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dusersad12 / VertexNova-PreviewRepo
Model's Last Updated: September 29 2026
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Introduction of VertexNova-PreviewRepo
Model Details of VertexNova-PreviewRepo
VertexNova
1. Introduction
VertexNova is the newest snapshot in our open-weight language model series, post-trained with a longer reasoning curriculum and a denser preference-optimization schedule than everything we shipped before. Across math, code and general knowledge probes it now sits comfortably ahead of our earlier releases, and it closes most of the remaining gap to several frontier-scale models while staying small enough to serve on a single accelerator.
The headline change is reasoning depth. On an internal AIME-style probe the pass rate moved from 61.9% in the previous snapshot to 84.6%, and average token usage per question grew from roughly 9K to 21K, which tells us the model actually spends its extra compute thinking. We also recorded fewer unsupported factual claims and noticeably steadier tool use in agent-style evaluations.
2. Evaluation Results
Comprehensive Benchmark Results
| Benchmark | ModelA | ModelB | ModelA-v2 | VertexNova | |
|---|---|---|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.521 | 0.548 | 0.664 | 0.691 |
| Logical Reasoning | 0.589 | 0.612 | 0.726 | 0.750 | |
| Common Sense | 0.604 | 0.655 | 0.713 | 0.742 | |
| Language Understanding | Reading Comprehension | 0.556 | 0.584 | 0.690 | 0.713 |
| Question Answering | 0.518 | 0.546 | 0.660 | 0.683 | |
| Text Classification | 0.623 | 0.651 | 0.742 | 0.768 | |
| Sentiment Analysis | 0.572 | 0.599 | 0.674 | 0.690 | |
| Generation Tasks | Code Generation | 0.537 | 0.564 | 0.668 | 0.691 |
| Creative Writing | 0.566 | 0.549 | 0.673 | 0.698 | |
| Dialogue Generation | 0.574 | 0.608 | 0.704 | 0.729 | |
| Summarization | 0.593 | 0.629 | 0.694 | 0.717 | |
| Specialized Capabilities | Translation | 0.602 | 0.641 | 0.703 | 0.728 |
| Knowledge Retrieval | 0.527 | 0.559 | 0.661 | 0.686 | |
| Instruction Following | 0.611 | 0.637 | 0.714 | 0.739 | |
| Safety Evaluation | 0.614 | 0.592 | 0.706 | 0.730 |
Overall Performance Summary
VertexNova posts its largest gains on the reasoning-adjacent categories, and it stays competitive on every generation-oriented task in the suite.
3. Chat Website & API Platform
An interactive demo plus hosted endpoints for VertexNova are available through our project page; bring your own API key and the usual chat completions interface works out of the box.
4. How to Run Locally
VertexNova loads through the standard transformers AutoModel/AutoTokenizer interface — no custom code is required. We recommend greedy decoding with temperature 0.6 for math-heavy workloads, and a short system prompt that states the current date for time-sensitive queries.
5. License
The VertexNova weights and the accompanying evaluation code are released under the Apache 2.0 license.
6. Contact
Questions and bug reports are welcome on our issue tracker, or reach the team at [email protected] .
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Updated:September 28 2026
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