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    Models/Sentence Similarity/nvidia/llama-nemotron-embed-vl-1b-v2-fp8
    Sentence Similaritysentence-transformersother

    llama-nemotron-embed-vl-1b-v2-fp8

    by nvidia

    Identifier
    Model ID
    nvidia/llama-nemotron-embed-vl-1b-v2-fp8

    Tags

    sentence-transformerssafetensorsllama_nemotron_vlfeature-extractionretrievalvisual document retrievalvlm embeddingpage image embeddingtext embeddingsemantic searchquestion-answering retrievalragtransformersquantizedsentence-similaritycustom_codemultilingualarxiv:2501.14818base_model:nvidia/llama-nemotron-embed-vl-1b-v2base_model:quantized:nvidia/llama-nemotron-embed-vl-1b-v2license:otherendpoints_compatiblemodeloptregion:us

    Use llama-nemotron-embed-vl-1b-v2-fp8 on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, llama-nemotron-embed-vl-1b-v2-fp8 runs as a managed extractor inside a processing pipeline. Point a bucket of sentence similarity data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.