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llm-semantic-router/Decision-1.0-Lux-9B

sourceHugging Faceapache-2.0updated 2d agoView on Hugging Face
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Decision-1.0-Lux-9B

Lux, Latin for light.

Give Lux evidence, questions and possible answers. It returns decisions and probabilities for labels you define at runtime.

Decision family

TypeUse it forOutput
ChoiceRoute a request or choose among 2–255 actions.Selected ID + probability distribution
NoulJudge a condition against supplied evidence.P(true)
ScoreApply 2–10 ordered rubric descriptions.Expected index + probability distribution

Measured capability

77.40% weighted accuracy across 3,766 decisions and 54 tasks: +5.51 points over Kev-9B and +4.32 over Nox-4B on the same benchmark.

ModelSizeDecisionsCompositionReadingInferenceTransferOverall
Lux-9B9B84.3852.7590.1691.4677.7277.40
Nox-4B4B83.0051.7979.0686.2569.6073.09
Kev-9B9B76.7545.7586.7283.5479.2571.89
Kev-4B4B71.9048.5481.8884.5876.1070.09
Qwen3.5-9B9B73.9144.6289.8479.5873.2369.73
Decider2B64.0146.5892.0384.3869.3167.71
Qwen3.5-4B4B69.8943.3387.9779.7968.8367.29
Sol-2B2B73.7546.0876.5684.1757.0766.32
Eos-0.8B0.8B65.9446.0470.3181.6752.0161.89
Kev-0.8B0.8B60.1442.2967.8168.7561.1958.28
Qwen3.5-2B2B57.1239.0073.7572.2956.3157.24
Kai-0.6B0.6B57.9640.8354.6969.7948.3753.52
Laya · English0.421B56.5435.3351.4163.7553.0651.03
Laya · Multilingual0.322B47.2538.9250.7857.2947.1347.19
Jev—79.1066.3894.5389.7987.1981.05

Accuracy (%), using the same five-panel decision benchmark. General decisions contribute 30%; composition contributes 25%; reading, inference and external transfer each contribute 15%. Bold marks a Decision model strictly above every external open reference in that column; Jev and other Decision models are excluded from this threshold. Full tasks, uncertainty and comparator identities.

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All 54 tasks · Probability quality, order and missing evidence

Use Lux-9B

Use the official TypeSafe Python SDK with your SystemOne-compatible endpoint, configured to serve Decision-1.0-Lux-9B. Replace the example URL and API key with your own.

bash
pip install typesafe-sdk
python
from typesafe_sdk import Choice, Noul, TypeSafeClient

with TypeSafeClient(
    api_key="YOUR_ENDPOINT_API_KEY",
    base_url="https://your-decision-endpoint.example",
    model="Decision-1.0-Lux-9B",
) as client:
    result = client.system_one(
        state="Customer reports a duplicate charge and asks for a refund.",
        questions={
            "route": Choice(
                instructions="Which team should handle this request?",
                criteria={"billing": "Payments and refunds", "technical": "Product faults"},
            ),
            "refund_requested": Noul(instructions="Did the customer request a refund?"),
        },
    )
    print(result.choices["route"].choice)
    print(result.nouls["refund_requested"].noul)

The same request with curl:

bash
curl -X POST 'https://your-decision-endpoint.example/v1/systemone' \
  -H 'Authorization: Bearer YOUR_ENDPOINT_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
  "model": "Decision-1.0-Lux-9B",
  "state": "Customer reports a duplicate charge and asks for a refund.",
  "questions": {
    "route": {
      "type": "choice",
      "instructions": "Which team should handle this request?",
      "criteria": {
        "billing": "Payments and refunds",
        "technical": "Product faults"
      }
    },
    "refund_requested": {
      "type": "noul",
      "instructions": "Did the customer request a refund?"
    }
  }
}'

Typed request and response guide · Model runtime requirements

More questions, measured

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Latency uses the same architecture and runtime, measured with earlier weights.

Distinct Choice questions, fixed at 499 input tokens per question. Thirty measured requests per point across six fresh processes on an otherwise idle AMD GPU. Python latency includes tokenization, inference and response construction; loading and network are excluded. p95, memory and hardware.

Architecture

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A causal Qwen3.5 text backbone combines Gated DeltaNet and full attention. A shared candidate head reads contextual candidate endpoints and the final query vector, producing one probability per supplied answer.

Candidate head · Vector architecture · Model details

The full state, question and candidates must fit 16,384 tokens; overflow is rejected. AMD gfx942 is validated; other hardware requires separate qualification. Lux judges supplied evidence without live retrieval, so confidence does not guarantee factual correctness.

License · Attributions · Runtime