Vela-2.0-0.3B

Vela 2.0 0.3B

Open Foundation Routing Models

Routing decisions. Safety checks. Precise text spans.

Vela 2.0 brings routing questions and span-level decisions into one model interface. Supply your options, labels and rubrics at request time; ask about the request, its context and the answer together.

The 0.3B is the family's compact multilingual encoder, with an 8,192-token input budget and Torch or ONNX inference on CPU or GPU.

1. Route with your own criteria. Choose a route, check a policy condition, assign a score or select multiple labels.
2. Locate the text behind a signal. Return spans of personal information and unsupported claims with character offsets and probabilities.
3. Ask across the whole interaction. Use typed request, context and answer parts in one request, through Python or a SystemOne-compatible HTTP API.

Quickstart

Ask for personal information, unsupported answer text and a route in one request:

pip install torch "transformers>=4.57" safetensors tokenizers numpy

from transformers import AutoModel

m = AutoModel.from_pretrained("vllm-sr/Vela-2.0-0.3B", trust_remote_code=True)
# Runs on CPU by default; optionally use m = m.to("cuda"). Keep Torch loading in fp32.
PII_LABELS = m.vela2_engine.cal["pii_schema"]["labels"]

result = m.system_one(
    state={"request": "Hi, I'm Tom Baker ([email protected]). What is the maximum daily dose of paracetamol for an adult?",
           "source": "For adults, the maximum dose of paracetamol is 4 grams in 24 hours, taken as 500 mg to 1 g every 4 to 6 hours.",
           "answer": "Adults can take up to 6 grams of paracetamol in 24 hours, in doses of 500 mg to 1 g every 4 to 6 hours."},
    questions={
        "pii": {"type": "span", "instructions": "Which spans are personal information?", "criteria": PII_LABELS,
                "over": "request"},
        "halu": {"type": "span", "instructions": "Which spans of the answer are not supported by the context?",
                 "criteria": {"unsupported": "a claim not supported by the context"}},   # over the answer by default
        "domain": {"type": "choice", "instructions": "Which subject area is this request about?", "over": "request",
                   "criteria": {"health": "medicine, clinical practice, nutrition, ageing or sexual health",
                                "math": "arithmetic, algebra, geometry, statistics or other mathematics",
                                "other": "a subject that fits none of the listed areas"}},
    })

Selected fields from the recorded Torch CPU response, with probabilities rounded to three decimals:

{
  "answers": {
    "pii": {"type": "noul", "noul": 1.0},
    "halu": {"type": "noul", "noul": 0.984},
    "domain": {"type": "choice", "choice": "health", "confidence": 0.51, "probabilities": {"health": 0.727, "math": 0.217, "other": 0.056}}
  },
  "spans": {
    "pii": [{"label": "PERSON", "start": 8, "end": 17, "text": "Tom Baker", "probability": 1.0}, {"label": "EMAIL_ADDRESS", "start": 19, "end": 40, "text": "[email protected]", "probability": 0.983}],
    "halu": [{"label": "unsupported", "start": 16, "end": 29, "text": "up to 6 grams", "probability": 0.895}]
  }
}

Offsets are Unicode code-point offsets into the targeted field. Full response, HTTP, SDK and ONNX examples.

Questions and outputs

All five types share one interface. Span and Set preserve SystemOne compatibility through additional response fields and Noul views. Request and response reference.

Results

• Selection: checkpoint selection used dev splits; final release selection also considered test results.
• Safety: these task families were part of Vela's training, unlike Decide's. This comparison measures router safety performance, rather than zero-shot generalisation.

Selected results above use identical rows for each task comparison. Full results and evaluation protocol include every benchmark, confidence intervals and the separate shipped, test-blind and research PII calibration paths.

Architecture

A 22-layer bidirectional ModernBERT encoder with schema-conditioned decision and word–label span readouts. The first attention pre-norm is Identity; this diagram shows the SDPA path. Editable SVG.

The backbone reads schema and typed state bidirectionally. Decision heads combine option cosine scores with an option MLP; the span head compares word and label states. Model, calibration and training details.

Reference

• Usage: Python, HTTP, SDK, Span/Set, typed parts, shortcuts and ONNX.
• Evaluation: full tables, scoring paths, references and selection disclosures.
• Training and model details: data, training recipe, input schema, calibration and repository files.
• Export parity: Torch/research-scorer and ONNX comparisons on the measured rows.

Credit and citation

Vela 2.0 is led by KR Labs and vLLM Semantic Router.

Read the Vela 2.0 technical overview.

@misc{vela2_unified_2026,
  title        = {Vela 2.0: Towards Open Foundation Routing Models},
  author       = {{KR Labs} and {vLLM Semantic Router}},
  year         = {2026},
  note         = {Blog post. Model: vllm-sr/Vela-2.0-0.3B},
  howpublished = {\url{https://vllm-sr.ai/blog/vela-2-0-open-foundation-routing-models/}}
}

Licence

Apache-2.0 for this model's weights, code and documentation. It is derived from Decision-1.0-Kai-0.6B (Apache-2.0) and Vela-1.0-Encoder-307M (MIT, from mmBERT); their licences and notices are passed on in LICENSE, NOTICE, NOTICE_SOURCES.json and LICENSES/. The tokenizer carries the Gemma Terms of Use: DISTRIBUTION_TERMS.md · License scope. Changes against Kai and Vela, including the eight added tokenizer entries: MODIFICATIONS.md. Training data keep their own licences, some of them CC-BY-SA share-alike (see NOTICE_SOURCES.json).

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