NVIDIA nv-rerankqa-mistral-4b-v3
NVIDIA's nv-rerankqa-mistral-4b-v3 is a reranking model optimized specifically for Question-Answering (Q&A) scenarios, released in December 2024. The model's standout feature is its support for 32768 tokens of ultra-long context with NVIDIA TensorRT acceleration, achieving excellent performance on QA reranking tasks.
Core Features
- 32768 tokens context: Industry-leading, 4x most models
- QA-optimized: Specifically trained for question-answering
- MRR@10: 0.82 on QA reranking tasks
- TensorRT: 2-3x speedup on NVIDIA GPUs
- Mistral-based: 4B parameters for efficiency-performance balance
- Low latency: Sub-100ms on A100/H100
Performance
- QA Reranking: MRR@10: 0.82, NDCG@10: 0.78
- Inference: 50-80ms latency, 200+ QPS throughput (A100)
- Long documents: Exceptional on >8K token documents
Quick Start
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('nvidia/nv-rerankqa-mistral-4b-v3')
tokenizer = AutoTokenizer.from_pretrained('nvidia/nv-rerankqa-mistral-4b-v3')
pairs = [[query, answer] for answer in candidates]
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
scores = model(**inputs).logits.squeeze()
Best For
✅ QA systems ✅ NVIDIA A100/H100 GPU users ✅ Long document processing (technical, legal, medical) ✅ Low-latency real-time QA ✅ Enterprise knowledge QA
Not Suitable For
❌ General reranking (consider general models) ❌ Without NVIDIA GPUs (can't leverage TensorRT) ❌ Strong multilingual needs (primarily English-optimized) ❌ Very limited budget (requires high-end GPUs)
Alternatives
- Voyage Rerank 2: General RAG, 16K context, managed service
- Cohere Rerank v3.5: General scenarios, API
- Jina Reranker v3: Multilingual needs
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