llama-nemotron-rerank-vl-1b-v2
by nvidia
Visual multimodal reranker that rescores text, image, and document results
nvidia/llama-nemotron-rerank-vl-1b-v2mixpeek://reranker@v1/nvidia_nemotron_rerank_vl_1b_v2Deploy llama-nemotron-rerank-vl-1b-v2
Single-tenantMixpeek 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
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| 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
Common Pipeline Companions
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Specification
Research Paper
Nemotron Multimodal Retrieval
arxiv.orgBuild a pipeline with llama-nemotron-rerank-vl-1b-v2
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