Krino-2B

Krino (Greek κρίνω, to decide; the root of criterion) is a 2B-parameter System One decision model. You give it a piece of text (the state) and one or more typed questions — noul (yes/no), choice (pick one option), score (an ordered level) — and it returns a probability for every option of every question in one forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate; what you get is a distribution you can threshold, route on, or log.

Krino is built on Kev (Apache-2.0): a rank-16 LoRA adapter and a pointer head on a frozen Qwen/Qwen3.5-2B-Base, served with the TypeSafe System One wire format (POST /v1/systemone). It is the 2B-parameter member of that family, between Kev-0.8B and Kev-4B, trained on Kev's open data plus a commercially licensed breadth mix, and served with one temperature per question type.

• Weights, code and training data: Apache-2.0 or more permissive. No research-only sources (XNLI, RACE and BeaverTails, which make other 2B decision models non-commercial, are deliberately out).
• Repository: github.com/guru0381/Krino — the recipe, every script, the evaluation protocol (docs/EVAL.md), every number (docs/RESULTS.md) and the decision log (PLAN.md).
• Base: Qwen/Qwen3.5-2B-Base at revision b1485b2fa6dfa1287294f269f5fb618e03d52d7c (1.9B parameters, Apache-2.0).

Serve

pip install "krino @ git+https://github.com/guru0381/Krino.git@v0.1.0"
krino-serve --run Guru0381/krino-2b@v0.1.0 --host 0.0.0.0 --port 8008

krino-serve is Kev's server (kev.serve: batching, prefix cache, CUDA graphs, bearer auth with KEV_API_KEY) with one addition: the checkpoint is loaded raw and every answer is re-served at its question type's temperature (krino.json in this repo; see Calibration). GET /v1/krino shows the map in use. It runs on any CUDA GPU with about 8 GB free in bf16 (an L4 is enough), on Apple Silicon through Kev's MLX backend, and on CPU for testing. KEV_TRUNCATE_STATES=1 truncates states over 65,536 tokens instead of refusing them with a 422.

curl -s http://127.0.0.1:8008/v1/systemone -H 'content-type: application/json' -d '{
  "model": "kev-latest",
  "state": "Shoes arrived two weeks late and in the wrong size. Also I see two charges on my card.",
  "questions": {
    "department": {"type": "choice", "instructions": "Which team should handle this?",
                   "criteria": {"returns": "Exchanges, refunds, wrong or damaged items",
                                "shipping": "Delivery status, delays, lost packages",
                                "billing": "Charges, invoices, payment problems"}},
    "escalate":   {"type": "noul",  "instructions": "Does this need urgent human attention?"},
    "frustration":{"type": "score", "instructions": "How frustrated is the customer?",
                   "criteria": ["Calm", "Frustrated", "Very angry"]}}}'

The response carries probabilities for every question ({"returns": 0.71, "shipping": 0.17, "billing": 0.12}, {"false": 0.3, "true": 0.7}, a probability per level), the argmax answers, and usage.input_tokens. The TypeSafe Python client works unchanged (TypeSafeClient(base_url="http://127.0.0.1:8008", api_key="local")).

Results

Every number below is on a partition that never entered training, read at the raw temperature (T = 1) unless the column says otherwise. Partitions, the decision rule and the disclosure log are in docs/EVAL.md; the full tables with seeds and intervals are in docs/RESULTS.md.

Locked test (read once, after the release candidate was chosen on development)

suite	questions	accuracy	Brier	ECE
transfer-v4 test (six never-trained public sources + held-out policy structures)	656	0.790	0.311	0.110
decision-v7 test (Kev's trained sources)	1,200	0.848	0.227	0.074

transfer-v4's test accuracy equals its development accuracy (0.790), so nothing was tuned to the development split. The nine breadth suites have no test partition (they are Malkuth's development-only suites); their numbers below are development reads, used to choose the checkpoint.

Development panel (chose the checkpoint; two seeds, the released seed shown)

suite	what it measures	questions	accuracy	Brier	ECE
hard-v1	Kev's seven hard families: long policies with exceptions, trade-offs, probability, multi-hop, dates and arithmetic, judging an answer, missing-fact abstention	1,083	0.717	0.392	0.091
documents-v1	real US consumer-finance complaints (CFPB), product and issue, states to 7k tokens	920	0.860	0.224	0.080
devtools-v1	developer tooling: code review, commit type, flaky tests, content safety, tool routing, prompt injection	1,074	0.682	0.450	0.108
transfer-v4 dev	never-trained public sources (MMLU, QNLI, PAWS, SciQ, emotion, tweet_offensive) + held-out policy structures	656	0.790	0.312	0.090
decision-v7 dev (guard)	Kev's trained sources	1,264	0.842	0.236	0.088
krino-breadth (nine suites)	datasets neither Kev nor the breadth mix trained on: Belebele, SIB-200, RTP-LX, PolyGuard, GoEmotions, LEDGAR, KOLD, Laya apps, MultiEURLEX	7,657	0.655 (mean)

Option-order permutation flip rate on transfer-v4: 2.8 % (13.9 % after stage 1 of the same recipe).

JevBench public items (report-only)

JevBench publishes 231 of its decisions (original 72, easy 48, hard 111) and keeps 1,200 sealed. Krino never trained on the public items — every training set is screened against them (screen_overlap.py, 0 offending records) — and never used them to choose a checkpoint; they were read twice for this checkpoint, once at a global temperature and once with the per-type map, both logged in docs/EVAL.md.

tier	correct	accuracy
original	66 / 72	0.917
easy	48 / 48	1.000
hard	47 / 111	0.423
all public	161 / 231	0.697

For scale on the same harness and items: Malkuth-2B 159/231 (hard 45/111), Kev-0.8B 147/231 (hard 41/111). The sealed score is the board's to measure; the request for a row is docs/jevbench-request.md in the repository.

