Crivo-17M

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Crivo-17M is an open-weights decision model from tardellirs. Its published size is 0.02B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Exact Decision Index tested engine crivo-17m; reported model Crivo-17M. Type: full fine-tune; technique: encoder. 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.

23.8±1.0#331 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Crivo-17M, out of 357 with a score, then more from tardellirs.

#	model	score	where	RAM
331	laya (DecisionBench 1c5edc17) ConvAI Innovations	25±1	local	~1.7GB
332	kev-0.5b (DecisionBench 9ce2fd39) Jared Palmer	24±2	model files
333	Crivo-17M tardellirs	24±1	model files
334	Bekko System One v0 68M hotchpotch	24±1	model files
335	MoganAI Lavoir ModernBERT-large Mogan AI	24±1	model files
	more from tardellirs
253	Crivo-1B tardellirs	43±1	model files
279	Crivo-400M tardellirs	37±1	model files
301	Crivo-150M v2 tardellirs	32±1	model files

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

$ what it's good at

tools & automation▁▃▁▃▁▂▃▄▆██▆ #222 of 250 · 10% of its score · 6 tests

Beats DM-JEPA 1.1, Kodiak v0.2 1B (accuracy), JEV FLAN-T5 Large public v2 (342M) here, though they rank higher overall. Trails Prumo (rule-based engine) 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	61.2% ✎	▁▂▁▃▁▂▂▄▅▆█▁ beats 27/246	very easy · avg 92%	▇▇▇ 6.7%	sharp · at its level · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	50.0% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 50/236	easy · avg 70%	▇ 1.7%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	53.6% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 50/246	easy · avg 72%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	47.6% ✎	▁▁▁▁▁·▁▃▁▄█▁ beats 27/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	3.4% ✎	█▁▃▃▄▄▆▄▁▂▁▃ beats 55/243	medium · avg 37%	▇ 0.1%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	53.1% ✎	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▁▁▁·▁▃▁▁▃▃█▅ #237 of 258 · 4.5% of its score · 7 tests

Beats Sieve 2B, DM-JEPA 1.1, Together Tev1 Qwen3.5-0.8B here, though they rank higher overall. Trails MoganAI Lavoir ModernBERT-large, Bekko System One v0 68M here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	25.8% ✎	▁▁▁▁▁▁▃▂▃▃█▅ beats 20/246	medium · avg 47%	▇ 1.2%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	23.4% ✎	▁▁▁▁▁▃▁▂▂▄▇█ beats 28/246	medium · avg 38%	▇ 1.2%	blunt
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	79.7% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 65/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	67.5% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 88/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	58.1% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 80/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	79.6% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 92/238	easy · avg 79%	▇ 0.4%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	38.6% ✎	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▃▂▂▃▂▅▆▃█▆▄▃ #249 of 256 · 23% of its score · 9 tests

Trails CLM-v0.1-8B Qwen3-8B heads, jeff GLiFormer-large (decision readout), Verdict 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	30.6% ✎	▃▄▂▂▄▇▃▇█▅▃▁ beats 7/246	very easy · avg 85%	▇▇▇▇ 9.4%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	49.6% ✎	▁▁▁▃▂▃▃▄▇▇█▄ beats 18/246	easy · avg 74%	▇▇ 5.4%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	29.3% ✎	▁▃▃▅▅█▂····▁ beats 12/246	medium · avg 60%	▇ 3.5%	at its level
Decision Index 0.4.1: FinEntity (macro-F1) submitted	67.5% ✎	▁···▁▁▁▁▃▆█▃ beats 37/246	easy · avg 84%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	0.0% ✎	▃·▁·▁▁▂▄▅█▇▄ beats 0/246	medium · avg 44%	▇ 1.0%	typical
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	71.6% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 83/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	59.8% ✎	▁▁▁▂▅▄▄▃▄▆█▄ beats 107/246	medium · avg 62%	▇ 0.7%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	65.2% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 78/246	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	10.8% ✎	▁··▂▃▃▅▇█▇▃▃ beats 63/246	hard · avg 22%	▇ 0.2%	too hard for it · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #158 of 247 · 7.0% of its score · 6 tests

Beats Jegue 0.1 (9B), Standard One 8B SH (v4), Sieve 4B here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: New Yorker (accuracy) submitted	41.5% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 39/246	medium · avg 61%	▇ 2.7%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	80.8% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 89/246	easy · avg 83%	▇ 1.9%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	51.2% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 31/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	50.8% ✎	▁········▁▇█ beats 25/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	36.4% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 26/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	1.8% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 42/237	hard · avg 21%	▇ 0.1%	sharp · too hard for it · noisy

knowledge & reasoning▃▂▂▂▂▃▃▅▅▆█▂ #259 of 263 · 49% of its score · 14 tests

Trails Supersonic Labs Julia 1, system-one-gemma, Bekko System One v0 17M 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	41.6% ✎	▁▃▂▁▂▂▂▂▃▆█▅ beats 5/246	very easy · avg 96%	▇▇▇▇▇▇ 15%	sharp · at its level · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	25.3% ✎	▃▂▂▂▂▂▁▃▄▆█▆ beats 3/246	very easy · avg 90%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	28.2% ✎	▃▄▄▃▃▂▅▇▅█▇▁ beats 4/246	easy · avg 70%	▇▇ 5.9%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	32.4% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 6/246	easy · avg 66%	▇▇ 4.8%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	11.0% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 0/246	medium · avg 46%	▇ 3.7%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	52.2% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 20/246	easy · avg 78%	▇ 3.7%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	21.9% ✎	▃▆▅▅▆█▆▃▁··▁ beats 4/246	medium · avg 39%	▇ 1.6%	blunt
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	18.2% ✎	▁··▄█▆▆▆▅▅▃▁ beats 16/246	medium · avg 45%	▇ 1.1%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	37.9% ✎	▃▁▂█▅▄▁▁▁▁▁▁ beats 6/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	50.0% ✎	▁███▆▅▃▃▁▁▁▁ beats 9/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	40.2% ✎	██▄▃▃▄▁▁···▁ beats 55/246	medium · avg 58%	▇ 0.7%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	0.2% ✎	▃▁▁▁▁▁▁▂▅█▂▁ beats 6/246	hard · avg 22%	▇ 0.4%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	7.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 12/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	14.4% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 182/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 5.7% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	15.0100 points ✎	▂▂▃▃▃▃▄▅▅▇█▃ beats 27/247	avg 41.206 points	▇ 3.7%	blunt
Decision Index 0.4.1: private new domains (equated points)	6.6100 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 4/247	avg 38.861 points	▇ 2.0%	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/tardellirs ↗

$ 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		= 23.8

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	53.1%	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]	38.6%	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