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

Measured capability

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

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.

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-Sol-2B 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. The weights do not depend on a particular accelerator; which hardware can serve them is decided by the runtime. 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-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.