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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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  • Download Available
    8
  • API Endpoint
    2
  • Retrieval Augmented Generation
    10
  • Text-to-Embedding
    7
  • Code Generation
    0
  • Drug Discovery
    0
  • Image-to-Text
    0
  • NVIDIA
    10
  • Meta
    0
  • Mistral AI
    0
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  • Retrieval Augmented Generation
    • 10 models
    NVIDIA

    llama-nemotron-embed-1b-v2

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Retrieval Augmented Generation
    Today
    NVIDIA

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

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    nemo retriever
    535K
    3w
    NVIDIA

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

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Retrieval Augmented Generation
    152K
    5mo
    NVIDIA

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

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Retrieval Augmented Generation
    57.22K
    7mo
    NVIDIA

    llama-3.2-nemoretriever-500m-rerank-v2

    GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.
    nemo retriever
    1.02K
    8mo
    NVIDIA

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

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    nemo retriever
    276K
    8mo
    NVIDIA

    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
    319K
    9mo
    NVIDIA

    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
    6.98M
    7mo
    NVIDIA

    llama-3.2-nv-rerankqa-1b-v2

    Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
    nemo retriever
    145K
    7mo
    NVIDIA

    nv-embedqa-e5-v5

    English text embedding model for question-answering retrieval.
    Embedding
    3.07M
    7mo
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