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jdi-ptr14-0.8b

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jdi-ptr14-0.8b is an open-weights decision model from AnamK. Its published size is 0.75B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Exact Decision Index tested engine jdi-ptr14-0.8b; reported model jdi-ptr14-0.8b. Type: full fine-tune; 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.

39.5±1.1#264 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of jdi-ptr14-0.8b, out of 357 with a score.

#	model	score	where
264	Decider 2B Mapika	41±1	model files
265	FLock this-that 1.2 FLock.io	40±1	model files
266	jdi-ptr14-0.8b AnamK	40±1	model files
267	Saina Helm 2.0 v2 Saina	39±1	model files
268	Valen-2B Valen	39±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #176 of 250 · 9.1% of its score · 6 tests

Beats LiquidAI d1-3B, Decider 4B, JevK5 v0.3 here, though they rank higher overall. Trails JevK5 Lite, Mola-150M, Gevva E2B 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	90.0% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 73/246	very easy · avg 92%	▇▇ 4.3%	sharp · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	47.2% ✎	▁▁▁▁▁▁▁▃▃▂█▂ beats 44/236	easy · avg 70%	▇ 2.4%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	79.9% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 139/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	60.1% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 65/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	0.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 7/243	medium · avg 37%	▇ 0.5%	sharp · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	61.0% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #145 of 258 · 5.1% of its score · 7 tests

Beats Quyet-1.0-Large, Decider chat · Qwen3.6-27B (decision readout), Sieve 9B v2 here, though they rank higher overall. Trails Amber 0.4B 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	33.9% ✎	▁▁▁▁▁▁▁▂▃▄▇█ beats 65/246	medium · avg 38%	▇ 1.6%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	51.2% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 111/246	medium · avg 47%	▇ 1.6%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	79.3% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 139/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	92.8% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 173/238	easy · avg 83%	▇ 0.5%	sharp · at its level · noisy
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.5% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 37/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	89.3% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 170/238	easy · avg 79%	▇ 0.3%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	38.5% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #172 of 256 · 24% of its score · 9 tests

Beats Nari Decision 4B v3.2, Kev 4B r10, StartLux-Decision v1.1 0.8B here, though they rank higher overall. Trails DecisionTune 1.0 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	80.8% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 85/246	very easy · avg 85%	▇▇▇▇ 8.9%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	62.1% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 48/246	easy · avg 74%	▇▇ 5.3%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	48.4% ✎	▁▂▃▅▅█▂····▁ beats 60/246	medium · avg 60%	▇▇ 4.4%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	11.9% ✎	▁·▁·▁▃▂▄▅█▇▄ beats 9/246	medium · avg 44%	▇ 1.5%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	66.4% ✎	▁▁▁▁▁▁▁▂▃▇▇█ beats 66/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: FinEntity (macro-F1) submitted	78.7% ✎	▁···▁▁▁▁▃▆█▃ beats 65/246	easy · avg 84%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	74.3% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 165/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	73.9% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 121/246	easy · avg 66%	▇ 0.7%	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	11.0% ✎	▁··▂▃▃▅▇█▇▃▃ beats 64/246	hard · avg 22%	▇ 0.5%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #212 of 247 · 7.8% of its score · 6 tests

Beats Valen-4B, Saracura PT-BR 4B, TokenRhythm NeoHorse-Jev-4B here, though they rank higher overall. Trails LFM2.5-350M-RLCD, system-one-gemma, Qwen-2.5-1B-RLCD (decision readout) 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	58.7% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 93/246	medium · avg 61%	▇ 3.4%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	52.9% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 35/246	easy · avg 83%	▇ 1.6%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	57.7% ✎	▁·▁▃▂▃▅█▄▂▁▁ beats 73/246	medium · avg 60%	▇ 1.5%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	58.8% ✎	▁········▁▇█ beats 92/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	3.6% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 65/237	hard · avg 21%	▇ 0.3%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	39.9% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 61/246	medium · avg 47%	▇ 0.2%	blunt · at its level

knowledge & reasoning▁▂▂▂▃▃▃▅▅▆█▂ #206 of 263 · 47% of its score · 14 tests

Beats Crivo-1B, vLLM-SR vela3-fast (Vela 3.0, 0.4B), Decision-1.0-Eos-0.8B (DecisionBench 3c2d6326) here, though they rank higher overall. Trails laya (DecisionBench 1c5edc17), DM-JEPA 1.1, Strands Decider 2B (Hobson v21) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	73.3% ✎	▂▂▂▂▂▃▁▃▄▆█▆ beats 59/246	very easy · avg 90%	▇▇▇▇ 8.8%	sharp
Decision Index 0.4.1: MMLU (accuracy) submitted	49.5% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 53/246	easy · avg 70%	▇▇▇ 7.2%	sharp · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	87.7% ✎	▁▁▂▁▂▃▂▂▃▆█▅ beats 61/246	very easy · avg 96%	▇▇▇ 6.9%	sharp · noisy
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	25.4% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 55/246	medium · avg 46%	▇▇▇ 6.7%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	45.2% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 51/246	easy · avg 66%	▇▇ 5.9%	at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	66.8% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 64/246	easy · avg 78%	▇ 3.5%	at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	35.1% ✎	▁··▄█▆▆▆▅▅▃▁ beats 93/246	medium · avg 45%	▇ 2.2%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	31.6% ✎	▂▆▅▅▆█▆▃▁··▁ beats 63/246	medium · avg 39%	▇ 2.1%	blunt · at its level

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	57.6% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 100/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	54.0% ✎	▁███▆▅▃▃▁▁▁▁ beats 33/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	35.3% ✎	██▄▃▃▄▁▁···▁ beats 26/246	medium · avg 58%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	4.8% ✎	▁▁▁▁▃▁▁▂▅█▂▁ beats 33/246	hard · avg 22%	▇ 0.7%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	9.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 54/246	hard · avg 16%	▇ 0.4%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	11.8% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 97/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 6.2% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	30.5800 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 67/247	avg 41.206 points	▇▇ 4.0%	blunt
Decision Index 0.4.1: private new domains (equated points)	13.0600 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 41/247	avg 38.861 points	▇ 2.2%	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/AnamK ↗

$ 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		= 39.5

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	61.0%	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]	38.5%	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