Sentence Similarity
PyTorch
sentence-transformers
multimodal
embeddings
retrieval
image-text
audio-text
text-image-audio
tri-encoder
semantic-router
Eval Results (legacy)
Instructions to use vllm-sr/multi-modal-embed-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vllm-sr/multi-modal-embed-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vllm-sr/multi-modal-embed-large") 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 model.pt from vllm-sr/multi-modal-embed-large: direct link, hf CLI and curl.
- Browser
- Download file 6.39 GB
-
https://huggingface.co/vllm-sr/multi-modal-embed-large/resolve/main/model.pt
- Command line
-
hf download hf://vllm-sr/multi-modal-embed-large/model.pt
-
curl -L -o model.pt https://huggingface.co/vllm-sr/multi-modal-embed-large/resolve/main/model.pt
6.39 GB
- Xet hash:
- 9d3def260e8915772a0f4c9fcce0c45cd3f76a6d26ed93a1c85321c1b00ebd25
- Size of remote file:
- 6.39 GB
- SHA256:
- f5fe61d4864fffb703f53860234a657a2f51f71e393e2dc1b7f635b284cb48c4
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