%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Jbaron34 Qwen2.5 0.5b Bebop Reranker New Small Gguf
A text reranking model based on the Qwen2.5 architecture with 0.5B parameters, suitable for reranking tasks.
Downloads 2,454
Release Time : 3/13/2025
Model Overview
This model is a small text reranking model based on the Qwen2.5 architecture, primarily used for reordering text to improve the relevance of search results.
Model Features
Efficient Quantization
Offers multiple quantization versions, from Q2_K to Q8_0, to accommodate different hardware requirements.
Lightweight
With only 0.5B parameters, it is suitable for deployment in resource-limited environments.
Reranking Optimization
Specifically optimized for text reranking tasks to enhance search result quality.
Model Capabilities
Text Reranking
Relevance Scoring
Search Result Optimization
Use Cases
Information Retrieval
Search Engine Result Reranking
Reorders the initial results returned by a search engine to improve the ranking of the most relevant results.
Enhances search result relevance and user satisfaction.
Recommendation Systems
Recommended Content Sorting
Optimizes the ranking of content lists generated by recommendation systems.
Increases click-through rates and user engagement with recommended content.
TransformersFeatured Recommended AI Models