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
Download the complete model repository
hf download llm-semantic-router/Decision-1.0-Lux-9B --local-dir Decision-1.0-Lux-9B
This downloads the complete model release. The root config.json lists the backbone, tokenizer, decision head, and calibration files.
Serve with vLLM Semantic Router
This repository contains model data only. Use the vLLM Semantic Router Decision runtime to load llm-semantic-router/Decision-1.0-Lux-9B and serve Choice, Noul, and Score requests. The serving implementation and its dependencies live in vLLM Semantic Router; this release does not bundle executable model code. transformers.AutoModel.from_pretrained cannot load the custom Decision head directly.
After configuring a compatible Decision endpoint, send a SystemOne request (replace the placeholder URL and key):
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 requests a refund.","questions":{"route":{"type":"choice","instructions":"Which team should handle this?","criteria":{"billing":"Payments and refunds","technical":"Product faults"}}}}'
The Hugging Face repository is a model download, not a hosted inference endpoint.
The published model's complete state, question, and candidates have a 16,384-token input limit. See the evaluation scope for measured conditions.
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
Lux judges supplied evidence without live retrieval, so confidence does not guarantee factual correctness.