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] - Notebooks
- Google Colab
- Kaggle
Download onnx/model_fa_layer_11.onnx from vllm-sr/Vela-1.0-Encoder-307M-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 78.6 kB
-
https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Embedding/resolve/main/onnx/model_fa_layer_11.onnx
- Command line
-
hf download hf://vllm-sr/Vela-1.0-Encoder-307M-Embedding/onnx/model_fa_layer_11.onnx
-
curl -L -o model_fa_layer_11.onnx https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M-Embedding/resolve/main/onnx/model_fa_layer_11.onnx
78.6 kB
- Xet hash:
- 6d7616e084c1edcc4cee9c9086e1b21d24fc892818007b9c92fb0ff41b52ac17
- Size of remote file:
- 78.6 kB
- SHA256:
- 643c64c83b1821b876b01324db4a12ad1a45db0fee1865195439b0006c8c12e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.