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nvidia
/
llama-embed-nemotron-8b

Feature Extraction
sentence-transformers
Safetensors
Transformers
multilingual
llama_bidirec
text
sentence-similarity
mteb
mmteb
custom_code
text-embeddings-inference
Model card Files Files and versions
xet
Community
12

Instructions to use nvidia/llama-embed-nemotron-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use nvidia/llama-embed-nemotron-8b with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("nvidia/llama-embed-nemotron-8b", 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]
  • Transformers

    How to use nvidia/llama-embed-nemotron-8b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="nvidia/llama-embed-nemotron-8b", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("nvidia/llama-embed-nemotron-8b", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
llama-embed-nemotron-8b
15 GB
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  • 4 contributors
History: 3 commits
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ybabakhin
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  • .ipynb_checkpoints
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  • .gitattributes
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  • LICENSE
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  • Notice.txt
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  • README.md
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  • config.json
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  • llama_bidirectional_model.py
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  • model-00001-of-00004.safetensors
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  • model-00002-of-00004.safetensors
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  • model-00003-of-00004.safetensors
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  • model-00004-of-00004.safetensors
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  • model.safetensors.index.json
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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