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:
- fe7995652886bb9213808e627d4cba03fc5a07af0e74c4f4329f234f217cc28d
- Size of remote file:
- 1.46 GB
- SHA256:
- 2455e72005b09aa32513e3b396451da996f9f84a259c4e3bd9b9512c26d08378
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