Decision-1.0-Sol-2B

Sol, Latin for sun.

Give Sol a state, questions and possible answers. It returns 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

66.32% weighted accuracy across 3,766 decisions and 54 tasks. Compare decision, reading and transfer capabilities in the complete results below.

Model	Size	Decisions	Composition	Reading	Inference	Transfer	Overall
Sol-2B	2B	73.75	46.08	76.56	84.17	57.07	66.32
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
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. Bold marks a Decision-family cell above every external open or untuned reference; Jev and other Decision models are excluded.

All 54 tasks · Probability, order and missing-evidence diagnostics · Methods and uncertainty

More questions, measured

Distinct Choice questions at a fixed 499 input tokens per question. Thirty measurements per point across six independently loaded processes on an otherwise idle AMD gfx942 GPU. Python request latency includes tokenization and inference; loading and network are excluded. p50, p95 and memory.

Download the complete model repository

hf download llm-semantic-router/Decision-1.0-Sol-2B --local-dir Decision-1.0-Sol-2B

This downloads the complete model release. The root config.json lists the backbone, tokenizer, decision head, and calibration files.

Use with 🤗 Transformers

The repository includes its inference code, so stock Transformers can download and run the complete model locally with trust_remote_code=True. system_one takes and returns the same System One request and response bodies as the Decision runtime; nothing is generated.

pip install "transformers>=5.17" torch safetensors huggingface_hub

from transformers import AutoModel

model = AutoModel.from_pretrained("llm-semantic-router/Decision-1.0-Sol-2B", trust_remote_code=True)
response = model.system_one(
    state="Customer requests a refund.",
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which team should handle this?",
            "criteria": {"billing": "Payments and refunds", "technical": "Product faults"},
        },
        "priority": {
            "type": "score",
            "instructions": "How urgent is this request?",
            "criteria": ["Not urgent", "Soon", "Today", "Immediately"],
        },
    },
)
print(response["answers"]["route"]["choice"], response["answers"]["priority"]["score"])

pipeline("decision", model="llm-semantic-router/Decision-1.0-Sol-2B", trust_remote_code=True) accepts the same request body. A malformed question is answered with an invalid_question error. The model loads on the first GPU when one is visible, otherwise on the CPU (pass device="cpu" or device="cuda:0" to choose). On a GPU the backbone runs in BF16 with an FP32 decision head; on the CPU everything runs in FP32. A complete question, its candidates and the state are limited to 16,384 tokens; if a question is longer, every question of the request is answered with a max_length_exceeded error and nothing is truncated. flash-linear-attention speeds up the linear-attention layers on a GPU.

Serve with vLLM Semantic Router

This repository contains model data and its Transformers loading code. Use the vLLM Semantic Router Decision runtime to load llm-semantic-router/Decision-1.0-Sol-2B and serve Choice, Noul, and Score requests. The serving implementation and its dependencies live in vLLM Semantic Router; this release bundles no serving code. The weights do not depend on a particular accelerator; which hardware can serve them is decided by the runtime. For local inference without a server, see Use with 🤗 Transformers above.

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-Sol-2B","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 Qwen3.5-2B. It evaluates supplied evidence without live retrieval; confidence does not guarantee correctness. License · Attributions.