Sentence Similarity
sentence-transformers
ONNX
Safetensors
modernbert
semantic-router
vela
matryoshka
text-embeddings-inference
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vllm-sr/Vela-1.0-Encoder-307M-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vllm-sr/Vela-1.0-Encoder-307M-Embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Publish R3 interim encoder: classification and long retrieval gains with disclosed tradeoffs
Verified signatureAtomic native weights, tokenizer/config, eight standard/CK ONNX graphs and shared weights, model card, and complete bounded evaluation evidence. Native FP32 scores compare primarily with mmBERT. Short retrieval and early-exit regressions remain visible; this is not a full-MMTEB or universal-superiority claim.ae3a92a
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Release Vela on the unified Encoder base
Verified signature4761bf4
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Release Vela 1.0 Embedding
Verified signatureMultilingual retrieval with flexible representations and long-context support.5e639f1