BAAI bge-reranker-v2.5-gemma2-lightweight
BAAI's bge-reranker-v2.5-gemma2-lightweight is a lightweight reranking model based on Google Gemma 2 architecture, released in November 2024. The model maintains high performance while significantly reducing computational resource requirements, enabling efficient operation on consumer-grade GPUs and even CPUs.
Core Features
- Lightweight: 2.6B parameters - significantly fewer than large models
- Consumer hardware: Runs on RTX 3060, GTX 1080Ti and similar GPUs
- Chinese-English optimized: Deep optimization for Chinese and English
- C-MTEB SOTA: State-of-the-art on C-MTEB reranking tasks
- Gemma 2 based: Built on Google's latest Gemma 2 architecture
- Apache 2.0: Fully open-source
Performance
- C-MTEB Reranking: #1 ranking
- Context length: 8192 tokens
- Inference speed: 3-5x faster than 7B+ models
- Memory: Only 4-6GB VRAM/RAM required
Quick Start
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', use_fp16=True)
scores = reranker.compute_score([[query, doc1], [query, doc2]])
Best For
✅ Chinese-focused applications ✅ Resource-constrained environments ✅ Cost-sensitive projects ✅ Edge deployment ✅ Fast response requirements
Alternatives
- BGE-reranker-large: Need higher accuracy with more GPU resources
- Jina Reranker v3: Need broad multilingual support
- Cohere Rerank v3.5: Need managed service
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