About
This model was quantized to BF16 and converted to Omni from vllm-sr/Decision-2.0-Nox-4B. Refer to the original model card for more details on the model.
It's like Nox but like Omni with identical text decisions as Nox and very competitive on Images, Audio and Video multimodal decisions. I don't know how to run benchies but feel free to benchmark if you feel like it.
Original Model Card
Decision-2.0-Nox-4B
Decision-2.0-Nox-4B is the 4B 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.
| Parameters | 4.21B |
| Context length | 16,384 tokens |
| Decision types | Choice · Yes / No · Score |
| License | Apache-2.0 |
Highlights
- Top JevArena score of its size: 63.6 among the 4 same-size models compared, statistically level with Decider 4B (61.9).
- Ahead of Decision 1.0 Nox: +7.1 on JevArena and +9.4 on the Jev Decision Index.
- Speed: a median of 12.9 ms per single-question request on a single GPU.
- Many questions, one pass: Choice, Yes / No and Score questions about the same input are answered together in one forward pass, with a probability for every option.
Quickstart
pip install "transformers>=5.17" torch safetensors
import json
from transformers import AutoModel
model = AutoModel.from_pretrained("vllm-sr/Decision-2.0-Nox-4B", 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-Nox-4B", trust_remote_code=True)(state=..., questions=...)
Evaluation
| Model | JevArena ↑ | Human-labelled transfer ↑ | Jev Decision Index ↑ |
|---|---|---|---|
| Decision-2.0-Nox-4B | 63.6 | 52.3 | 43.8 |
| Decider 4B | 61.9 | 55.5 | — |
| Jet v6.2 | 60.4 | 53.8 | — |
| Decision 1.0 Nox | 56.5 | 51.9 | 34.4 |
JevArena
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).
Jev Decision Index
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.
License
Apache-2.0 (LICENSE).
Citation
@misc{decision_2_0_nox_4b_2026,
title = {{Decision-2.0-Nox-4B}: 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-Nox-4B}}
}