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Sponsored by
APIMaster
llm-semantic-router / Decision-1.0-Sol
Model's Last Updated: September 22 2026
Introduction of Decision-1.0-Sol
Model Details of Decision-1.0-Sol
Decision-1.0-Sol
Sol, Latin for sun.
Your move. Give Sol a state, questions and possible answers. It returns decisions and probabilities with labels defined at runtime.
1.884B parameters · 16K complete-question budget · English / Chinese evaluated · Apache 2.0
| 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
70.14% four-panel mean across 2,720 decisions. Results include strengths and remaining gaps across all 27 tasks.
| Model | Mean accuracy ↑ | Decisions | Composition | Reading | Inference |
|---|---|---|---|---|---|
| Sol · 2B · v1.3 | 70.14 | 73.75 | 46.08 | 76.56 | 84.17 |
| Nox · 4B · v1.3 | 75.03 | 83.00 | 51.79 | 79.06 | 86.25 |
| Kev · 9B | 73.09 | 76.36 | 45.54 | 86.72 | 83.75 |
| Decider · 2B | 71.75 | 64.01 | 46.58 | 92.03 | 84.38 |
| Qwen3.5 · 4B · untuned | 70.25 | 69.89 | 43.33 | 87.97 | 79.79 |
| Qwen3.5 · 2B · untuned | 60.54 | 57.12 | 39.00 | 73.75 | 72.29 |
| Laya · Upstream default | 52.44 | 57.01 | 37.75 | 51.25 | 63.75 |
| Jev · 1.13.0 · frontier | 82.45 | 79.10 | 66.38 | 94.53 | 89.79 |
Accuracy (%). Each panel contributes one quarter with its frozen family/source weights. Bold marks Sol or Nox strictly above every external open reference for that metric; the other Decision model and Jev are excluded from the threshold. Jev is a closed-service frontier reference. Methods and uncertainty .
Detailed capabilities
Semantic consistency
| Model | Consistent / eligible groups | Consistency ↑ |
|---|---|---|
| Sol · 2B · v1.3 | 167/192 | 86.98 |
| Nox · 4B · v1.3 | 174/192 | 90.62 |
| Kev · 9B | 143/192 | 74.48 |
| Decider · 2B | 126/192 | 65.62 |
| Qwen3.5 · 4B · untuned | 98/192 | 51.04 |
| Qwen3.5 · 2B · untuned | 94/192 | 48.96 |
| Laya · Upstream default | 115/192 | 59.90 |
| Jev · 1.13.0 · frontier | 158/192 | 82.29 |
Mixed equivalent variants of language, keys and presentation; consistency is not accuracy . A consistently wrong answer still counts. This observed-core supplement weights 192 eligible semantic groups equally and is outside the four-panel mean. Definitions and exact counts .
More questions, measured
Same inputs and physical AMD gfx942 GPU; 30 measured requests per point across three blocks. Python request latency includes tokenization and inference, excluding loading and network. p95 and all three native types .
Try it
Download
hf download llm-semantic-router/Decision-1.0-Sol --revision v1.3 --local-dir decision-model
, then follow
ROCm setup
. In that container, with the model mounted at
/model
:
from decision import DecisionModel
from decision.example import REQUEST
model = DecisionModel.from_pretrained("/model", local_files_only=True)
print(model.decide(**REQUEST)["answers"])
Tested request and output · Install and API guide
The complete state, question and candidates must fit 16,384 tokens; overflow is rejected. The bundled normalization profile loads automatically. AMD gfx942 is validated; CPU/MPS are unsupported and NVIDIA is unqualified. Use a fresh Python process when switching profiles.
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. Each question uses one forward pass; questions run independently in batches of eight.
Candidate head · Vector architecture · Inference code
Adapted from Qwen3.5-2B . It evaluates supplied evidence without live retrieval; confidence does not guarantee correctness. License · Attributions .
Runs of llm-semantic-router Decision-1.0-Sol on huggingface.co
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Updated:March 06 2026
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