Releases·Model Release
ImaJev-4b: I spent 15 days fine-tuning a 4B model to make business decisions from text and photos, and it just ranked #1 of 91 on JevBench & ahead of GPT-5.6 Luna on DecisionBench
A business consultant fine-tuned Qwen3.5-4B into ImaJev-4b, a model that makes business decisions from text and up to two photos in one forward pass. It ranked #1 of 91 on JevBench (67.37) and #3 of 56 on DecisionBench, ahead of GPT-5.6 Luna and DeepSeek V4.1. Weights are released under Apache-2.0 with code and a demo, and the total training cost was about $1,200 in rented GPU time.
245 engagement1 sourceMon, Sep 28, 2026, 02:53 PM
AI Overview
- Consultant Mohit fine-tuned Qwen3.5-4B with a LoRA and decision head to output per-option probabilities plus 'unknown' in one forward pass
- First 500k-decision fine-tune dropped JevBench hard reasoning 64.9 to 42.3; two-model-agreement data filtering recovered it
- ImaJev-4b scored 67.37 on JevBench v1.4.2.2 vs Jev 1.13.0's 63.29, ranking #1 of 91; #3 of 56 on DecisionBench, ahead of GPT-5.6 Luna
- The #1 JevBench score weighs accuracy, calibration, speed, and cost equally; on accuracy alone ImaJev-4b ranks #3
- Released Apache-2.0 weights, code, and a demo; runs on a Mac via MLX or one GPU; cost about $1,200 in rented GPU time
Related
Releases·Model Release
H2O.ai releases H2O-Lightning-4B, Apache-2.0 decision model that tops JevBench open leaderboard at 72.5 vs Jev 1.13's 71.5
H2O.ai released H2O-Lightning-4B, an open-weight Apache-2.0 model built for 'decision API' inference: it returns calibrated probabilities from a single forward pass with no generated tokens, making decisions fast and cheap. The 4B model (fine-tuned from Qwen3.5-4B) scores 72.5 on the JevBench public leaderboard, above Jev 1.13 at 71.5, making it the top open model. Announced by an H2O.ai employee with weights on Hugging Face; 12B and 31B versions are planned.
H2O.ai released H2O-Lightning-4B, an open-weight Apache-2.0 model built for 'decision API' inference: it returns calibrated probabilities from a single forward pass with no generated tokens, making decisions fast and cheap. The 4B model (fine-tuned from Qwen3.5-4B) scores 72.5 on the JevBench public leaderboard, above Jev 1.13 at 71.5, making it the top open model. Announced by an H2O.ai employee with weights on Hugging Face; 12B and 31B versions are planned.
77 engagement1 sourceredditFri, Oct 9, 2026, 08:26 PM
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.
2 engagement1 sourceredditFri, Sep 25, 2026, 10:53 PM
Releases·Model Release
EmpirioLabs releases Aplomb 1: open-weights 5.3B decision model, 1M context, text/image/video/audio in one request
EmpirioLabs released Aplomb 1, a 5.3B open-weights decision model built on Qwen3.5-4B that reads up to 1M tokens of text, JSON, images, video and audio in a single request. It returns probabilities for every tool and each enum/boolean argument, letting agents act on confident calls and defer uncertain ones to larger models. It ranks #1 on the Hugging Face Typed Decisions benchmark and #1 among 4B models on Decision Index 0.2.1.
EmpirioLabs released Aplomb 1, a 5.3B open-weights decision model built on Qwen3.5-4B that reads up to 1M tokens of text, JSON, images, video and audio in a single request. It returns probabilities for every tool and each enum/boolean argument, letting agents act on confident calls and defer uncertain ones to larger models. It ranks #1 on the Hugging Face Typed Decisions benchmark and #1 among 4B models on Decision Index 0.2.1.
Updated Fri, Oct 9, 2026, 03:58 PM — Aplomb 1 takes #1 on the Typed Decisions leaderboard with the best KL (0.123) and Brier (0.065) scores of any model list
02115 engagement3 sourcesredditTue, Oct 6, 2026, 05:56 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
Products·launched
Jiwo - Qwen3.5 fine-tuned small decision models topping the Decision Index leaderboard
Jiwo is a set of small decision models created by fine-tuning Qwen3.5 LLMs (sub-1B and 4B) for decision-making tasks. The models are evaluated on the Decision Index benchmark, where the 0.8B model tops the sub-1B category and the 4B model ranks second in the 3-6B class. It addresses the problem of getting strong decision performance from small, efficient models, relevant to researchers and practitioners benchmarking LLM decision-making.
Jiwo is a set of small decision models created by fine-tuning Qwen3.5 LLMs (sub-1B and 4B) for decision-making tasks. The models are evaluated on the Decision Index benchmark, where the 0.8B model tops the sub-1B category and the 4B model ranks second in the 3-6B class. It addresses the problem of getting strong decision performance from small, efficient models, relevant to researchers and practitioners benchmarking LLM decision-making.
1 engagement1 sourcehackernewsMon, Oct 5, 2026, 05:02 PM