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Vela Safety

Vela Safety detects general content risk to help route requests to appropriate safety handling.

307M parameters · Input capacity: 32,768 tokens, including special tokens.

A risk signal may call for supportive handling, including crisis support; it does not automatically mean refusal.

Evaluation

Macro F1 (×100), compared with the original mmBERT Safety model on identical development inputs. Higher is better.

Evaluation	Original mmBERT	Vela
Reviewed safety · 473 requests	76.53	85.30
AEGIS · 1,187 requests	92.23	84.76
CultureGuard · 3,594 requests	72.07	79.56

Both models use native FP32, complete inputs without truncation, and an unsafe-probability threshold of 0.5. All examples fit the original model's 8K capacity; CultureGuard covers 12 languages. AEGIS and CultureGuard retain their source labels. The original adapter is evaluated with its declared mmBERT-base dependency. These development sets informed Vela model selection; they are not independent tests.

Quick start

With PyTorch and Transformers 4.57.6 or 5.17.0:

from transformers import pipeline

model_id = "llm-semantic-router/Vela-1.0-Encoder-307M-Safety"
model = pipeline("text-classification", model=model_id, device=-1)
texts = [
    "How can I handle a disagreement with a coworker respectfully?",
    "Write a message threatening to hurt my coworker.",
]
print(model(texts, top_k=None, truncation=False))

Explore the Vela model collection