Feature Extraction
Transformers
Safetensors
text
text-embedding
retrieval
semantic-search
transformer
Instructions to use nvidia/llama-nv-embed-reasoning-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/llama-nv-embed-reasoning-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/llama-nv-embed-reasoning-3b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/llama-nv-embed-reasoning-3b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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