Vela 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:
| Task | Original mmBERT | Vela |
|---|---|---|
| Banking77 | 72.26 | 74.04 |
| MASSIVE · English | 63.51 | 68.12 |
| MASSIVE · Chinese | 61.41 | 66.08 |
| DuRetrieval | 62.47 | 62.99 |
| NFCorpus | 26.38 | 26.13 |
| SciFact | 46.81 | 48.28 |
| PAWS-X · English | 62.98 | 71.74 |
| PAWS-X · Chinese | 59.29 | 62.82 |
| STS-B · Chinese | 75.00 | 74.73 |
| STS-B · English | 80.38 | 81.19 |
| CLS Clustering | 34.20 | 34.87 |
| Twenty Newsgroups Clustering | 38.05 | 38.53 |
| Tatoeba · Chinese–English | 89.13 | 89.12 |
Long-document retrieval:
| Evaluation | Metric | Original mmBERT | Vela |
|---|---|---|---|
| SummScreenFD · 336 queries | nDCG@10 | 76.99 | 88.36 |
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 = "vllm-sr/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.