jbaron34_-_Qwen2.5-0.5b-bebop-reranker-newer-small-gguf Open-Source Text Ranking Model - Empowering Information Retrieval and Document Sorting

Jbaron34 Qwen2.5 0.5b Bebop Reranker Newer Small Gguf

Developed by RichardErkhov

A 50-million-parameter text reranking model based on the Qwen2.5 architecture, suitable for information retrieval and document ranking tasks

Large Language Model #Text Reordering #Lightweight Model #Multiple Quantization Versions

Downloads 2,117

Release Time : 3/13/2025

Model Overview

This is a lightweight model specifically designed for text reranking tasks, developed based on the Qwen2.5 architecture, capable of effectively evaluating and ranking document relevance

Model Features

Lightweight Design

Only 50 million parameters, suitable for deployment in resource-limited environments

Efficient Reranking Capability

Specifically optimized for document relevance ranking tasks

Multiple Quantization Versions

Offers various quantization levels from Q2_K to Q8_0

Model Capabilities

Document Relevance Scoring

Information Retrieval Result Ranking

Text Matching Evaluation

Use Cases

Information Retrieval

Search Engine Result Ranking

Rerank search engine results by relevance

Improves search result relevance and user experience

QA System Document Filtering

Filter the most relevant answers from candidate documents

Enhances QA system accuracy

Property	Details
Model Creator	https://huggingface.co/jbaron34/
Original Model	https://huggingface.co/jbaron34/Qwen2.5-0.5b-bebop-reranker-newer-small/
Model Type	text-ranking
Base Model	unsloth/qwen2.5-0.5b-bnb-4bit
Tags	text-generation-inference, transformers, unsloth, qwen2, trl
License	apache-2.0
Language	en

Name	Quant method	Size
Qwen2.5-0.5b-bebop-reranker-newer-small.Q2_K.gguf	Q2_K	0.32GB
Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_XS.gguf	IQ3_XS	0.32GB
Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_S.gguf	IQ3_S	0.32GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_S.gguf	Q3_K_S	0.32GB
Qwen2.5-0.5b-bebop-reranker-newer-small.IQ3_M.gguf	IQ3_M	0.32GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K.gguf	Q3_K	0.33GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_M.gguf	Q3_K_M	0.33GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q3_K_L.gguf	Q3_K_L	0.34GB
Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_XS.gguf	IQ4_XS	0.33GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_0.gguf	Q4_0	0.33GB
Qwen2.5-0.5b-bebop-reranker-newer-small.IQ4_NL.gguf	IQ4_NL	0.33GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_S.gguf	Q4_K_S	0.36GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K.gguf	Q4_K	0.37GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_K_M.gguf	Q4_K_M	0.37GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q4_1.gguf	Q4_1	0.35GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_0.gguf	Q5_0	0.37GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_S.gguf	Q5_K_S	0.38GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K.gguf	Q5_K	0.39GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_K_M.gguf	Q5_K_M	0.39GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q5_1.gguf	Q5_1	0.39GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q6_K.gguf	Q6_K	0.47GB
Qwen2.5-0.5b-bebop-reranker-newer-small.Q8_0.gguf	Q8_0	0.49GB

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Jbaron34 Qwen2.5 0.5b Bebop Reranker Newer Small Gguf

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Qwen2.5-0.5b-bebop-reranker-newer-small - GGUF

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Model Information

Model Details

Quantized Model Variants

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