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Jbaron34 Qwen2.5 0.5b Bebop Reranker Gguf

Developed by RichardErkhov
A 0.5B parameter text reranking model based on Qwen2.5 architecture, efficiently trained using Unsloth and TRL libraries
Downloads 2,119
Release Time : 3/13/2025

Model Overview

This is a text reranking model based on the Qwen2.5 architecture, specifically designed for reordering text results to improve relevance ranking.

Model Features

Efficient Training
Trained using Unsloth and Huggingface's TRL library, achieving 2x speed improvement
Multiple Quantization Versions
Provides 22 different quantization levels in GGUF format, ranging from Q2_K to Q8_0
Lightweight
0.5B parameter scale, suitable for deployment in resource-constrained environments

Model Capabilities

Text Relevance Ranking
Search Result Reranking
Text Relevance Scoring

Use Cases

Information Retrieval
Search Engine Result Ranking
Rerank search engine results to improve relevance
Recommendation System Ranking
Rerank candidate content generated by recommendation systems for better relevance
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