Releases·Model Release
Trained locally: ultra-fast 0.8B/2B System 1 decision models that match Jev on benchmarks and Doom, ~30 ms per decision (open weights)
A developer fine-tuned Qwen3.5 0.8B/2B and Gemma models into zero-shot classifiers ('Jeff models') that return calibrated probabilities in one forward pass, releasing Apache 2.0 weights on HuggingFace. The 2B scores 83.1% on a five-benchmark panel versus Jev's published 83.0%, while deciding in ~28 ms locally. Training used only local hardware and synthetic data from a local teacher model, replicating TypeSafe's Jev experiment with small models.
191 engagement2 sourcesMon, Sep 28, 2026, 08:17 PM
AI Overview
- Reddit user mstrasser released Apache 2.0 Jeff models (Qwen3.5-0.8B/2B, Gemma4-E2B): 2B scores 83.1% vs Jev's 83.0% on a five-benchmark panel
- Jeff decides in ~29 ms on an M4 Max via one forward pass, no text generation, using a Jev-compatible API
- Training used only local hardware: one RTX PRO 6000, ~31k synthetic questions written by Qwen3.8-Flash-Next on two DGX Sparks
- Zero-shot games: Jeff 0.8B matches Jev's Doom score (6.55 kills) without a prompting crutch, at ~29 ms vs Jev's ~212 ms per call
- Jeff trails Jev on multi-step reasoning: BBH 64-68% vs 94%, ~50% on JevBench hard tier vs ~73%
- Author found wording of options matters enormously; the 0.8B beat the 2B in games despite lower benchmark scores
Related
Releases·Model Release
Kev: tiny Jev-like decision models (0.8B/4B/9B) on Qwen3.5 you can train and run locally - the 9B fits a 32GB Mac
Kev is a family of small Jev-architecture decision models (0.8B, 4B, 9B) built on Qwen3.5 and released under Apache-2.0 with training code and frozen eval suites. The 9B model runs in bf16 on a 32GB Mac, and the models are API-compatible with TypeSafe's System One, so its Python SDK works against a local Kev server unchanged. The release targets LLM-as-judge eval pipelines, and the web playground demonstrates option-order bias that judge models commonly exhibit.
Kev is a family of small Jev-architecture decision models (0.8B, 4B, 9B) built on Qwen3.5 and released under Apache-2.0 with training code and frozen eval suites. The 9B model runs in bf16 on a 32GB Mac, and the models are API-compatible with TypeSafe's System One, so its Python SDK works against a local Kev server unchanged. The release targets LLM-as-judge eval pipelines, and the web playground demonstrates option-order bias that judge models commonly exhibit.
34 engagement1 sourceredditTue, Sep 22, 2026, 03:08 AM
Products·launchedfeatured
TypeSafe Jev: AI model that returns typed decisions with probabilities instead of text
Jev is a decision-only AI model from TypeSafe: you provide app state plus typed questions (yes/no, pick-one, score) and it returns answers with probabilities, no text to parse. It targets the many LLM calls in apps that are really just classification or routing decisions, offering a cheaper, faster alternative for developers building agentic or decision-driven software.
Jev is a decision-only AI model from TypeSafe: you provide app state plus typed questions (yes/no, pick-one, score) and it returns answers with probabilities, no text to parse. It targets the many LLM calls in apps that are really just classification or routing decisions, offering a cheaper, faster alternative for developers building agentic or decision-driven software.
34031 engagement8 sourcesreddit, rssWed, Sep 16, 2026, 11:42 PM
Releases·Tool Release
Show HN: Snap – local-first, open-weight 2B decision engine by emnlmn
A developer released Snap, a local-first decision engine serving as a drop-in replacement for the TypeSafe System One API, built to test sensitive use cases locally. It ships with the open-weight snap1-2b model under MIT license, classifies, ranks, and decides on inputs in sub-second via single-pass logit reads rather than text generation. Installable on Mac via Homebrew, it targets practitioners wanting local decision models without cloud subscriptions.
A developer released Snap, a local-first decision engine serving as a drop-in replacement for the TypeSafe System One API, built to test sensitive use cases locally. It ships with the open-weight snap1-2b model under MIT license, classifies, ranks, and decides on inputs in sub-second via single-pass logit reads rather than text generation. Installable on Mac via Homebrew, it targets practitioners wanting local decision models without cloud subscriptions.
1 engagement1 sourcehackernewsThu, Oct 1, 2026, 02:06 PM
Releases·Model Release
Jiwo Qwen3.5 fine-tunes top sub-1B and place second in 3–6B on the decision index leaderboard
A developer released Jiwo, two decision-model fine-tunes of Qwen3.5 (0.8B and 4B) that top categories on the decision index leaderboard. The work centered on data calibration from adapted external datasets. It shows community fine-tuning of small LLMs can substantially outperform base-model peers on decision tasks.
A developer released Jiwo, two decision-model fine-tunes of Qwen3.5 (0.8B and 4B) that top categories on the decision index leaderboard. The work centered on data calibration from adapted external datasets. It shows community fine-tuning of small LLMs can substantially outperform base-model peers on decision tasks.
1 engagement1 sourceredditMon, Oct 5, 2026, 05:05 PM
Releases·Model Release
Amazon releases its own Jev clone as decision models flood the web
AWS released Strands Decider 2B, an open source decision model that sorts between pre-decided options with calibrated confidence scores instead of generating text, built on the Qen3.5-2B LLM torso. Distinguished engineer Marc Brooker built it after seeing TypeSafe's Jev, and it briefly topped the Jevbench ranking for its size class before Amazon released it via Strands Labs. The release lands the same week OpenAI announced a similar offering, amid a wave of decision-model clones following Jev's debut.
AWS released Strands Decider 2B, an open source decision model that sorts between pre-decided options with calibrated confidence scores instead of generating text, built on the Qen3.5-2B LLM torso. Distinguished engineer Marc Brooker built it after seeing TypeSafe's Jev, and it briefly topped the Jevbench ranking for its size class before Amazon released it via Strands Labs. The release lands the same week OpenAI announced a similar offering, amid a wave of decision-model clones following Jev's debut.
101 engagement2 sourcesrss, githubThu, Oct 1, 2026, 04:49 PM