Models › kev family

jaredpalmer Apache 2.0 out 37 days ago

Text in, labels out.

Verified 2026-10-26 07:27 UTC Rank #0 in Decision models · score 0 method v1 Sources 3 Downloads 30d 20k · #458 of 619 tracked

Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-26
Do people use it?20kdownloads, 30 days#458 of 619 we track · below the median+19 likes, downloads −23% this week159 likesHugging Face2026-10-26

Worth reading

Picked by rule from 1 tracked piece: the lab's own post, the most shared, a field report, the latest.

Most discussed · 463 HN pts

Kev: Tiny Jev-like family of decision models built on top of Qwen3.5

Kev is a family of small decision models built on Qwen3.5 and Qwen3.8 that you can train and run locally.

  • The Kev-27B model requires 96–128 GB of RAM on Macs, a requirement marked as expected but not measured.
  • An explicitly requested accelerator fails instead of falling back if the probe times out or crashes.
github.com 2026-09-21 463 HN pts · 210 comments 6.7k words about the kev family family

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 12 quantized builds and 17 Spaces.

  • As a service0no API provider listed
  • On own hardware12quantized builds
  • Built on113 fine-tunes · 6 adapters · 2 merges
  • In apps17Hugging Face Spaces

Top Spaces running it: multimodalart/jev-decision-index ♥ 802 · jaredpalmer/kev ♥ 93 · Station-house/ConspiracyBench ♥ 9 · jaredpalmer/kev-4b-decision-demo ♥ 2

The story so far

Weights, every dated article, and repo commits on one rail.

1 dated article and 20 repo commits so far

Teal is the weights (2026-09-19), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

Oct2026-10-01: 1 repo commit2026-09-24: 3 repo commits2026-09-21: 4 repo commits2026-09-20: 6 repo commits2026-09-19: 6 repo commitsKev: Tiny Jev-like family of decision models built on top of Qwen3.5 · 2026-09-21Weights 2026-09-19 · 2026-09-19Weights 2026-09-19now
Oct2026-10-01: 1 repo commit2026-09-24: 3 repo commits2026-09-21: 4 repo commits2026-09-20: 6 repo commits2026-09-19: 6 repo commitsKev: Tiny Jev-like family of decision models built on top of Qwen3.5 · 2026-09-21Weights 2026-09-19 · 2026-09-19Weights 2026-09-19now

What people built on it

23 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

23 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

12 quantized (Hugging Face model tree)3 fine-tunes (Hugging Face model tree)2 merges (Hugging Face model tree)6 adapters (Hugging Face model tree)12 quantized3 fine-tunes2 merges6 adapters
12 quantized (Hugging Face model tree)3 fine-tunes (Hugging Face model tree)2 merges (Hugging Face model tree)6 adapters (Hugging Face model tree)12 quantized3 fine-tunes2 merges6 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

14 rows

Benchmarks 14 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (14)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported MMLU-Pro · Reason through hard problems0.56—model author2026-10-07
author-reported Out-of-domain, transfer-v4 development (656): accuracy / Brier0.817 / 0.243—model author2026-10-07
author-reported Out-of-domain, transfer-v4 locked test (656): accuracy / Brier0.838 / 0.224—model author2026-10-07
author-reported Unanswerable items answered with p ≥ 0.9 (lower is better)0—model author2026-10-07
author-reported breadth-v1 development, all 14 datasets (3,075)0.7—model author2026-10-07
author-reported breadth-v1 development, audited, 10 datasets (2,475)0.77—model author2026-10-07
author-reported breadth-v1 test, all 14 datasets (3,089)0.69—model author2026-10-07
author-reported breadth-v1 test, chance-corrected index² [95 % CI]38.0 [35.5, 41.3]—model author2026-10-07
author-reported devtools-v1 development (1,072) / test (1,071), all sources0.739 / 0.756—model author2026-10-07
author-reported devtools-v1 development, audited sources (772)0.78—model author2026-10-07
author-reported hard-v1 development (1,083) / test (1,088)0.786 / 0.803—model author2026-10-07
author-reported tasksource-heldout-v1 development, all 24 families (2,788)0.63—model author2026-10-07
author-reported tasksource-heldout-v1 development, audited, 17 families (1,993)0.68—model author2026-10-07
author-reported transfer-v4 locked test: ECE / confident errors (p ≥ 0.9 and wrong) / coverage at ≤ 5 % error0.017 / 1.5% / 0.701—model author2026-10-07

