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Rank Zephyr 7b V1 Full GGUF

Developed by tensorblock
This is the GGUF quantized version of the castorini/rank_zephyr_7b_v1_full model, designed for text ranking tasks.
Downloads 66
Release Time : 11/16/2024

Model Overview

This model is a quantized version of Zephyr 7B, specifically tailored for text ranking tasks, offering multiple quantization options to suit various hardware requirements.

Model Features

Multiple Quantization Options
Offers 12 quantization levels from Q2_K to Q8_0 to meet usage needs under different hardware conditions
Efficient Inference
Optimized GGUF format enhances inference efficiency while maintaining model performance
Strong Compatibility
Compatible with llama.cpp and can run on various devices

Model Capabilities

Text Ranking
Text Understanding
Contextual Reasoning

Use Cases

Information Retrieval
Search Result Ranking
Rank the relevance of search engine results
Improves search result relevance and user experience
Recommendation Systems
Recommended Content Ranking
Optimize the ranking of content lists generated by recommendation systems
Increases click-through rates and user satisfaction for recommended content
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