Kitani releases OpenJudgement-4B-Preview, an open-weight Qwen-based alternative to Jev / TypeSafe

Social·Community

CLM: open Qwen3-8B projection head claims full API parity with TypeSafe AI's Jev at 4x-13x speed

A r/LocalLLaMA post argues that CLM, a new open-weights projection head built on Qwen3-8B, achieves functional parity with TypeSafe AI's proprietary Jev decision API while offering 4x–13x faster latency and free self-hosting. CLM implements Jev's full primitive set (Choice, Noul, Score) and reports state-of-the-art verifier scores when fine-tuned, though it trails Jev in zero-shot generalization and context length.

A r/LocalLLaMA post argues that CLM, a new open-weights projection head built on Qwen3-8B, achieves functional parity with TypeSafe AI's proprietary Jev decision API while offering 4x–13x faster latency and free self-hosting. CLM implements Jev's full primitive set (Choice, Noul, Score) and reports state-of-the-art verifier scores when fine-tuned, though it trails Jev in zero-shot generalization and context length.

Releases·Model Release

LibertAI releases Deem, an open-weight Jev alternative built on Qwen3.5-9B

LibertAI released Deem, an open-weight model positioned as an alternative to Jev, built on Qwen3.5-9B with a smaller 0.8B variant. It trails Jev on hard benchmarks but arrives early in a new model category, and the small version runs on CPU for local tinkering.

LibertAI released Deem, an open-weight model positioned as an alternative to Jev, built on Qwen3.5-9B with a smaller 0.8B variant. It trails Jev on hard benchmarks but arrives early in a new model category, and the small version runs on CPU for local tinkering.

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.

Releases·Model Release

Mica v0.1 4B: open Jev-style decision model (yes/no, choice, score) that runs on an 8 GB GPU — trained for under $30 of GPU time

A developer released Mica v0.1 4B, an Apache-2.0 decision model for agent gates and routers that returns calibrated probabilities instead of generating text. Built on Qwen3.5-4B with a rank-16 LoRA and trained on 77,732 rows for under $30 of rented RTX 3090 time. It scores below the closed Jev 1.13 API on benchmarks but resists in-state prompt injection far better (69% vs 18%) and runs in about 47 ms per decision on an RTX 3090.

A developer released Mica v0.1 4B, an Apache-2.0 decision model for agent gates and routers that returns calibrated probabilities instead of generating text. Built on Qwen3.5-4B with a rank-16 LoRA and trained on 77,732 rows for under $30 of rented RTX 3090 time. It scores below the closed Jev 1.13 API on benchmarks but resists in-state prompt injection far better (69% vs 18%) and runs in about 47 ms per decision on an RTX 3090.

Reddit users report that Alibaba's Qwen team has published a docs page for a model called decision-model-preview, described as a competitor to Jev. The page appeared without any announcement, and no open weights are available yet.