whatmodel.sh / decision / Jeffan-31b-1710

Jeffan-31b-1710 is an open-weights decision model from tattrongvu. Its published size is 31.27B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine jeffan-31b-1710; reported model Jeffan-31b-1710. 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.

75.8±1.6#1 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Jeffan-31b-1710, out of 357 with a score, then more from tattrongvu.

#modelscorewhereRAMreleased
1Jeffan-31b-1710 tattrongvu76±2model files
2baselabs mammoth-1 (27B) baselabs75±1model files2026-10-21
3Sieve 31B v3 Sthānika AI74±2model files2026-10-27
4Fastino GLiDE 1.2 Fastino73±1model files2026-10-22
5KnowLine 31B Gen1 PelaAI73±2model files2026-10-24
more from tattrongvu
64Jeffan-4B-2010 tattrongvu64±1model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #40 of 250 · 4.5% of its score · 6 tests

Trails baselabs joey-1 (2B), Solomon v1.1, Clef-flash here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted75.6% ✎▁▁▁▁▁·▁▁▁▄█▃ beats 246/246medium · avg 60%▇ 1.3%blunt
Decision Index 0.4.1: API-Bank (accuracy) submitted88.0% ✎▁▁▁▁▁▁▁▂▃▂█▃ beats 219/236easy · avg 70%▇ 1.3%sharp · maxed out · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted92.1% ✎▁▁▁▂▃▅▅▆█▇█▃ beats 239/246easy · avg 72%▇ 0.8%noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted100.0% ✎█▁▁▃▄▄▆▄▁▂▁▃ beats 234/243medium · avg 37%▇ 0.6%sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted97.5% ✎▁▂▁▁▁▂▂▄▅▆█▁ beats 204/246very easy · avg 92%▇ 0.5%sharp · maxed out · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted80.0% ✎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▁▁▁·▁▁▁▁▃▃█▅ #5 of 258 · 8.9% of its score · 7 tests

Trails WeMM-JEV-27B, StartLux-Decision v1.1 27B, Sieve 31B v3 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted47.9% ✎▁▁▁▁▁▁▁▂▂▄▇█ beats 197/246medium · avg 38%▇▇ 3.9%blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted63.6% ✎▁▁▁▁▁▁▂▂▃▃█▅ beats 242/246medium · avg 47%▇ 3.2%at its level
Decision Index 0.4.1: HoVer (accuracy) submitted88.6% ✎▆▆▇▅▄▄▃▇▄█▆▄ beats 224/246easy · avg 74%▇ 0.7%blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted84.9% ✎█▅▅▄▃▂▁▁▁▁·▁ beats 190/245easy · avg 70%▇ 0.6%noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted91.9% ✎▁·▁▁▁▁▁▁▂█▇█ beats 211/238easy · avg 79%▇ 0.3%noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted96.4% ✎▁▁▁▁▁▁▁▂▃▆█▄ beats 225/238easy · avg 83%▇ 0.2%sharp · maxed out · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted66.1% ✎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▂▂▂▃▂▅▆▃█▆▄▃ #10 of 256 · 21% of its score · 9 tests

Trails Clef-omni, vLLM-SR d3 (27B), AutoTrust-JEV-27B v1.1 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: ANLI (macro-F1) submitted82.2% ✎▁▂▃▅▅█▃····▁ beats 244/246medium · avg 60%▇▇ 5.4%typical
Decision Index 0.4.1: NLI4CT (macro-F1) submitted87.8% ✎▁▁▁▂▂▃▃▄▇▇█▄ beats 244/246easy · avg 74%▇▇ 4.9%typical
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted68.8% ✎▁·▁·▁▁▂▄▅█▇▄ beats 227/246medium · avg 44%▇ 3.0%at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted63.4% ✎▁··▂▃▃▅▇█▇▃▃ beats 238/246hard · avg 22%▇ 2.4%at its level · noisy
Decision Index 0.4.1: HellaSwag (accuracy) submitted97.7% ✎▂▄▂▂▄▇▃▇█▅▃▁ beats 233/246very easy · avg 85%▇ 1.8%sharp · maxed out
Decision Index 0.4.1: VAST (macro-F1) submitted84.2% ✎▁▁▁▂▅▄▄▂▄▆█▄ beats 246/246medium · avg 62%▇ 1.1%typical
Decision Index 0.4.1: ContractNLI (macro-F1) submitted84.1% ✎▁▁▁▁▁▁▁▂▂▇▇█ beats 210/246easy · avg 71%▇ 0.9%noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted84.2% ✎▁▁▁▁▁▁▁▁▁▃▆█ beats 225/246easy · avg 66%▇ 0.7%noisy
1 more test
testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: FinEntity (macro-F1) submitted94.2% ✎▁···▁▁▁▁▃▆█▃ beats 217/246easy · avg 84%▇ 0.4%maxed out · noisy
arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #51 of 247 · 11% of its score · 6 tests

