M: a 144M bilingual decision model on mmBERT-small (English-first, Chinese evaluated on translated cases). Calibrated
noul/choice/scoreanswers robust to option reordering, one pass per decision.0.8B multimodal Jev-like model that plays GUI games directly from raw pixels. Apache 2.0.
Independent reproduction of Jev-style post-training on MiniCPM5-2B via LoRA: 51% → 80.5% on a sealed, leakage-checked holdout for about $5 of compute. Stage 1 covers single-letter
choiceonly; the rigor is the point — significance testing, baselines, and a technical report that voids its own figure after finding contamination. MIT.Recursively self-improving research loop that trains the models: AI agents propose hypotheses, register predictions before spending GPU time, and retire their own champions on the evidence. Eight releases in 13 days, 496 published experiments including failures; v6.1-VL 27B posts 65.64 on the Decision Index 0.3 public set, the top listed score. MIT code, Apache 2.0 weights, Jev-compatible API.
Jev-style replica on Gemma 4 E2B / Gemma 3 270M, trained and served on Modal. One forward pass, no decoding. Live demos for support, Doom, browser-use, and smart home.
Together AI's Jev-inspired experiment: Qwen3.5 fine-tunes that take state + question + 2–24 options and return one letter — openly next-token, not a non-autoregressive runtime. MIT, with full data recipe and a "train your own for $17" writeup. A 4B experimental build is also served on OpenRouter.
Train-your-own multimodal System One model: evaluates text, images, and video against task instructions and returns probabilities over supplied candidates. Ships the model code, data pipeline, SFT plus experimental RLCD training, and evals.
Typed decisions from a frozen LM plus a small physics engine, from Laya's author: calibrated probabilities, conformal answer sets, and an explicit abstain flag. 800M or 4B, 73K context, image inputs.
ModernBERT-base fine-tune with a decision head that answers
noul/choice/scoreand also multi-label questions, with a fitted temperature per question type. Publishes its held-out numbers: 61.2% accuracy and 0.043 ECE on datasets it never trained on. Apache 2.0 code and weights; serves TypeSafe/v1/systemone, so the official SDK works by repointingTYPESAFE_BASE_URL, and an ONNX build runs in the browser.
### Serve any open model 16
Turns any LLM into a Jev-style decision model: typed decisions with probabilities, no training. From Nokia Applied Research, Apache 2.0.
DiffusionGemma plus vLLM: an inference method rather than new weights, with native image inputs and a hosted API. Ranked 3rd on JevBench v1.2, just under Jev itself.
Self-hosted drop-in on GLiFormer 400M, MIT. Speaks the TypeSafe request shape (JevBench drove it with the official adapter); ranked in v1.2.2.
Jev-style parallel constrained decisions for any MLX model on Apple Silicon. Scores every allowed answer per schema field in one pass, so the output is valid by construction.
Educational Jev-like visual inference experiment on Apple Silicon: shared context, direct candidate scoring, and local visual demos.
Adapts local language models into Jev-compatible decision engines with Choice / Score / Noul outputs, via prefill-only binary inference. Apache 2.0.
Ollama for decision models:
ollaya pull/serve/runmanage Laya, decider, NLI, and GLiClass locally behind a wire-identical TypeSafe/v1/systemone(and/v1/decisions) endpoint.System One-style layer on open weights, ranked on JevBench. Its cautionary detail: 72% vs 21% on the same answer-judging items with option order flipped.
Open, Jev-compatible System One decision server on DiffusionGemma. Apache 2.0.
TypeSafe
/v1/systemoneon Qwen 35B MoE via SGLang. Prefill once, then first-token logits per question. No extra training.Open recreation on Qwen3.5. Prefills the state once, then scores every question in parallel from next-token logits. Serves the TypeSafe request shape. Browser demo on WebGPU. #3 on Image JevBench in its released stable configuration.
Runtime-defined semantic decisions from direct option logits. CUDA, MLX, and WebGPU, plus committed benchmarks. Repo recently renamed from SemIf.
Turns any open model into a classifier / Jev endpoint. From Featherless, Apache 2.0.
Turns any open LLM into a System One classifier: batched single-token choice inference, TypeSafe SDK compatible. Doom and wikiracing demos on Qwen3-8B.
Apple's Neural Engine as a free local System One decision engine on macOS: "Jev at home."
Non-autoregressive local drop-in, sub-15 ms decisions, Apache 2.0.
nothing matches. try fewer words, or clear a filter.