llama-nemotron-rerank-1b-v2
by nvidia
Lightweight 1B text reranker for production retrieval pipelines
nvidia/llama-nemotron-rerank-1b-v2mixpeek://reranker@v1/nvidia_nemotron_rerank_1b_v2Overview
Llama-Nemotron Rerank 1B is NVIDIA's lightweight cross-encoder reranker based on Llama-3.2-1B. At 1B parameters, it is 3.5x smaller than mistral-based rerankers while delivering competitive quality across 26 languages and 8192-token context. It supports true/false relevance scoring: the model outputs the probability that a document is relevant to a query.
On Mixpeek, this reranker slots into the second stage of retrieval pipelines: after a fast embedding-based first stage returns top-K candidates, the reranker rescores each candidate with full cross-attention between query and document, significantly improving precision.
Architecture
Cross-encoder based on Llama-3.2-1B. Outputs relevance probability via true/false token logits. 8192-token max context. 26-language support. Trained with progressive curriculum on diverse relevance data.
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-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
- 1B parameters: 3.5x smaller than 4B rerankers
- 26-language support including CJK and European languages
- 8192-token context for long document reranking
- Cross-lingual reranking (86.83% on MLQA)
- Production-grade latency on modest hardware
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| NQ + HotpotQA + FiQA + TechQA | Recall@5 | 73.64% | NVIDIA, 2026: Model Card |
| MLQA (cross-lingual, 7 langs) | Recall@5 | 86.83% | NVIDIA, 2026: Model Card |
| MLDR (long docs, 13 langs) | Recall@5 | 70.69% | NVIDIA, 2026: Model Card |
Performance
Common Pipeline Companions
Explore on Mixpeek
Compare alternatives in this category
Hand-picked tools & platforms compared
Deep-dive technical guide
See how Mixpeek runs models as extractors
Store & search embeddings at scale
Usage-based pricing for pipelines
Compare models, APIs & infrastructure
Specification
Research Paper
NVIDIA NeMo Retriever Reranking
arxiv.orgBuild a pipeline with llama-nemotron-rerank-1b-v2
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Run it on your own data, free