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Cataloged from vllm-sr/Decision-2.0-Vega-27B
All models by vllm-sr →[Image blocked: Decision-2.0-Vega-27B]
Decision-2.0-Vega-27B is the 27B model of Decision 2.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all at once and returns a probability for every answer, without generating text.
[Image blocked: JevArena: Decision-2.0-Vega-27B and same-size models]
[Image blocked: JevArena by decision type: Decision-2.0-Vega-27B and same-size models]
Every model answers the same frozen prompts, scored the same way; missing or invalid answers count as errors. Human-labelled transfer is the median macro-F1 over 15 human-labelled tasks (×100).
[Image blocked: Jev Decision Index against model size]
[Image blocked: Jev Decision Index by area: Decision-2.0-Vega-27B and Decision-2.0-Lux-9B]
Decision 2.0: independent reproduction with the official 0.2.1 kit on the released weights; others: public board snapshot, 2026-09-28. Training data audited at row level against all Index test items.
Apache-2.0 (LICENSE [blocked]).
pip install "transformers>=5.17" torch safetensors peftimport json
from transformers import AutoModel
model = AutoModel.from_pretrained("vllm-sr/Decision-2.0-Vega-27B", trust_remote_code=True)result = model.system_one( state="The order arrived damaged yesterday. The customer has a receipt and asks for a replacement today.", questions={ "route": { "type": "choice", "instructions": "Which team should handle this request?", "criteria": { "returns": "Refunds, replacements and damaged deliveries", "billing": "Payments, invoices and charges", "technical": "Product setup and faults" } }, "receipt": { "type": "noul", "instructions": "Does the customer have a receipt?" }, "urgency": { "type": "score", "instructions": "How urgent is this request?", "criteria": [ "Routine", "Soon", "Today" ] } },)print(json.dumps(result["answers"], indent=2))
# Or as a pipeline:# transformers.pipeline("decision", model="vllm-sr/Decision-2.0-Vega-27B", trust_remote_code=True)(state=..., questions=...)@misc{decision_2_0_vega_27b_2026, title = {{Decision-2.0-Vega-27B}: A Decision 2.0 Model for Structured Decisions}, author = {{vLLM Semantic Router Team}}, year = {2026}, howpublished = {\url{https://huggingface.co/vllm-sr/Decision-2.0-Vega-27B}}}