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jeff GLiFormer-large (decision readout)

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jeff GLiFormer-large (decision readout) is a source-available decision implementation from logan-markewich. Its published size is 0.58B parameters.

Catalogue identity notes

Exact Decision Index tested engine jeff-uncapped; reported model jeff GLiFormer-large. Type: inference technique; technique: GLiFormer. Published checkpoint/base reference: knowledgator/gliformer-large-v1. Implementation: https://github.com/logan-markewich/jeff. Comparison entry; no current run command, API price or speed inferred from the benchmark.

Model information sources

· Parameter count ↗

Publisher parameter count (estimated by publisher); includes the served backbone, not just adapter size.

20.5±1.1#340 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of jeff GLiFormer-large (decision readout), out of 357 with a score.

#	model	score	where	RAM
340	Laya ConvAI Innovations	21±1	model files
341	Verdict heman10x	21±1	model files
342	jeff GLiFormer-large (decision readout) logan-markewich	21±1	source code
343	CLM-v0.1-8B Qwen3-8B heads Contrastive-LM	20±1	local	~15GB
344	gliner2.5-base-v1 (DecisionBench 1a8bc24e) Fastino	20±2	model files

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

$ what it's good at

tools & automation▃▃▁▁▁▂▃▄▆██▆ #250 of 250 · 10% of its score · 6 tests

Trails Zev (decision readout), LFM2.5-350M-RLCD, Nirnaya 0.4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	21.5% ✎	▃▂▁▁▁▂▂▄▅▆█▁ beats 0/246	very easy · avg 92%	▇▇▇ 7.0%	sharp · at its level · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	3.1% ✎	▁▁▃▁▁▁▁▂▃▂█▂ beats 9/236	easy · avg 70%	▇ 1.6%	sharp · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	23.3% ✎	▁▁▃▂▃▅▅▆█▇█▃ beats 9/246	easy · avg 72%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	17.6% ✎	▁▁▃▁▁·▁▁▁▄█▁ beats 9/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	0.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 29/243	medium · avg 37%	▇ 0.1%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	53.8% ✎	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▁▁▁·▃▁▁▁▃▃█▅ #247 of 258 · 4.5% of its score · 7 tests

Beats DM-JEPA 1.1, JevK5 Lite, Qwen-2.5-1B-RLCD (decision readout) here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	21.3% ✎	▁▁▁▁▁▃▂▂▃▃█▅ beats 10/246	medium · avg 47%	▇ 1.2%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	8.1% ✎	▃▁▁▁▁▁▁▂▂▄▇█ beats 0/246	medium · avg 38%	▇ 1.2%	blunt
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	44.3% ✎	▁▁▁▁▁▁▃▂▃▆█▄ beats 23/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	56.8% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 33/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.2% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 34/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	61.0% ✎	▁·▁▁▁▁▁▁▃█▇█ beats 25/238	easy · avg 79%	▇ 0.4%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	44.8% ✎	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▂▃▂▃▂▅▆▃█▆▄▃ #244 of 256 · 23% of its score · 9 tests

Beats Crivo-32M v2, Bosun v3.1 0.6B LoRA, Crivo-17M here, though they rank higher overall. Trails CLM-v0.1-8B Qwen3-8B heads here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: HellaSwag (accuracy) submitted	34.5% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 12/246	very easy · avg 85%	▇▇▇▇ 9.1%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	55.0% ✎	▁▁▁▂▃▃▃▄▇▇█▄ beats 26/246	easy · avg 74%	▇▇ 5.7%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	29.3% ✎	▁▃▃▅▅█▂····▁ beats 13/246	medium · avg 60%	▇ 3.5%	at its level
Decision Index 0.4.1: FinEntity (macro-F1) submitted	74.9% ✎	▁···▁▁▁▁▃▆█▃ beats 57/246	easy · avg 84%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	21.3% ✎	▁▁▁▃▁▁▁▂▂▇▇█ beats 8/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	28.3% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 49/246	medium · avg 44%	▇ 0.9%	typical
Decision Index 0.4.1: VAST (macro-F1) submitted	40.5% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 32/246	medium · avg 62%	▇ 0.7%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	17.1% ✎	▁▁▁▁▁▁▃▁▁▃▆█ beats 20/246	easy · avg 66%	▇ 0.6%	at its level · noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ACOS (per-review F1) submitted	3.2% ✎	▁··▃▃▃▅▇█▇▃▃ beats 12/246	hard · avg 22%	▇ 0.2%	too hard for it · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #241 of 247 · 7.1% of its score · 6 tests