Calibration: one temperature per question type

Krino's head is trained at T = 1 and served through a per-type map, krino.json:

type	served T	why
choice	1.95	minimises top-label ECE on the nine never-trained breadth suites (five-fold, out-of-fold ECE 0.023); hard-v1 dev ECE 0.091 → 0.041
noul	0.30	yes/no answers are read as decisions: a probability near 0.5 is an abstention, and a soft temperature puts half of them there. 0.3 keeps 90 % of answers decisive at the argmax accuracy
score	0.50	ordered levels are read by expected level; a soft distribution drags the expectation to the middle of the scale

The argmax never changes with the temperature, so accuracy is identical under any map; what changes is how the distribution should be read. If you want raw probabilities, run with KRINO_TEMPERATURES="choice=1,noul=1,score=1". If you want a single global temperature as Kev serves, choice=1.95,noul=1.95,score=1.95. Hard, out-of-distribution inputs are over-confident at every setting: on the JevBench hard tier the per-type map gives Brier 0.844 / ECE 0.321 against 0.706 / 0.236 for the global 1.95 — the price of decisive yes/no answers.

Training

Two stages, both with Kev's trainer (kev.train) on one H100 through Kev's Modal runner, LoRA rank 16 (α 32, dropout 0.05, all linear projections), pointer head from scratch, bf16, gradient checkpointing, cross-entropy over each question's options, option order shuffled, "none of the above" options and distractors inserted at random, a quarter of choice records paired with their none-of-the-above minimal pair (p_none_pair 0.25).

1. Stage 1 — Kev's base recipe. Two epochs on decision-v7 (12,576 records: ten public classification datasets with native labels, generated policy minimal pairs, generated rule structures), learning rate 7e-5, batch 4 × 2 accumulation. Two seeds; the seed with the better transfer-v4 development accuracy was carried forward.
2. Stage 2 — one combined delta. One epoch from stage 1 at learning rate 2e-5, batch 2 × 4, states to 7,552 tokens, on mix v1 (79,484 records) plus 6,000 records replayed from decision-v7: the breadth half is Malkuth's commercially licensed sources — MASSIVE intent and scenario (Apache-2.0), PAWS-X without English (free for any use), CLINC150 (CC BY 3.0), KLUE (CC BY-SA 4.0), NSMC (CC0), KoBEST (CC BY-SA 4.0), typed-decisions (Apache-2.0), ARC (CC BY-SA 4.0), multilingual toxicity (OpenRAIL++), jailbreak classification (Apache-2.0), Civil Comments (CC0), APEACH (CC BY-SA 4.0), XQuAD without English (CC BY-SA 4.0), Aegis 2.0 (CC BY 4.0); the depth half is Kev's own training partitions — hard-v1 (generated, twice), documents-v1 (CFPB narratives, public domain) and devtools-v1 (permissive per-source licences in its manifest). Two seeds; both passed every clause of the decision rule; the released seed was chosen by transfer-v4 development.

Out, by measurement rather than by taste: a KL anchor to the frozen base (null in Kev's and strands-decider's logs), a second stacked delta of answer-adequacy and multi-step-document rows (docs/RESULTS.md, Run 1: +15 to +39 points on its own held-out formats and nothing on the hard families, so it was not released), and Malkuth's weights as a warm start (research-only licence). No output of Jev (TypeSafe's hosted model) was used anywhere.

Limitations

• A 2B torso has a ceiling on reading. Dates and arithmetic (hard-v1 temporal_numeric 0.56) and probability (0.60) are where it is weakest, then trade-offs (0.69); long policies, multi-hop and judging sit at 0.73–0.80.
• Over-confident out of distribution. The temperatures are fitted on development rows; on harder, unfamiliar inputs the confidence runs ahead of the accuracy (JevBench hard tier: 42 % accurate at ECE 0.32 with the per-type map). Treat the probabilities as rankings there, or re-fit krino.json on your own held-out data.
• Abstention is a label, not a behaviour. The model picks the best option it is given; it will not refuse. Put an explicit "insufficient information" option in criteria if that is a valid answer.
• Context. States are encoded up to 65,536 tokens; with KEV_TRUNCATE_STATES=1 longer ones are cut, otherwise refused.
• English-centred with multilingual breadth. Korean, the six PAWS-X languages and the XQuAD languages appear in the mix; everything else is zero-shot from the base.
• A decision model answers the question it is asked, on the state it is given. It has no access to anything else.

Reproduce

The repository pins every input: Kev at fe64b1274ea7f80d4095866df90666abb03e9cf6, the base revision above, the suite checksums in third_party/kev/evals/*/manifest.json, the mix builder (tools/build_mix.py) with its screen, the study plans (experiments/stage1-2b.json, experiments/stage2-2b-s1.json) and the serving map (tools/typed_competence.py --write). provenance.json and training_config.json beside the weights carry the trial's hashes.

Citation

@misc{krino2b,
  title  = {Krino-2B: a 2B System One decision model with per-type serving temperatures},
  author = {Gurunath Reddy},
  year   = {2026},
  url    = {https://huggingface.co/Guru0381/krino-2b}
}

Built on Kev by Jared Palmer (Apache-2.0) and on Malkuth's breadth recipe by newfull5 (Apache-2.0 code); evaluated with JevBench by Benchmark Heaven. Thanks to all three.