Feature Extraction
sentence-transformers
Safetensors
English
fusion-embedding-connector
embeddings
multimodal
audio
retrieval
matryoshka
qwen3-vl
adapters
custom_code
Instructions to use EximiusLabs/fusion-embedding-2-2b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EximiusLabs/fusion-embedding-2-2b-preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EximiusLabs/fusion-embedding-2-2b-preview", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Positioning figure: trained-parameters axis (matches the technical report)
Browse files
assets/fe_positioning.png
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