Sentence Similarity
Transformers
ONNX
Safetensors
sentence-transformers
English
multimodal
embeddings
image-text
audio-text
retrieval
2DMSE
matryoshka
Eval Results (legacy)
Instructions to use llm-semantic-router/multi-modal-embed-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-semantic-router/multi-modal-embed-small with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("llm-semantic-router/multi-modal-embed-small", dtype="auto") - sentence-transformers
How to use llm-semantic-router/multi-modal-embed-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("llm-semantic-router/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] - Notebooks
- Google Colab
- Kaggle