Trails Kas 4B, baselabs joey-1 (2B), Clef-flash here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: New Yorker (accuracy) submitted88.3% ✎▁▁▂▃▃▃▇▅█▅▃▃ beats 246/246medium · avg 61%▇▇ 3.9%typical
Decision Index 0.4.1: Humicroedit (accuracy) submitted72.6% ✎▁·▁▃▂▂▅█▄▂▁▃ beats 245/246medium · avg 60%▇ 2.8%blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted66.7% ✎▁········▁▇█ beats 223/246medium · avg 60%▇ 1.6%blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted82.2% ✎▄▅▅▃▆▅▃▅▂▅▃█ beats 230/237hard · avg 21%▇ 1.3%sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted95.1% ✎▁█▄▄▅█▆▅▄▁▁▁ beats 192/246easy · avg 83%▇ 0.6%sharp · maxed out · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted63.0% ✎▁▂▄█▇▅▁▁▄▄▁▁ beats 229/246medium · avg 47%▇ 0.5%blunt · at its level
knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▃ #4 of 263 · 41% of its score · 14 tests

Trails Jev 1.13, Perplexity Decider v1.1 (27B), baselabs mammoth-1 (27B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted79.0% ✎▄▄▃▃▂▄▅▇██▅▃ beats 244/246medium · avg 46%▇▇▇▇ 11%sharp
Decision Index 0.4.1: BBH (accuracy) submitted93.0% ✎▄▆▄▄█▇▇▄▃▁▃▁ beats 242/246easy · avg 66%▇▇ 5.2%typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted53.1% ✎▂▆▅▅▆█▆▃▁··▁ beats 222/246medium · avg 39%▇▇ 5.1%blunt · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted89.5% ✎▁▄▄▃▃▂▅▇▅█▇▁ beats 237/246easy · avg 70%▇▇ 4.7%sharp
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted92.3% ✎▁··▄█▆▆▆▅▅▃▃ beats 245/246medium · avg 45%▇ 3.4%sharp
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted44.0% ✎▁▁▁▁▁▁▁▂▅█▃▁ beats 235/246hard · avg 22%▇ 2.9%at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted95.3% ✎█▅█▅▄█▆▄▃▃▁▁ beats 239/246easy · avg 78%▇ 2.3%maxed out
Decision Index 0.4.1: MuSR (accuracy) submitted82.5% ✎▁▁▂█▅▄▁▁▃▁▁▁ beats 235/246medium · avg 60%▇ 1.6%blunt · at its level
6 more tests
testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: ChessBench (accuracy) submitted29.7% ✎▄▇▄▄▄▆▄█▄▃▁▁ beats 237/246hard · avg 16%▇ 1.4%blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted90.2% ✎██▄▃▃▄▃▁···▁ beats 240/246medium · avg 58%▇ 1.2%noisy
Decision Index 0.4.1: CLadder (accuracy) submitted95.5% ✎▁███▆▅▃▃▁▁▃▁ beats 241/246easy · avg 71%▇ 1.0%typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted98.0% ✎▂▂▂▂▂▂▁▃▄▆█▆ beats 245/246very easy · avg 90%▇ 0.9%sharp · maxed out
Decision Index 0.4.1: ARC-Easy (accuracy) submitted99.4% ✎▁▁▂▁▂▂▂▂▃▆█▅ beats 243/246very easy · avg 96%▇ 0.3%sharp · maxed out · noisy
Decision Index 0.4.1: HLE (accuracy) submitted16.0% ✎▁▁▂▃▆█▇▅▅▃▁▁ beats 217/246very hard · avg 13%▇ 0.0%blunt
other tests2 not in a task · 13% of its score
testresultvs the others in its rundifficultycountswhy
Decision Index 0.4.1: private same skills (equated points)65.8600 points ✎▂▂▂▃▃▃▄▅▅▇█▃ beats 242/247avg 41.206 points▇▇▇ 8.6%blunt
Decision Index 0.4.1: private new domains (equated points)75.5800 points ✎▃█▃▃▂▆▃▃▄▄▄▄ beats 241/247avg 38.861 points▇▇ 4.8%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/tattrongvu ↗

$ 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

sourceresultsshare of the scorelicence
Decision Index 0.4.1 · submitted; independent execution unverified42CC BY 4.0
prior · pull toward the middle
weighted average= 75.8

not counted (2)

Decision Index 0.4.1Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]80.0%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 0.4.1Decision Index 0.4.1: SGD/SGD-X (macro-F1) [correct]66.1%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

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 · sources and licences · json