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Bosun v3.1 1.7B LoRA

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Bosun v3.1 1.7B LoRA is an open-weights decision model from Hanno Labs. Its published size is 1.72B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine bosun-v3.1-1.7b; reported model Bosun v3.1 1.7B LoRA. Type: LoRA + head; technique: autoregressive. Implementation: https://github.com/Hanno-Labs/jev-compatible-server. 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.

29.0±1.0#314 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Bosun v3.1 1.7B LoRA, out of 357 with a score, then more from Hanno Labs.

#	model	score	where	RAM	released
314	LiquidAI d1-omni-600M Liquid AI	30±1	local	~2.7GB
315	laya-typed-decisions (DecisionBench bc76315b) ConvAI Innovations	29±2	model files
316	Bosun v3.1 1.7B LoRA Hanno Labs	29±1	model files
317	vLLM-SR Decision 2.0 Kai 0.6B vLLM Semantic Router	29±1	local	~3.7GB
318	Mola-68M codavidgarcia	29±1	model files		2026-10-14
	more from Hanno Labs
42	bosun-v3.1-1.7b (DecisionBench 2ef2d972) Hanno Labs	67±2	model files
65	bosun-v3.1-0.6b (DecisionBench aaa9dd06) Hanno Labs	64±2	model files		2026-09-22
330	Bosun v3.1 0.6B LoRA Hanno Labs	25±1	model files		2026-09-22

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

$ what it's good at

tools & automation▁▃▁▃▁▂▃▄▆██▆ #223 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), Crivo-17M, JevK5 Lite 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	60.2% ✎	▁▂▁▃▁▂▂▄▅▆█▁ beats 26/246	very easy · avg 92%	▇▇ 6.1%	sharp · at its level · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	60.8% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 74/236	easy · avg 70%	▇ 1.9%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	32.4% ✎	▁▁▁▃▃▅▅▆█▇█▃ beats 17/246	easy · avg 72%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	57.0% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 48/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	0.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 40/243	medium · avg 37%	▇ 0.2%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	68.3% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #164 of 258 · 4.5% of its score · 7 tests

Beats Solar Decide, decisio 0.4.0 (Qwen3.6-35B-A3B) (decision readout), JevK5 9B v0.3.3 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	47.8% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 94/246	medium · avg 47%	▇ 1.3%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	32.9% ✎	▁▁▁▁▁▁▁▂▃▄▇█ beats 60/246	medium · avg 38%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	90.9% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 141/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	53.6% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 20/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.2% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 32/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	85.6% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 137/238	easy · avg 79%	▇ 0.4%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	54.7% ✎	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▂▃▂▃▂▅▆▃█▆▄▃ #242 of 256 · 23% of its score · 9 tests

Trails CLM-v0.1-8B Qwen3-8B heads, Qwen-2.5-1B-RLCD (decision readout), MoganAI Lavoir ModernBERT-large 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	34.9% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 13/246	very easy · avg 85%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	53.2% ✎	▁▁▁▂▃▃▃▄▇▇█▄ beats 23/246	easy · avg 74%	▇▇ 5.3%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	40.5% ✎	▁▂▃▅▅█▂····▁ beats 37/246	medium · avg 60%	▇ 3.7%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	15.4% ✎	▁·▁·▁▁▃▄▅█▇▄ beats 14/246	medium · avg 44%	▇ 1.1%	typical
Decision Index 0.4.1: FinEntity (macro-F1) submitted	72.3% ✎	▁···▁▁▁▁▃▆█▃ beats 48/246	easy · avg 84%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	44.4% ✎	▁▁▁▁▁▁▃▂▂▇▇█ beats 27/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	36.4% ✎	▁▁▁▃▅▄▄▂▄▆█▄ beats 19/246	medium · avg 62%	▇ 0.7%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	22.1% ✎	▁▁▁▁▁▁▃▁▁▃▆█ beats 23/246	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	8.8% ✎	▁··▂▃▃▅▇█▇▃▃ beats 46/246	hard · avg 22%	▇ 0.3%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #207 of 247 · 7.1% 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	36.0% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 28/246	medium · avg 61%	▇ 2.9%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	55.2% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 40/246	easy · avg 83%	▇ 1.8%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	53.8% ✎	▁·▁▃▃▂▅█▄▂▁▁ beats 49/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	55.5% ✎	▁········▁▇█ beats 55/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	36.1% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 21/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	1.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 33/237	hard · avg 21%	▇ 0.1%	sharp · too hard for it · noisy

knowledge & reasoning▁▂▃▂▂▃▃▅▅▆█▂ #230 of 263 · 49% of its score · 14 tests

Beats Decision-1.0-Eos-0.8B (DecisionBench 3c2d6326), Amber 0.4B, vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B) here, though they rank higher overall. Trails laya (DecisionBench 1c5edc17), LFM2.5-2.6B-RLCD, Jev-Style 0.8B v3 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	66.5% ✎	▁▁▂▃▂▂▂▂▃▆█▅ beats 27/246	very easy · avg 96%	▇▇▇▇▇ 13%	sharp · at its level · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	53.6% ✎	▂▂▂▃▂▂▁▃▄▆█▆ beats 33/246	very easy · avg 90%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	42.4% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 41/246	easy · avg 70%	▇▇▇ 6.4%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	36.8% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 23/246	easy · avg 66%	▇▇ 5.1%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	24.6% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 50/246	medium · avg 46%	▇▇ 4.5%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	51.8% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 18/246	easy · avg 78%	▇ 3.6%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	25.0% ✎	▂▆▅▅▆█▆▃▁··▁ beats 18/246	medium · avg 39%	▇ 1.7%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	18.9% ✎	▁··▄█▆▆▆▅▅▃▁ beats 25/246	medium · avg 45%	▇ 1.4%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	41.8% ✎	▁▃▂█▅▄▁▁▁▁▁▁ beats 15/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	53.2% ✎	▁███▆▅▃▃▁▁▁▁ beats 28/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	40.2% ✎	██▄▃▃▄▁▁···▁ beats 59/246	medium · avg 58%	▇ 0.7%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	3.9% ✎	▁▁▁▁▃▁▁▂▅█▂▁ beats 30/246	hard · avg 22%	▇ 0.4%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	9.1% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 46/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	13.4% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 158/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 5.6% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	19.6000 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 36/247	avg 41.206 points	▇ 3.6%	blunt
Decision Index 0.4.1: private new domains (equated points)	11.9500 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 33/247	avg 38.861 points	▇ 2.0%	blunt

$ where to run it

Download model files ↗
Published model files verified. Includes adapters or readout weights; follow the model documentation for the required base model. 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/Hanno-Labs ↗

$ 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		= 29.0

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	68.3%	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]	54.7%	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