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llm-semantic-router / Decision-1.0-Lux-9B
Model's Last Updated: October 01 2026
Introduction of Decision-1.0-Lux-9B
Model Details of Decision-1.0-Lux-9B
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.
| Type | Use it for | Output |
|---|---|---|
| Choice | Route a request or choose among 2–255 actions. | Selected ID + probability distribution |
| Noul | Judge a condition against supplied evidence. | P(true) |
| Score | Apply 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.
| Model | Size | Decisions | Composition | Reading | Inference | Transfer | Overall |
|---|---|---|---|---|---|---|---|
| Lux-9B | 9B | 84.38 | 52.75 | 90.16 | 91.46 | 77.72 | 77.40 |
| Nox-4B | 4B | 83.00 | 51.79 | 79.06 | 86.25 | 69.60 | 73.09 |
| Kev-9B | 9B | 76.75 | 45.75 | 86.72 | 83.54 | 79.25 | 71.89 |
| Kev-4B | 4B | 71.90 | 48.54 | 81.88 | 84.58 | 76.10 | 70.09 |
| Qwen3.5-9B | 9B | 73.91 | 44.62 | 89.84 | 79.58 | 73.23 | 69.73 |
| Decider | 2B | 64.01 | 46.58 | 92.03 | 84.38 | 69.31 | 67.71 |
| Qwen3.5-4B | 4B | 69.89 | 43.33 | 87.97 | 79.79 | 68.83 | 67.29 |
| Sol-2B | 2B | 73.75 | 46.08 | 76.56 | 84.17 | 57.07 | 66.32 |
| Eos-0.8B | 0.8B | 65.94 | 46.04 | 70.31 | 81.67 | 52.01 | 61.89 |
| Kev-0.8B | 0.8B | 60.14 | 42.29 | 67.81 | 68.75 | 61.19 | 58.28 |
| Qwen3.5-2B | 2B | 57.12 | 39.00 | 73.75 | 72.29 | 56.31 | 57.24 |
| Kai-0.6B | 0.6B | 57.96 | 40.83 | 54.69 | 69.79 | 48.37 | 53.52 |
| Laya · English | 0.421B | 56.54 | 35.33 | 51.41 | 63.75 | 53.06 | 51.03 |
| Laya · Multilingual | 0.322B | 47.25 | 38.92 | 50.78 | 57.29 | 47.13 | 47.19 |
| Jev | — | 79.10 | 66.38 | 94.53 | 89.79 | 87.19 | 81.05 |
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 .
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.
pip install typesafe-sdk
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:
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
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
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.
Runs of llm-semantic-router Decision-1.0-Lux-9B on huggingface.co
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More Information About Decision-1.0-Lux-9B huggingface.co Model
Url of Decision-1.0-Lux-9B
Decision-1.0-Lux-9B huggingface.co Url
Provider of Decision-1.0-Lux-9B huggingface.co
Other API from llm-semantic-router
Updated:March 06 2026
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