Model reference · open weights
Decision-2.0-Eos is an open-weight embedding model from vllm-sr. Decision-2.0-Eos-0.8B (BF16) weighs 4 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the vllm-sr/Decision-2.0-Eos-0.8B model card, 2026-10-07
What it is
| Released by | vllm-sr |
|---|---|
| Released | 2026-09-28 |
| VRAM | 4 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
Decision-2.0-Eos-0.8B is the 0.8B 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 | 0.75B |
| Context length | 16,384 tokens |
| Decision types | Choice · Yes / No · Score |
| License | Apache-2.0 |
pip install "transformers>=5.17" torch safetensors
import json
from transformers import AutoModel
model = AutoModel.from_pretrained("vllm-sr/Decision-2.0-Eos-0.8B", 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-Eos-0.8B", trust_remote_code=True)(state=..., questions=...)
| Model | JevArena ↑ | Human-labelled transfer ↑ | Jev Decision Index ↑ |
|---|---|---|---|
| Decision-2.0-Eos-0.8B | 53.9 | 50.3 | 20.1 |
| Intern-Decision-0.8B | 43.5 | 38.2 | — |
| Kev-0.8B | 43.2 | 39.0 | — |
| Decision 1.0 Eos | 42.5 | 46.1 | 18.4 |
Apache-2.0 (LICENSE).
@misc{decision_2_0_eos_0_8b_2026,
title = {{Decision-2.0-Eos-0.8B}: 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-Eos-0.8B}}
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.