birgermoell / sven-0.6b

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Model's Last Updated: September 23 2026
text-classification

Introduction of sven-0.6b

Model Details of sven-0.6b

Sven-0.6B — svensk System One-beslutsmodell

Sven-0.6B

Sven answers typed questions ( choice / score / noul ) about Swedish or English text with calibrated probabilities in one forward pass. No text generation, no type errors. It is a delta fine-tune of Kev-0.6B — the open, Apache-2.0 Jev-family member built by Jared Palmer — adapted to Swedish by Birger Moell / AI Sweden.

Intended use

System One requests in Swedish or English: kundtjänst-routing, mejltriage, phishing/bedrägeri and prompt-injection-gardrails, incident-klassning, urgency/frustration-rubriker — the same mixed distribution as the upstream Kev recipe (banking77, MNLI, SST-5, BoolQ, …), machine-translated to Swedish plus hand-written Swedish seed records. Serve with the vendored Kev server: python -m kev.serve --run <this repo> --port 8009 (TypeSafe-compatible /v1/systemone ).

Results

Evaluated fp32 (MPS), frozen upstream dev partitions; Swedish split held out per record.

eval accuracy Brier ECE selective acc @50% cov n (rejected)
Swedish held-out 0.745 0.331 0.079 0.947 400 (8)
└ choice 0.751 — — — —
└ noul (yes/no) 0.863 0.225 0.075 0.986 —
└ score (0–4 rubrics) 0.520 0.556 0.099 — —
English in-distribution (decision-v7 dev) 0.794 0.299 0.090 0.959 1,204
English out-of-domain (transfer-v4 dev) 0.623 0.536 0.146 0.726 764

Upstream reference, same suites (Kev-0.6B, English): 0.801 in-distribution / 0.620 out-of-domain acc; Brier 0.266 / 0.483 (locked test). Sven retires < 1 point of upstream English accuracy with the Swedish delta applied — the fine-tune does not wash out the base recipe.

Rejected records (state/branch over Kev's serving context, e.g. 77-option routing with long Swedish criteria) count as wrong; 8/400 = 2% on the Swedish split.

Training
  • Base: Qwen/Qwen3-0.6B-Base @ da87bfb6… (Apache-2.0), warm-started from jaredpalmer/kev-0.6b (392 LoRA tensors + pointer head), delta LR 2e-5 , 2 epochs, batch 1 × grad-accum 8, fp32 master weights, cross-entropy on the option distribution (upstream protocol; no Jev outputs used).
  • Data: 5,000 EN→SV machine-translated records (stratified sample of upstream decision-v7 train; MT: Helsinki-NLP/opus-mt-en-sv , unique-string cache; option keys kept English with Swedish criteria text) + 36 hand-written Swedish seed records (Swedish option keys; routing, phishing, incident, GDPR and prompt-injection domains) + 200 English replay records from the upstream suite to prevent recipe drift.
  • Provenance: sven-data dataset (train/held-out split sha256 of suite, per-source counts, MT audit sample of 30 EN→SV pairs).
Data & licenses

Everything open: Apache-2.0 (code + weights) per upstream Kev; Qwen3-0.6B-Base Apache-2.0; OPUS-MT translator open. Translated training data is redistributed for research with per-source documentation in DATA_LICENSES.md (Banking77 CC-BY-4.0, BoolQ/MNLI/DBpedia CC-BY-SA, etc.); dataset release ships provenance so stricter per-source terms can be traced. Hand-written Swedish seeds and this card: CC-BY-4.0 / Apache-2.0 respectively.

Known limits
  • Score questions are the weak spot (0.52 on translated 0–4 rubrics): the length of Swedish criteria plus 0.6B capacity — not data volume — is the binding constraint (upstream notes the same physics). The documented fix is the 4B backbone (planned v0.2).
  • MT-translated labels inherit MT noise; review audit_sv.tsv ( sven-data ) for a human-checked sample. One long-review record showed visible MT truncation.
  • Most translated records keep English option keys ( fakturering -style keys only in seeds), so Swedish option-key handling is narrower than the state/question coverage.
  • Probabilities are advisory out-of-domain (ECE 0.15 transfer) — gate automation on confidence (at ≥0.9 confidence the Swedish split lands ~0.90 accuracy on the covered share).
  • Same architecture caveats as upstream: option-order sensitivity remains; questions cannot read each other by design; latency unchanged from Kev-0.6B (0.12 s / five questions on M-series).
Use
from typesafe import TypeSafeClient
client = TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8009", model="sven-latest")
KEV_DTYPE=bf16 python -m kev.serve --run <HF repo or local run dir> --port 8009
License

Apache-2.0 for the adapter and head (upstream Kev license). Base model Apache-2.0 (Qwen3). Built with the vendored Kev codebase (see VENDORED.md).

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51
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