Bge Reranker Base Q4 K M GGUF
GGUF format re-ranking model converted from BAAI/bge-reranker-base, supporting Chinese and English text sorting tasks
Text Embedding Supports Multiple LanguagesOpen Source License:MIT#Chinese Re-ranking#Medical QA Optimization#Low-resource Inference
Downloads 44
Release Time : 4/1/2025
Model Overview
This model is a quantized version of BAAI/bge-reranker-base, converted to GGUF format using llama.cpp, specifically designed for text re-ranking tasks to improve relevance sorting in retrieval systems
Model Features
Efficient Quantization
Uses Q4_K_M quantization level to significantly reduce resource usage while maintaining model performance
Cross-platform Compatibility
GGUF format supports multiple hardware platforms including CPU and GPU
Bilingual Support
Specifically optimized for processing both Chinese and English texts
Lightweight Deployment
Achieves lightweight deployment through the llama.cpp toolchain
Model Capabilities
Text Relevance Ranking
Retrieval Result Optimization
Cross-language Text Processing
Use Cases
Information Retrieval
Search Engine Result Optimization
Re-ranking search engine results by relevance
Improves relevance scoring of search results
QA Systems
Ranking candidate answers by relevance
Enhances accuracy of QA systems
Content Recommendation
Personalized Recommendations
Ranking recommended content by relevance
Improves precision of recommendation systems
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