Feature Extraction
Transformers
Safetensors
llama_bidirec
text
text-embedding
retrieval
semantic-search
transformer
custom_code
text-embeddings-inference
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", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/llama-nv-embed-reasoning-3b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b7b9a0e3b1b297e87e65c7f50ee428f506f1f0955a0f2bd8b36c8c5ab285af7
- Size of remote file:
- 4.97 GB
- SHA256:
- b0d81543f68b53678fbc7542b18c025dd7cdc216860bb09f50f378937d13a473
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