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
File size: 283 Bytes
a82f367 e6b2249 a82f367 | 1 2 3 4 5 6 7 8 9 | {
"output_dim": 384,
"text_encoder_name": "sentence-transformers/all-MiniLM-L6-v2",
"image_encoder_name": "google/siglip-base-patch16-512",
"audio_encoder_name": "openai/whisper-tiny",
"fusion_type": "transformer",
"num_fusion_layers": 2,
"enable_layer_outputs": true
} |