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Decision-2.0-Eos-0.8B
The 0.8B model in vLLM Semantic Router's Decision 2.0 family: a 0.75B fine-tune of Decision-1.0-Eos-0.8B with a 16,384-token context, reporting JevArena 53.9 and a 6.0 ms median per single-question request.
- Reading time
- 2 min
- Published by
- Community
- Author
- vLLM Semantic Router
- Last verified
- Oct 2026
- Capabilities
- Open sourceSelf-hostedRuns locally
Highlights
- 0.75B parameters with a 16,384-token context, Apache-2.0, fine-tuned from vllm-sr/Decision-1.0-Eos-0.8B.
- Card reports JevArena 53.9, ahead of the 3 other same-size models compared.
- +11.4 JevArena and +1.7 Decision Index over Decision 1.0 Eos; human-labelled transfer 50.3.
- Median 6.0 ms per single-question request on a single GPU, all question types in one forward pass.
- Same-size table: Intern-Decision-0.8B 43.5 and Kev-0.8B 43.2 on JevArena.
Quickstart
python
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"}},
"receipt": {"type": "noul", "instructions": "Does the customer have a receipt?"}},
)
print(json.dumps(result["answers"], indent=2))Watch out
Card reviewed 2026-10-17; the JevArena 53.9, Decision Index 20.1 and 6.0 ms median are self-reported, competitor rows come from a public board snapshot dated 2026-09-28, and no GPU model is given for the timing.
Questions about this entry
Short answers drawn from the linked project or page.
The 0.8B model of Decision 2.0 from vLLM Semantic Router, a 0.75B fine-tune of vllm-sr/Decision-1.0-Eos-0.8B that answers choice, yes/no and score questions and returns a probability for every option without generating text.
The card reports +11.4 JevArena and +1.7 Jev Decision Index over Decision 1.0 Eos, which scored 42.5 and 18.4 against this model's 53.9 and 20.1. Both figures are the publisher's own reproduction.
JevArena 53.9, ahead of the three other same-size models compared, with human-labelled transfer 50.3 and Jev Decision Index 20.1, all self-reported on the card.
A median of 6.0 ms per single-question request on a single GPU, with all question types of a request answered in one forward pass; the card does not name the GPU used.
0.75B parameters and a 16,384-token context under Apache-2.0, with the zero-shot-classification pipeline tag.
Use transformers 5.17 or newer: AutoModel.from_pretrained('vllm-sr/Decision-2.0-Eos-0.8B', trust_remote_code=True), then call system_one with a state and typed questions; a 'decision' pipeline call also works.
The table shows Intern-Decision-0.8B at 43.5 and Kev-0.8B at 43.2 JevArena against this model's 53.9; those competitor values come from the public board snapshot dated 2026-09-28.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Hacker News: ASTGL replay chose local Laya routing over hosted Jev on one Mac Studio setup.
Jmeg8r15 pointsDoes an open-weight decision model beat a hosted one? Jev vs. LayaRead the thread on Hacker NewsReddit: A walkthrough of routing between models with Jev.
X: Standard One SH (StandardThinking)
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