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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BAAI bge-reranker-v2.5-gemma2-lightweight