Sentence Similarity
Transformers
ONNX
Safetensors
sentence-transformers
English
multimodal
embeddings
image-text
audio-text
retrieval
2DMSE
matryoshka
Eval Results (legacy)
Instructions to use vllm-sr/multi-modal-embed-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/multi-modal-embed-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vllm-sr/multi-modal-embed-small", device_map="auto") - sentence-transformers
How to use vllm-sr/multi-modal-embed-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vllm-sr/multi-modal-embed-small") 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
Download model.pt from vllm-sr/multi-modal-embed-small: direct link, hf CLI and curl.
- Browser
- Download file 976 MB
-
https://huggingface.co/vllm-sr/multi-modal-embed-small/resolve/main/model.pt
- Command line
-
hf download hf://vllm-sr/multi-modal-embed-small/model.pt
-
curl -L -o model.pt https://huggingface.co/vllm-sr/multi-modal-embed-small/resolve/main/model.pt
976 MB
- Xet hash:
- 5c759de8cfc41a7031f01acb91d9794df72a48233fb19339b4926a90f18c7105
- Size of remote file:
- 976 MB
- SHA256:
- a4e280a185550651d299dfcd10df7e2cd02629c2f0c0b0964122daabe723ef4b
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