Does it verify the remote model instead of trusting it?

No. It trusts the model for now and only reports deployment health: the top keys spell the version, and JevModel is a fixed string. Operators should still validate answers downstream.

Can I really point my hosted-Jev client at this unchanged?

It speaks the same POST /v1/systemone contract as hosted Jev, so a client written for Jev can point at the local server. Accuracy and calibration can still differ by model.

Do I need a GPU, or will CPU handle local decisions?

Yes. CPU works with no GPU required; weights stay resident and a bounded prefix cache reuses decoded state. A large diffusion checkpoint needs much more memory than a small autoregressive one.

How do I choose between reflex, SemIf, and RWKV layouts?

Pick the layout matching the backend: reflex_markdown by default, plus compact, semif, and rwkv_jev variants, with chat-template wrappers including native GGUF templates and auto detection.

Which prompt styles does it support?

reflex_markdown (default, up to 26 options), reflex_compact (JSON, up to 26), semif (JSON, up to 16), rwkv_jev (word readout, up to 128), and custom via --prompt-file; the style is how reflex, SemIf, and RWKV-Jev are switched.

What is the diffusion mode for?

It answers typed decisions on block-diffusion models like DiffusionGemma and LLaDA-MoE, where there are no next-token logits; a canvas is seeded with a pinned answer template and one denoise step gives a distribution over each slot.

How does it handle multi-step questions?

Questions can be staged with depends_on and ask_if, run in dependency levels with one joint canvas read per level; later levels are conditioned on earlier answers via a prefilled "Answers so far" context, and a question whose dependency is outside its allowed values is skipped and answered null.

CommunityJev Models & Reimplementations

ifReflex.cpp

Native C++ server on llama.cpp that answers System One choice, score, and noul questions locally in one prefill pass with zero generated tokens.

See highlights

Open github.com

Stars: 1
Language: C++
License: MIT
Published by: Community
Author: DreamBlooms
Last verified: Oct 2026

DreamBlooms/ifReflex.cpp

Organization repository on GitHub

View on GitHub

Any LLM can serve as SystemOne decision model on your Personal Computer. And support for LLaDA-MoE/DiffusionGemma.

Forks: 0
Last pushed: Updated Oct 2026

Repo stats from the GitHub API, cached Oct 2026.

About this project

Any LLM. Structured decisions. No generation. No GPU. Native C++ inference for System One decision models, built on llama.cpp. You send a piece of state and a map of typed questions; it answers every one in a single forward pass and returns the full probability distribution over your exact options — no prose, no parsing, no "As an AI language model". Answers are read straight off the model's next-token logits, so output cost is zero generated tokens.

Quoted from the project's README. Read the full README.

Tagscommunity github

Works forJev for Classification

Used inJev for Software & DevTools

Highlights

Prefill-only inference reads answers from next-token logits, with full distributions and no prose to parse.
One binary covers reflex, SemIf, RWKV-Jev, and stock instruct GGUFs behind a Jev-compatible HTTP API.
State prefix decoded once per request and reused across requests via a bounded LRU cache.
Prompt styles, chat templates, diffusion mode, permutations, and staged depends_on questions supported.

Watch out

Local inference needs a compatible GGUF and enough RAM, and matching the wire contract does not mean matching hosted accuracy. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

More Jev resources like this

system-one-foundation-models

Native Swift 6 package that evaluates @Generable structs and enums through System One decision models in 15-150 ms, with on-device Laya, self-hosted laya-serve, and TypeSafe Jev cloud backends.