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Cnmoro TinyLlama ContextQuestionPair Classifier Reranker Gguf

Developed by RichardErkhov
This is a lightweight text ranking model based on TinyLlama, specifically designed for classifying and reordering context-question pairs.
Downloads 391
Release Time : 10/16/2024

Model Overview

The model optimizes relevance ranking for information retrieval and Q&A systems by classifying and reordering context-question pairs.

Model Features

Lightweight Quantization
Offers multiple quantized versions, with the smallest being only 0.4GB, suitable for resource-constrained environments.
Context-Question Pair Processing
Specially optimized for analyzing the relevance between context and question pairs.
Multiple Quantization Options
Provides 21 different quantization levels from Q2_K to Q8_0 for model selection.

Model Capabilities

Text Relevance Scoring
Question-Answer Pair Ranking
Context Understanding
Information Retrieval Optimization

Use Cases

Q&A Systems
FAQ Ranking
Ranks candidate answers by relevance to improve Q&A system accuracy.
Inference can enhance answer selection accuracy.
Information Retrieval
Document Passage Ranking
Reorders retrieved document passages based on query questions.
Inference can improve retrieval result relevance.
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