K-Decision-9B v0.1

Korean-specialized decision model by ONYULTEA — first public release.

K-Decision is a lightweight decision engine that takes a state and typed questions as input and returns structured decisions with calibrated probability distributions over all options — all in a single forward pass. Unlike chat LLMs, it does not generate text; it predicts only the answer-symbol logits for each question (System One / Jev compatible).

Benchmarks

• Trained and served on a single 24GB consumer GPU (int8 LoRA, r=32)
• Calibration: ECE 0.001 level (domain holdout), per-type temperature fitting

Quickstart

1) Hugging Face transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ONYULTEA/K-Decision-9B", torch_dtype=torch.bfloat16, device_map="auto")
tok = AutoTokenizer.from_pretrained("ONYULTEA/K-Decision-9B")

2) /v1/systemone server (System One / Jev compatible)

git clone https://github.com/onyultea/k-decision
python src/serve_systemone.py --merged ONYULTEA/K-Decision-9B --port 8111

// Request
{"model": "k-decision",
 "state": "Civil complaint: Strong gas smell reported (call center)",
 "questions": {
   "route":  {"type": "choice", "instructions": "Which department handles this?",
              "criteria": {"gas": "Gas Safety Division", "water": "Waterworks", "road": "Road Maintenance"}},
   "urgent": {"type": "noul", "instructions": "Is this an urgent case?"}}}

// Response — probability for every option
{"answers": {
   "route":  {"type": "choice", "choice": "gas",
              "probabilities": {"gas": 0.87, "water": 0.05, "road": 0.08}},
   "urgent": {"type": "noul", "noul": 0.93}}}

Question types: choice (2–26 options), noul (yes/no). Compatible with Decision Index reproduction kit and Kev clients.

Method

1. Marker single-pass — A JSON skeleton {"route":□,"urgent":□} is appended to the prompt; the logit immediately before each marker predicts that field's answer. All fields decided in one forward pass (no decoding loop).
2. Constrained decoding — Softmax restricted to legal answer symbols (A–Z / Y,N) → guaranteed sum-to-1 probabilities for all options.
3. Calibration — Per-type (choice/noul) temperature fitted on held-out data to optimize Brier/ECE — probabilities are trust scores.
4. int8 LoRA training — 4-bit quantization was measured to degrade probability precision; int8 adopted instead (+3pp accuracy at same budget).

Training Data

• Korean life-domain synthetic (~28K records): 20 government services, medical triage, shopping CS, life infrastructure (real estate/transport/telecom), education, workplace, agent tool gating — with honorific/casual register, typo, and slang augmentation.
• General typed decisions: tasksource-jev-typed-decisions (CC-BY-4.0) sample, multilingual distillation (Apache-2.0, teacher-probability soft labels), tool-decision pool (CC-BY-4.0).
• Regulation compliance task: public university regulation documents (topic routing, numeric-threshold violation, applicability).

Limitations

• Choice up to 26 options, state up to 6,000 characters — exceeding limits returns Unsupported (HTTP 422, honest rejection).
• World knowledge (KMMLU ~30%) is at 9B base level — fine-tuning changes expression, not knowledge.
• High-stakes decisions (medical treatment, credentialing, hiring) require human review.
• High probability does not guarantee correctness — threshold-based operation recommended.

License

• Model weights: Apache-2.0 (derived from Qwen3.5-9B)
• Training & evaluation code: github.com/onyultea/k-decision

Citation

@software{kdecision2026,
  title = {K-Decision-9B: Korean-specialized decision model},
  author = {ONYULTEA},
  year = {2026},
  url = {https://huggingface.co/ONYULTEA/K-Decision-9B}
}

K-Decision-9B v0.1 (한국어)

ONYULTEA 한국 특화 디시전 모델 — 첫 공개 릴리스.

K-Decision은 상태(state)와 타이핑드 질문(typed questions)을 입력받아, 모든 옵션에 걸쳐 보정된 확률 분포를 붙인 구조화 결정을 단일 forward 패스로 반환하는 경량 의사결정 엔진입니다. 일반 챗 LLM과 달리 텍스트를 생성하지 않고, 각 질문의 정답 심볼 로짓만 예측합니다 (System One / Jev 호환).

성능

• 학습·서빙 모두 단일 24GB 소비자 GPU (int8 LoRA, r=32)
• 확률 보정: ECE 0.001 수준, 타입별 온도 피팅

빠른 시작

허깅페이스 transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ONYULTEA/K-Decision-9B", torch_dtype=torch.bfloat16, device_map="auto")
tok = AutoTokenizer.from_pretrained("ONYULTEA/K-Decision-9B")

/v1/systemone 서버

git clone https://github.com/onyultea/k-decision
python src/serve_systemone.py --merged ONYULTEA/K-Decision-9B --port 8111

질문 타입: choice(2~26옵션), noul(예/아니오). Decision Index 킷·Kev 클라이언트 호환.

작동 원리

1. 마커 단일패스 — JSON 스킬레톤의 마커(□) 직전 로짓이 해당 필드 결정을 예측, N개 필드 1회 forward 동시 결정
2. 제약 디코딩 — 합법 심볼(A~Z / Y,N)만으로 softmax, 합계 1 보장
3. 캘리브레이션 — choice/noul 각각 온도 피팅, Brier/ECE 최적화
4. int8 LoRA — 4비트 양자가 확률 정밀도를 해침을 실측 확인, int8 채택

한계

• choice 최대 26옵션, state 최대 6,000자 — 초과 시 정직 거부
• 세계 지식(KMMLU ~30%)은 9B 베이스 수준 — 파인튜닝은 발현을 바꾸지 지식을 만들지 않음
• 고위험 의사결정에는 인간 검토 필수

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