What the card says

3 cited sentences

Summary generated, cited

Kev-4B is an English decision model that functions as a LoRA adapter and pointer head on the Qwen3.5-4B-Base backbone.[1] The model is designed for text classification tasks such as routing and triage, returning calibrated probability distributions without generating text.[2] It is released under the Apache 2.0 license and can be served on a single 24 GB GPU.[3][4]

  1. “LoRA adapter and a pointer head on Qwen3.5-4B-Base”
  2. “returns a calibrated probability distribution over the op…”
  3. license
  4. “small enough for one 24 GB GPU”

Generated 2026-10-07 15:18 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

3 use cases

What it is good at 3 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

12 fields

Listed in Decision models

Facts

Pipeline tagtext-classification
Librarypeft
MoEno
Languagesen
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-26

Decision facts

catalog

Decision-model catalog curated

How it was madeLoRA + new scoring/pointer head
Lineage bucketF - fine-tune of an open model (public/synthetic data)
Lineage evidenceboard file inspection (adapter/head/weight files) + HF base_model field
Base modelQwen/Qwen3.5-4B-Base
DI 0.2.1 score34.64 (independent (DI 0.2.1 board))
ECE (board)0.1756
Median latency52.1 ms (DI board, 1x RTX PRO 6000, single process)
Hosted price (input)self-hosted/M
Sourcehttps://huggingface.co/jaredpalmer/kev-4b

All variants

23 variants

Lineage

Adapter Family: jaredpalmer/kev 23 derivatives on the Hub

Base model: Qwen/Qwen3.5-4B-Base

quantized: 12adapter: 6finetune: 3merge: 2

Top variants (23 shown, ranked by downloads)

Relation
Quant
Size
23 rows
Variant Relation Quant Params Band License Author Likes Downloads
ggml-org/Kev-4B-GGUF quantized GGUF — — apache-2.0 ggml-org 28 50.9k
mys/kev-4b-GGUF quantized GGUF — — apache-2.0 mys 1 2.1k
turintech/Kev-4B-Artemis-GGUF quantized GGUF — — apache-2.0 turintech 8 1.6k
RoderickQiu/kev-4b-mlx-8bit quantized mlx 4.21B M apache-2.0 RoderickQiu 1 1.1k
Jakevin/kev-4b-ternary-mlx quantized mlx 488M S apache-2.0 Jakevin 1 414
alanhuangya/OpenJev-4B adapter — 4.21B M apache-2.0 alanhuangya 2 232
DreamBlooms/kev-4b-GGUF quantized GGUF — — apache-2.0 DreamBlooms 1 252
litert-community/Kev-4B-LiteRT finetune — — — apache-2.0 litert-community 0 283
aselea/Kev-4B-MLX-Serve-8bit quantized MLX 4.21B M apache-2.0 aselea 0 245
midudev/kev-4b-ONNX quantized — — — apache-2.0 midudev 2 0
ayan4m1/kev-4b-merged merge — 2.66B M apache-2.0 ayan4m1 0 87
Flolep2607/kev-4b-actions adapter — — — apache-2.0 Flolep2607 1 0
ArthurZ/kev-4b finetune — — — apache-2.0 ArthurZ 1 0
jtatman/kev-4b-code-verify-v1 adapter — — — apache-2.0 jtatman 0 38
Datchthana/kev-thai-pdpa-guard adapter — — — apache-2.0 Datchthana 0 35
jtatman/kev-4b-code-verify-v2 adapter — — — apache-2.0 jtatman 0 31
mlboydaisuke/Kev-4B-CoreAI quantized — — — apache-2.0 mlboydaisuke 0 24
danielamitay/kev-4b-fp32-swev quantized — — — apache-2.0 danielamitay 0 7
ai-ecoverse/kev-4b-vision-intent quantized — — — openrail ai-ecoverse 0 0
Flolep2607/kev-4b-guard adapter — — — apache-2.0 Flolep2607 0 0
verathos-ai/kev-4b-mlx-fp16 finetune mlx 4.21B M apache-2.0 verathos-ai 0 0
Starlinghq/kev-4b-onnx merge — — — apache-2.0 Starlinghq 0 0
jfrog-boost/Kev-4B-GGUF quantized GGUF — — apache-2.0 jfrog-boost 0 0

