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Decision 2.0 Lux 9B
The 9B entry in vLLM Semantic Router's Decision 2.0 family: a 7.94B decision model with a 16,384-token context that answers choice, yes/no and score questions in one pass, reporting 68.1 JevArena and 18.4 ms median single-question latency.
- Reading time
- 2 min
- Published by
- Community
- Author
- vLLM Semantic Router
- Last verified
- Oct 2026
- Capabilities
- Open sourceSelf-hostedRuns locally
Highlights
- 7.94B-parameter decision model with a 16,384-token context; Choice, Yes/No and Score questions about one input are answered in a single forward pass.
- Card reports JevArena 68.1, human-labelled transfer 56.2 and Jev Decision Index 46.3, ahead of Decision 1.0 Lux by 2.3 and 2.8 points.
- Median 18.4 ms per single-question request on a single GPU, per the card's speed line.
- The 9B member of the Decision 2.0 collection from the vLLM Semantic Router team; loads with AutoModel.from_pretrained and trust_remote_code=True.
- Apache-2.0; the card describes Decision 2.0 as an independent reproduction with the official 0.2.1 kit, audited row-level against all Index test items.
Quickstart
bash
pip install "transformers>=5.17" torch safetensorsWatch out
Card reviewed 2026-10-17; the JevArena 68.1, transfer 56.2 and Decision Index 46.3 figures are self-reported on the card, with Decision 2.0 described as an independent reproduction using the official 0.2.1 kit and the comparison rows drawn from a public board snapshot dated 2026-09-28. The median 18.4 ms latency gives no GPU model, batch size or measurement method.
Questions about this entry
Short answers drawn from the linked project or page.
The 9B decision model in Decision 2.0, the decision-model family from the vLLM Semantic Router project. It takes an input (text or JSON) and typed questions, answering them all at once with a probability for every answer and no generated text.
It is an adjacent decision model evaluated on Jev-named benchmarks: JevArena, human-labelled transfer and the Jev Decision Index. Like Jev it returns probabilities for typed Choice, Yes/No and Score questions instead of generating prose, and the card says it has the top JevArena score of its size.
Choice between named options, Yes/No and Score on an ordered scale, all in one forward pass, with a 16,384-token context length and 7.94B parameters, per the card's specification table.
pip install 'transformers>=5.17' torch safetensors, then AutoModel.from_pretrained('vllm-sr/Decision-2.0-Lux-9B', trust_remote_code=True) and call model.system_one(state=..., questions=...); the card also shows a transformers.pipeline('decision', ...) form.
JevArena 68.1, human-labelled transfer 56.2 and Jev Decision Index 46.3, against Decision 1.0 Lux at 65.8, 55.8 and 43.5 and Nimble v2 at 62.1 and 53.6. The card says missing or invalid answers counted as errors.
It is a fine-tune of vllm-sr/Decision-1.0-Lux-9B and the card reports gains of 2.3 points on JevArena (68.1 versus 65.8) and 2.8 points on the Jev Decision Index (46.3 versus 43.5).
Apache-2.0, as stated in the card's specification table and repository licence file. The card also provides a BibTeX citation crediting the vLLM Semantic Router Team.
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)
Facebook: Intake Router keeps diagnosis out of the answer space at the front desk.
FacebookClinicnotes
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