Decision-1.0-Nox-4B

Nox, Latin for night.

Give Nox a state, questions and possible answers. It returns typed decisions and probabilities, with labels defined at runtime.

Decision family

Type	Use it for	Output
Choice	Route a request or choose among 2–255 actions.	Selected ID + distribution
Noul	Check a condition against supplied evidence.	P(true)
Score	Apply 2–10 ordered rubric descriptions.	Expected index + distribution

Measured capability

73.09% weighted accuracy across 3,766 decisions and 54 tasks: +2.99 points over Kev-4B on the same benchmark.

Model	Size	Decisions	Composition	Reading	Inference	Transfer	Overall
Nox-4B	4B	83.00	51.79	79.06	86.25	69.60	73.09
Lux-9B	9B	84.38	52.75	90.16	91.46	77.72	77.40
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 (%). Overall weights: Decisions 30%, Composition 25%, Reading 15%, Inference 15%, Transfer 15%. These outcome-informed product-priority weights were chosen after observing results; reweighting is not a training improvement. Bold marks Decision-family cells above every external open or untuned reference for that metric, excluding Jev and the other Decision models.

All 54 tasks · Order, missing-evidence and calibration diagnostics · Methods and uncertainty

Download the complete model repository

hf download llm-semantic-router/Decision-1.0-Nox-4B --local-dir Decision-1.0-Nox-4B

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-Nox-4B 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-Nox-4B","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.

Architecture

A causal Qwen3.5 text backbone combines gated linear and full attention. A shared candidate head reads candidate endpoints and the final query vector. The serving runtime schedules questions according to available hardware and request load.

Candidate head · Vector architecture

Adapted from Qwen3.5-4B. It evaluates supplied evidence without live retrieval; confidence does not guarantee correctness. License · Attributions.