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Lightblue Reranker 0.5 Cont Filt Gguf
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Lightblue Reranker 0.5 Cont Filt Gguf

Developed by RichardErkhov
A text ranking model fine-tuned based on Qwen2.5-0.5B-Instruct, suitable for information retrieval and relevance ranking tasks
Large Language Model#Text Ranking#Lightweight Model#Fine-tuning Optimization
Downloads 2,130
Release Time : 3/14/2025

Model Overview

This is a text ranker fine-tuned from the Qwen2.5-0.5B-Instruct model, primarily used for evaluating and ranking text relevance. The model has been quantized, offering multiple quantization versions to accommodate different hardware requirements.

Model Features

Multiple Quantization Versions
Offers 20 different quantization levels from Q2_K to Q8_0 to meet various hardware and performance needs
Efficient Fine-tuning
Fine-tuned on the Qwen2.5-0.5B-Instruct model, achieving good results on the reranker_continuous_filt_train dataset
Lightweight
The smallest quantized version is only 0.39GB, suitable for deployment in resource-constrained environments

Model Capabilities

Text Relevance Evaluation
Information Retrieval Ranking
Document Relevance Scoring

Use Cases

Information Retrieval
Search Engine Result Ranking
Re-ranking search engine results by relevance
Q&A Systems
Evaluating the relevance of candidate answers to questions
Content Recommendation
Related Content Recommendation
Recommending related articles or products based on user browsing history
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