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14 results for

Filters (1)

  • Download Available
    7
  • Launchable
    5
  • Enterprise
    2
  • API Endpoint
    2
  • Retrieval Augmented Generation
    9
  • Text-to-Embedding
    7
  • Image-to-Text
    0
  • NVIDIA
    13
  • Cyborg
    1
  • Meta
    0
  • BAAI
    0
  • NVIDIA AI
    4
  • nemo retriever
  • Cyborg
    Launchable

    Cyborg Enterprise RAG

    Securely extract, embed, and index multimodal data with encryption in-use for fast, accurate semantic search.
    Blueprint
    NIM
    3w
    NVIDIA
    Launchable

    Multi-Agent Intelligent Warehouse

    An AI-powered, multi-agent system designed to optimize warehouse operations through intelligent automation, real-time monitoring, and natural language interaction.
    Blueprint
    nemo retriever
    3w
    NVIDIA
    Launchable

    Retail Shopping Assistant

    Elevate Shopping Experiences Online and In Stores.
    Blueprint
    nemo retriever
    3w
    NVIDIA
    LaunchableEnterprise

    Build an AI Agent for Enterprise Research

    Build a custom enterprise research assistant powered by state-of-the-art models that process and synthesize multimodal data, enabling reasoning, planning, and refinement to generate comprehensive reports.
    Blueprint
    NIM
    3w
    NVIDIA
    LaunchableEnterprise

    Build an Enterprise RAG Pipeline Blueprint

    Power fast, accurate semantic search across multimodal enterprise data with NVIDIA’s RAG Blueprint—built on NeMo Retriever and Nemotron models—to connect your agents to trusted, authoritative sources of knowledge.
    Blueprint
    NIM
    3w
    NVIDIA

    nv-embedqa-e5-v5

    English text embedding model for question-answering retrieval.
    Model
    Embedding
    3.14M
    7mo
    NVIDIA

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

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    Model
    nemo retriever
    270K
    8mo
    NVIDIA

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

    Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
    Model
    nemo retriever
    166K
    7mo
    NVIDIA

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

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Model
    Retrieval Augmented Generation
    90.75K
    7mo
    NVIDIA

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

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Model
    Retrieval Augmented Generation
    122K
    5mo
    NVIDIA

    llama-nemotron-embed-1b-v2

    Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
    Model
    Retrieval Augmented Generation
    327K
    1w
    NVIDIA

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

    Multimodal question-answer retrieval representing user queries as text and documents as images.
    Model
    nemo retriever
    818K
    4w
    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.
    Model
    nemo retriever
    7.11M
    7mo
    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.
    Model
    nemo retriever
    263K
    9mo
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