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Qwen3 Embedding 0.6B MXL 4bit

Developed by kerncore
This is a 4-bit quantized version converted from the Qwen3-Embedding-0.6B model, optimized for the MLX framework.
Downloads 128
Release Time : 7/8/2025

Model Overview

This model is a 4-bit quantized version converted from Qwen/Qwen3-Embedding-0.6B, suitable for embedding tasks and capable of converting text into high-dimensional vector representations.

Model Features

4-bit Quantization
The model has undergone 4-bit quantization processing, significantly reducing memory usage and computational resource requirements.
MLX Optimization
Optimized specifically for the MLX framework, providing efficient inference performance.
Efficient Embedding
Capable of efficiently converting text into high-dimensional vector representations, suitable for various downstream tasks.

Model Capabilities

Text Embedding
Semantic Similarity Calculation
Information Retrieval

Use Cases

Information Retrieval
Document Search
Convert documents into embedding vectors to achieve efficient semantic search.
Improve search accuracy and efficiency
Semantic Analysis
Text Similarity Calculation
Calculate the semantic similarity between texts.
Can be used for tasks such as clustering and classification
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