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Models

Deploy and scale models on your GPU infrastructure of choice with NVIDIA NIM inference microservices

Optimized by NVIDIALaunch from Hugging FaceBeta

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  • nemo retriever
    • 8 models
    NVIDIA
    Downloadable

    llama-nemotron-embed-1b-v2

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Text-to-Embedding
    545K
    2w
    NVIDIA
    Downloadable

    llama-nemotron-embed-vl-1b-v2

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    nemo retriever
    1.62M
    1mo
    NVIDIA
    Downloadable

    llama-3_2-nemoretriever-300m-embed-v2

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Text-to-Embedding
    75.34K
    5mo
    NVIDIA
    Free Endpoint

    llama-3_2-nemoretriever-300m-embed-v1

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Text-to-Embedding
    106K
    8mo
    NVIDIA
    Downloadable

    llama-3.2-nemoretriever-1b-vlm-embed-v1

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    nemo retriever
    384K
    8mo
    NVIDIA
    Free Endpoint

    nv-embedcode-7b-v1

    The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
    nemo retriever
    265K
    9mo
    NVIDIA
    Downloadable

    llama-3.2-nv-embedqa-1b-v2

    Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
    nemo retriever
    6.23M
    8mo
    NVIDIA
    Downloadable

    nv-embedqa-e5-v5

    English text embedding model for question-answering retrieval.
    Embedding
    4.41M
    8mo
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