Releases·Tool Release
LLM2Jev turns local LLMs into Jev-style structured decision engines with prefill-only binary inference
A new open-source project, LLM2Jev, adapts local Hugging Face-compatible causal language models into Jev-style structured decision engines, emitting typed Choice, Score, and Noul answers with probabilities. It uses prefill-only binary inference — scoring candidates with yes/no logit judgments without generating answer tokens — and leverages SGLang's Radix Cache for shared-prefix efficiency. The project is independent and unaffiliated with Jev or TypeSafe.
109 engagement1 sourceSat, Sep 19, 2026, 10:59 AM
AI Overview
- LLM2Jev adapts local Hugging Face-compatible causal LLMs to Jev-style decisions, answering runtime-defined Choice, Score, and Noul questions with typed answers and probabilities
- It derives probabilities from logits via independent yes/no judgments per candidate, assembling JSON in code without generating answer tokens
- It stages candidate submissions to reuse SGLang's Radix Cache — scoring a real criteria candidate first, then each candidate once, sharing state and instructions prefixes
- Choice and Score outputs include confidence; Noul returns the probability a condition is true
- The independent open-source project runs on Linux with NVIDIA GPUs via SGLang and is unaffiliated with Jev or TypeSafe
Entities
Related
Products·launched
Jevless: Jev-style typed decisions (Choice/Noul/Score) from any logprobs-exposing model API
Jevless is an open-source tool that produces Jev-style typed decisions — Choice, Noul, and Score outputs — using any LLM whose API exposes logprobs, rather than requiring a single supported backend. It also ships a local /v1/systemone-compatible server. The author built it to keep Jev's low cost, speed, and bias-canceling accuracy while working across as many model backends as possible.
Jevless is an open-source tool that produces Jev-style typed decisions — Choice, Noul, and Score outputs — using any LLM whose API exposes logprobs, rather than requiring a single supported backend. It also ships a local /v1/systemone-compatible server. The author built it to keep Jev's low cost, speed, and bias-canceling accuracy while working across as many model backends as possible.
1 engagement1 sourceredditFri, Sep 25, 2026, 07:57 PM
Releases·Tool Release
Nokia Applied Research releases AnyJev, turning any LLM into a Jev-style typed-decision model with real probabilities and no training
Nokia Applied Research, with a Tencent Hunyuan collaborator, released AnyJev, an open-source Python package (Apache-2.0, on PyPI) that converts any LLM into a Jev-style decision model producing typed decisions and real probabilities with no training. The release demonstrates that raw logit readout is order-sensitive — Qwen3-8B flips a BANKING77 answer with 1.00 confidence when options are reversed — while AnyJev's L0 mode stays consistent with zero labels.
Nokia Applied Research, with a Tencent Hunyuan collaborator, released AnyJev, an open-source Python package (Apache-2.0, on PyPI) that converts any LLM into a Jev-style decision model producing typed decisions and real probabilities with no training. The release demonstrates that raw logit readout is order-sensitive — Qwen3-8B flips a BANKING77 answer with 1.00 confidence when options are reversed — while AnyJev's L0 mode stays consistent with zero labels.
144 engagement1 sourcegithubMon, Sep 21, 2026, 05:55 AM
Releases·Tool Release
How to get Jev-style typed decisions out of any hosted LLM provider today, no new model needed
A developer released decidr-ts, an MIT-licensed open-source tool that extracts typed decisions from any hosted LLM provider without new models. It sets max_tokens to 1 and reads logprobs over user-supplied option ids, cutting speed and costs for many tasks. A web demo is available.
A developer released decidr-ts, an MIT-licensed open-source tool that extracts typed decisions from any hosted LLM provider without new models. It sets max_tokens to 1 and reads logprobs over user-supplied option ids, cutting speed and costs for many tasks. A web demo is available.
4 engagement1 sourceredditWed, Sep 23, 2026, 08:30 PM
Products·launched
notjev – turn any OpenAI-compatible LLM into a Jev-style decision engine via single-token logprobs
notjev is a Node library that makes any OpenAI-compatible model produce fast multiple-choice decisions. It requests one token with logprobs enabled, renormalizes the letter probabilities, and returns a verdict, probability, margin, and abstention threshold. This gives Jev-style Choice/Score/Noul outputs to developers running local models (vLLM, llama.cpp, Ollama) without runtime dependencies.
notjev is a Node library that makes any OpenAI-compatible model produce fast multiple-choice decisions. It requests one token with logprobs enabled, renormalizes the letter probabilities, and returns a verdict, probability, margin, and abstention threshold. This gives Jev-style Choice/Score/Noul outputs to developers running local models (vLLM, llama.cpp, Ollama) without runtime dependencies.
29 engagement1 sourceredditTue, Sep 22, 2026, 08:57 AM
Releases·Tool Release
Turning GLM-5.3-Flash into a Jev-like decision model
A privatemode.ai blog post describes a prompting approach that gives standard LLMs like GLM-5.3-Flash Jev-like decision properties: crafting the input prompt so the first output token answers the question, enabling decisions in a single forward pass. The post details implementation with GLM-5.3-Flash and vLLM and benchmarks it against Jev and Laya.
A privatemode.ai blog post describes a prompting approach that gives standard LLMs like GLM-5.3-Flash Jev-like decision properties: crafting the input prompt so the first output token answers the question, enabling decisions in a single forward pass. The post details implementation with GLM-5.3-Flash and vLLM and benchmarks it against Jev and Laya.
172 engagement1 sourcehackernewsSat, Sep 26, 2026, 03:49 PM