Beats JEV Ettin 150M, JEV FLAN-T5 Large public v2 (342M), Kev 0.8B r15 here, though they rank higher overall. Trails Zev (decision readout), LFM2.5-350M-RLCD, Nirnaya 0.4B 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	18.8% ✎	▃▁▂▃▃▃▇▅█▅▃▁ beats 2/246	medium · avg 61%	▇ 2.7%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	48.1% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 6/246	easy · avg 83%	▇ 1.9%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	46.7% ✎	▁·▃▃▂▂▅█▄▂▁▁ beats 3/246	medium · avg 60%	▇ 1.5%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	50.4% ✎	▁········▁▇█ beats 22/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	32.5% ✎	▁▃▄█▇▅▁▁▄▄▁▁ beats 8/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	2.4% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 52/237	hard · avg 21%	▇ 0.0%	sharp · too hard for it · noisy

knowledge & reasoning▁▃▂▂▂▃▃▅▅▆█▂ #252 of 263 · 49% of its score · 14 tests

Beats Crivo-32M v2, Crivo-17M, Prumo (rule-based engine) here, though they rank higher overall. Trails CLM-v0.1-8B Qwen3-8B heads here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	50.6% ✎	▁▁▃▁▂▂▂▂▃▆█▅ beats 11/246	very easy · avg 96%	▇▇▇▇▇▇ 15%	sharp · at its level · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	31.7% ✎	▂▃▂▂▂▂▁▃▄▆█▆ beats 10/246	very easy · avg 90%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	31.6% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 12/246	easy · avg 70%	▇▇ 5.8%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	33.5% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 9/246	easy · avg 66%	▇▇ 4.7%	at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	50.4% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 5/246	easy · avg 78%	▇▇ 3.8%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	14.2% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 17/246	medium · avg 46%	▇ 3.3%	sharp
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	24.5% ✎	▂▆▅▅▆█▆▃▁··▁ beats 15/246	medium · avg 39%	▇ 1.6%	blunt
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	18.6% ✎	▁··▄█▆▆▆▅▅▃▁ beats 22/246	medium · avg 45%	▇ 1.0%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	47.9% ✎	▁▁▃█▅▄▁▁▁▁▁▁ beats 25/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	49.6% ✎	▁███▆▅▃▃▁▁▁▁ beats 2/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	39.0% ✎	██▄▃▃▄▁▁···▁ beats 47/246	medium · avg 58%	▇ 0.7%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	1.6% ✎	▁▁▃▁▁▁▁▂▅█▂▁ beats 21/246	hard · avg 22%	▇ 0.3%	too hard for it
Decision Index 0.4.1: ChessBench (accuracy) submitted	8.6% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 39/246	hard · avg 16%	▇ 0.2%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	16.6% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 224/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 6.0% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	5.8800 points ✎	▃▂▂▃▃▃▄▅▅▇█▃ beats 10/247	avg 41.206 points	▇▇ 3.9%	blunt
Decision Index 0.4.1: private new domains (equated points)	6.7000 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 5/247	avg 38.861 points	▇ 2.1%	blunt

$ where to run it

Implementation and setup instructions ↗
Published implementation and setup documentation verified. This entry describes a decision readout or evaluated configuration, not necessarily separate trained weights. Follow the referenced version and model settings; hardware compatibility has not been verified. Source · checked 2026-10-31.

Model repository and documentation: github.com/logan-markewich ↗

$ how the score was worked out · Decision Index 0.4.1 100%

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	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%	CC BY 4.0
prior · pull toward the middle		▇ 0%
weighted average		= 20.5

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	53.8%	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]	44.8%	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