3 篇博文 含有标签「vela」

Name it. Vela finds it. Decisions and the spans you name, in one call.

Vela 3.0 is a family of six open models for the vLLM Semantic Router, from a 0.3B encoder that reads images to a 27B model. Each one reads a request once and returns the router's decisions and the exact spans you name: personal data, unsupported claims, evidence, entities and relations. Its default router, vela3-fast, is a 380M UltraBERT encoder with the highest public Decision Index of any model up to 500M parameters: 50.36. Across the family, entities are at GLiNER level, and from 4B up classification and relations are at or above every model the GLiFormer paper reports, prompted Gemma-4-31B, GPT-OSS-120B and GPT-5-mini included.

Vela 3.0 is the next step from Vela 2.0: the same system_one interface, scaled at once in backbone, decisions, sizes, modality, languages and tasks.

Explore the six models → · Read about Vela 2.0 →

Open Foundation Routing Models. Bringing span-level decisions to System One.

Vela 2.0 is a family of open models for routing, safety checks and span-level decisions. Define your options, labels and rubrics at request time; get structured answers with probabilities and character-offset spans through one SystemOne-compatible interface.

Explore the four models → · Read about Decision →

Today, we're excited to release Vela 1.0: 14 open models built for a world of many models.

Vela helps systems understand requests, protect sensitive information, find better context, and connect text, images, and audio. It is the model family we're building for vLLM Semantic Router, and a new foundation for everyone turning a collection of models into a capable system.

Explore all 14 models → · Try in Vela Studio →