ezjev 4B s2

+ contribute

ezjev 4B s2 is an open-weights decision model from everettjf. Its published size is 4.66B parameters. Licence: other.

Catalogue identity notes

Exact Decision Index tested engine ezjev-4b-s2; reported model ezjev 4B s2. Type: LoRA; technique: autoregressive. Comparison entry; no current run command, API price or speed inferred from the benchmark. Current repository metadata checked separately; an unpinned alias is not evidence that today’s weights reproduce this historical run.

Model information sources

· Weights licence ↗ · Parameter count ↗

Publisher parameter count; includes the served backbone, not just adapter size.

54.6±1.3#162 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of ezjev 4B s2, out of 357 with a score.

#	model	score	where	released
162	kev-8b (DecisionBench c80773da) Jared Palmer	55±2	model files
163	Winnow-12B EldanRing	55±1	model files
164	ezjev 4B s2 everettjf	55±1	model files
165	JoshuaSP diffgemma (decision readout) JoshuaSP	55±1	source code
166	decisio 0.4.0 (Gemma 4 12B) (decision readout) aminry	54±1	source code	2026-10-04

see all decision models · find the ones that fit your machine

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #97 of 250 · 7.5% of its score · 6 tests

Beats Jev 1.13, Xor 26B-A4B v1.2, Senova-4B v2 here, though they rank higher overall. Trails OneJev1.6-0.8B, TokenRhythm NeoHorse-Jev-4B, Jev-Omni here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: API-Bank (accuracy) submitted	82.5% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 129/236	easy · avg 70%	▇ 2.3%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	96.0% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 150/246	very easy · avg 92%	▇ 2.0%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	20.4% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 88/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	88.1% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 197/246	easy · avg 72%	▇ 1.1%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	66.0% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 160/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.9% ✎	no other model yet		—	failed run: Publisher excludes this protocol because its prompt reveals GPT-4 as the best route. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

retrieval & classification▁▁▁·▁▁▁▁▃▃█▅ #80 of 258 · 6.5% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, oenogarum (Gemma 4 31B), Decider 31B (NVFP4) here, though they rank higher overall. Trails StartLux-Decision v1.1 0.8B, Standard One 3B SH (v4) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	44.4% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 145/246	medium · avg 38%	▇ 2.4%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	56.6% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 175/246	medium · avg 47%	▇ 2.2%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	62.0% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 63/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	72.0% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 136/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	92.8% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 172/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	85.6% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 136/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	41.0% ✎	no other model yet		—	failed run: Publisher reports a builder bug exposing dialogue turns after the classified turn; awaiting corrected rerun. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

language understanding▂▂▂▃▂▅▆▃█▆▄▃ #109 of 256 · 23% of its score · 9 tests

Beats jev-brain · Gemma-4-31B (decision readout), deck31b (Gemma 4 31B) (decision readout), Decider chat · Gemma-4-31B (decision readout) here, though they rank higher overall. Trails Decider 4B, jiwo 4B v2, Sieve 4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	78.2% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 130/246	easy · avg 74%	▇▇ 5.4%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	92.4% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 147/246	very easy · avg 85%	▇▇ 5.3%	sharp
Decision Index 0.4.1: ANLI (macro-F1) submitted	64.0% ✎	▁▂▃▅▅█▂····▁ beats 121/246	medium · avg 60%	▇▇ 5.3%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	54.4% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 154/246	medium · avg 44%	▇ 2.3%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	34.4% ✎	▁··▂▃▃▅▇█▇▃▃ beats 182/246	hard · avg 22%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	82.5% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 186/246	easy · avg 71%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	77.6% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 191/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	80.4% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 181/246	easy · avg 66%	▇ 0.8%	at its level · noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	82.2% ✎	▁···▁▁▁▁▃▆█▃ beats 80/246	easy · avg 84%	▇ 0.7%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #143 of 247 · 9.3% of its score · 6 tests

Beats OneJev1.6-4B, Nace.AI Drex v1.5, Jegue 0.1 (9B) here, though they rank higher overall. Trails Crivo-32M v2, Crivo-68M, Crivo-150M v2 here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: New Yorker (accuracy) submitted	71.6% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 164/246	medium · avg 61%	▇▇ 4.0%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	61.8% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 143/246	medium · avg 60%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	83.5% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 104/246	easy · avg 83%	▇ 1.2%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	57.6% ✎	▁········▁▇█ beats 74/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	5.8% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 80/237	hard · avg 21%	▇ 0.9%	sharp · at its level · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	42.7% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 102/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #132 of 263 · 45% of its score · 14 tests

Beats Winnow-12B (DecisionBench b6ac22b0), Cerno 4B v1.1, Amber Hybrid 4B (decision readout) here, though they rank higher overall. Trails djev diffgemma (decision readout), Kev 9B v2, Bespoke Nimble 9B v2 here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	54.3% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 129/246	medium · avg 46%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	76.1% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 127/246	easy · avg 70%	▇▇▇ 7.1%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	71.2% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 139/246	easy · avg 66%	▇▇▇ 6.4%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	93.2% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 111/246	very easy · avg 90%	▇▇ 4.0%	sharp · maxed out
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	41.8% ✎	▁··▄█▆▆▆▅▅▃▁ beats 118/246	medium · avg 45%	▇ 3.5%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	78.8% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 122/246	easy · avg 78%	▇ 3.2%	typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	38.8% ✎	▂▆▅▅▆█▆▃▁··▁ beats 106/246	medium · avg 39%	▇ 3.1%	blunt · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.2% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 134/246	very easy · avg 96%	▇ 2.1%	sharp · maxed out · noisy

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	27.5% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 119/246	hard · avg 22%	▇ 1.4%	at its level
Decision Index 0.4.1: MuSR (accuracy) submitted	55.5% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 73/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	53.2% ✎	██▄▃▃▄▁▁···▁ beats 122/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	67.7% ✎	▁███▆▅▃▃▁▁▁▁ beats 117/246	easy · avg 71%	▇ 1.0%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	13.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 96/246	hard · avg 16%	▇ 0.6%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	11.2% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 67/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 8.2% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	45.7000 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 124/247	avg 41.206 points	▇▇ 5.3%	blunt
Decision Index 0.4.1: private new domains (equated points)	41.1900 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 134/247	avg 38.861 points	▇ 2.9%	blunt

$ where to run it

Download model files ↗
Published model files verified. This is a verified repository snapshot; the benchmark does not establish an identical weight revision. A local run command and hardware requirements have not been verified for this entry. Source · checked 2026-10-31.

Model repository and documentation: huggingface.co/everettjf ↗

$ how the score was worked out · Decision Index 0.4.1 99%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

source	results	share of the score	licence
Decision Index 0.4.1 · submitted; independent execution unverified	42	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%	CC BY 4.0
prior · pull toward the middle		▇ 1%
weighted average		= 54.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.9%	failed run: Publisher excludes this protocol because its prompt reveals GPT-4 as the best route. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json
Decision Index 0.4.1	Decision Index 0.4.1: SGD/SGD-X (macro-F1) [correct]	41.0%	failed run: Publisher reports a builder bug exposing dialogue turns after the classified turn; awaiting corrected rerun. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated 2026-11-03 02:37 UTC · sources and licences · json