Qwen-encoder-0.5B-GGUF Open Source Text Encoding Model - Free Deployment to Facilitate Text Encoding Tasks

Qwen Encoder 0.5B GGUF

Developed by mradermacher

This is a statically quantized version of the knowledgator/Qwen-encoder-0.5B model, primarily designed for text encoding tasks.

Large Language Model EnglishOpen Source License:Apache-2.0 #Quantized Encoder #Lightweight LLM #Static Quantization

Downloads 175

Release Time : 3/20/2025

Model Overview

This model is a 0.5B-parameter encoder based on the Qwen architecture, quantized to reduce model size and improve inference efficiency. It supports multiple quantization levels and is suitable for tasks like classification, named entity recognition, and question answering.

Model Features

Multiple Quantization Levels

Offers various quantization levels from Q2_K to Q8_0 to meet different performance and precision requirements.

Efficient Inference

The quantized model is compact and fast, making it ideal for resource-constrained environments.

High-Quality Encoding

Based on the Qwen architecture, it delivers excellent performance in text encoding tasks.

Model Capabilities

Text encoding

Text classification

Named entity recognition

Question answering system

Use Cases

Natural Language Processing

Text Classification

Classify texts, such as sentiment analysis or topic classification.

Named Entity Recognition

Identify entities like person names, locations, and organizations in text.

Question Answering System

Build a question-answering system to respond to user queries.

🚀 Qwen-encoder-0.5B Quantized Model

This repository provides static quantizations of the Qwen-encoder-0.5B model, offering various quantized versions for different use cases.

🚀 Quick Start

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

✨ Features

Multiple Quantized Versions: Offers a variety of quantized versions of the Qwen-encoder-0.5B model, sorted by size.
Useful References: Provides links to comparison graphs and relevant discussions for better understanding of quantized types.

📚 Documentation

About

Static quants of Qwen-encoder-0.5B. Weighted/imatrix quants are available at Qwen-encoder-0.5B-i1-GGUF.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q3_K_S	0.4
GGUF	Q2_K	0.4
GGUF	IQ4_XS	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	Q3_K_L	0.5
GGUF	Q4_K_S	0.5	fast, recommended
GGUF	Q4_K_M	0.5	fast, recommended
GGUF	Q5_K_S	0.5
GGUF	Q5_K_M	0.5
GGUF	Q6_K	0.6	very good quality
GGUF	Q8_0	0.6	fast, best quality
GGUF	f16	1.1	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: Artefact2's Gist

FAQ / Model Request

See Model Requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

📄 License

This project is licensed under the apache-2.0 license.

📦 Information Table

Property	Details
Base Model	knowledgator/Qwen-encoder-0.5B
Datasets	wikimedia/wikipedia
Language	en
Library Name	transformers
License	apache-2.0
Quantized By	mradermacher
Tags	LLM2Vec, encoder, LLM, classification, NER, question-answering

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