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    Models/Reranking/nvidia/llama-nemotron-rerank-vl-1b-v2
    HFRerankingother

    llama-nemotron-rerank-vl-1b-v2

    by nvidia

    Visual multimodal reranker that rescores text, image, and document results

    49Kdl/month
    60likes
    1.7Bparams
    Identifiers
    Model ID
    nvidia/llama-nemotron-rerank-vl-1b-v2
    Feature URI
    mixpeek://reranker@v1/nvidia_nemotron_rerank_vl_1b_v2

    Deploy llama-nemotron-rerank-vl-1b-v2

    Single-tenant

    Mixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.

    Overview

    Llama-Nemotron Rerank VL extends NVIDIA's reranking to visual modalities. Built on SigLIP 2 (400M vision) + Llama 3.2 (1B language), it reranks results that contain images, document pages, or mixed text-image content. It pairs naturally with nvidia/llama-nemotron-embed-vl-1b-v2 for a complete two-stage multimodal retrieval pipeline.

    On Mixpeek, the visual reranker improves precision for document retrieval, product search, and any pipeline where visual content matters. After embedding-based retrieval returns top candidates, the reranker applies full cross-attention between the query and each candidate's visual+textual content.

    Architecture

    SigLIP 2 (400M) vision encoder + Llama 3.2 (1B) language model. 1.7B total params. Processes text-only, image-only, and mixed text+image inputs. Outputs relevance probability via cross-encoder scoring.

    Mixpeek SDK Integration

    // Reranking is a retriever STAGE in Mixpeek, not an ingest-time extractor.
    // The rerank stage runs a cross-encoder inference service; the shipped default
    // is BAAI/bge-reranker-v2-m3. Pointing it at llama-nemotron-rerank-vl-1b-v2 means registering that
    // model as a custom reranker plugin and naming it in feature_uri, which is an
    // Enterprise path. Stage contract read from GET /v1/discovery/stages.
    const retriever = await mx.retrievers.create({
      namespace_id: "my-namespace",
      retriever_name: "search-then-rerank",
      stages: [
        {
          stage_name: "candidates",
          stage_id: "feature_search",
          parameters: { limit: 100 },
        },
        {
          stage_name: "rerank_results",
          stage_id: "rerank",
          parameters: {
            inference_name: "BAAI__bge_reranker_v2_m3",
            query: "{{INPUT.query}}",
            document_field: "content",
            top_k: 10,
          },
        },
      ],
    });

    Capabilities

    • Multimodal reranking: text, image, and mixed document results
    • 1.7B parameters: efficient for production deployment
    • Pairs with nemotron-embed-vl for two-stage multimodal retrieval
    • +6-7% Recall@5 improvement over embedding-only retrieval
    • Evaluates on ViDoRe V1/V2/V3 document benchmarks

    Use Cases on Mixpeek

    Document retrieval: rerank scanned pages by visual+textual relevance
    Product search: rerank product images by visual similarity to query
    Mixed-media search: rerank results combining text and visual content
    Visual QA pipelines: improve retrieval precision before VLM generation

    Benchmarks

    DatasetMetricScoreSource
    Multimodal (text query)Recall@5 improvement+7.2%NVIDIA, 2025: arxiv,2501.14818
    Multimodal (image query)Recall@5 improvement+6.9%NVIDIA, 2025: arxiv,2501.14818
    Multimodal (image+text)Recall@5 improvement+6.0%NVIDIA, 2025: arxiv,2501.14818

    Performance

    Input SizeText + Image (mixed)
    GPU Latency~8ms / pair (A100)
    GPU Throughput~125 pairs/sec (A100)
    GPU Memory~4 GB

    Specification

    FrameworkHF
    Organizationnvidia
    FeatureReranking
    OutputRelevance score per candidate
    Modalities
    RetrieverCross-Modal Reranker
    Parameters1.7B
    Licenseother
    Downloads/mo49K
    Likes60

    Research Paper

    Nemotron Multimodal Retrieval

    arxiv.org

    Build a pipeline with llama-nemotron-rerank-vl-1b-v2

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