Vela-1.0-Encoder-Embedding

Connect requests with the right context. Vela Embedding powers multilingual semantic matching, retrieval, and memory in your router.

307M parameters · 32K context · Multilingual

Evaluation

Compared with the original mmBERT Embedding. Scores are ×100; higher is better.

Selected tasks from MTEB 2.21.0:

Long-document retrieval:

Both models use 22 layers, 768 dimensions and FP32. The MTEB rows use each task's standard main metric; SummScreenFD uses its validation split.

These results describe the listed tasks, not a full MTEB score or ranking.

Quick start

Install sentence-transformers. Use a ROCm-enabled PyTorch build for AMD GPUs.

import torch
from sentence_transformers import SentenceTransformer

model_id = "llm-semantic-router/Vela-1.0-Encoder-307M-Embedding"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SentenceTransformer(model_id, device=device)
vectors = model.encode([
    "The library opens in the morning.",
    "图书馆早上开门。",
])
print(vectors.shape)  # (2, 768)

The default returns 768-dimensional unit vectors. The 32,768-token input limit includes special tokens.

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