Repository changelog

20 commits

Repository changelog 20 commits

DateCommitSHA
2026-10-01 Kev 1.0 model card (weights unchanged) 6cfce5c
2026-09-24 Kev-4B round 10: skills delta (hard-v1 + devtools-v1) on top of round 8 139fdd9
2026-09-24 card: name the branch that holds the round-8 record 1da696f
2026-09-24 Kev-4B: real-document delta (round 8; documents-v1 locked test 0.804 -> 0.904) 957b91e
2026-09-21 card wording 485ace8
2026-09-21 calibration built in: head.pt carries the fitted temperature; KEV_TEMPERATURE=1.0 for raw logits e226ccc
2026-09-21 Model card: link the Space demo 70dd408
2026-09-21 Kev-4B: dates+unknowable delta on the v7 checkpoint (locked test 0.871 / 0.837); previous weights at tag v7-base 4bc64c6
2026-09-20 Kev-4B: Qwen3.5-4B-Base, decision-v7 recipe (locked test 0.870 / 0.832); previous Qwen3 weights at tag qwen3 f780095
2026-09-20 Card: Kev display names; Kev-0.5B scored on the same out-of-domain items (0.561) c4bfa11
2026-09-20 kev-4b: card note 4ccdc4e
2026-09-20 kev-4b: card refresh (0.6b column) 6b332b5
2026-09-20 kev kev-4b: release card 55dc64b
2026-09-20 kev-4b preview, decision-v7 recipe (transfer 0.790 dev / 0.806 locked; held-out pairs 0.73) 2bd3bb9
2026-09-19 Card: Jev reference numbers from the transfer-v4 report (0.857 / Brier 0.211 / pairs 0.86) 0d68f24
2026-09-19 Card: third seed (0.759/0.758/0.759) 002ed9f
2026-09-19 Card: fix per-source line f9e2c84
2026-09-19 Card: product-example caveat d11d4fa
2026-09-19 Research preview: Qwen3-4B-Base + LoRA r16, decision-v4, lr 5e-5 (trial lowdrift-4b-v4/01-trial-1); dev 0.843, transfer 0.759; locked test 0.852 / 0.794 (one ungated read) 1a0cb0a
2026-09-19 initial commit e254cf5

Articles and coverage

1 piece

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Analysis1

github.com2026-09-21authorityHN front pageabout the kev familyHN 463 pts · 210 comments6,746 words
Kev is a family of small decision models built on Qwen3.5 and Qwen3.8 that you can train and run locally. The API matches TypeSafe's System One, so you can point their Python SDK at your local server. Models use a small adapter on a frozen base, except Kev-27B which ships as 51 GB of full weights. The 27B model achieves 0.851 accuracy on new sources.written, reviewed, cited
  • The Kev-27B model requires 96–128 GB of RAM on Macs, a requirement marked as expected but not measured.
  • An explicitly requested accelerator fails instead of falling back if the probe times out or crashes.

Similar models

3 models

Similar models

  • Bespoke-Nimble-9B
    same task (text-classification), permissive license
  • NeoHorse-Jev-4B
    same task (text-classification), permissive license
  • Solomon
    same task (text-classification), permissive license

Sources

3 sources

Sources

Hub snapshot: 2026-10-26 · HF API: 2026-10-26 07:25 UTC · Providers: 2026-10-26 07:26 UTC · Articles: 2026-10-26 07:27 UTC · Summary: 2026-10-07 15:18 UTC · Last verified 2026-10-26 07:27 UTC

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