Feature Extraction
LiteRT
LiteRT
sentence-transformers
on-device
edge
embeddings
retrieval
rag
multilingual
nemotron
Instructions to use litert-community/Nemotron-3-Embed-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/Nemotron-3-Embed-1B with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use litert-community/Nemotron-3-Embed-1B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("litert-community/Nemotron-3-Embed-1B") 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
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!