t5_summary_en_ru_zh_base_2048-GGUF Open-source Multilingual Summarization Model - Free English, Russian, and Chinese Summary Generation

T5 Summary En Ru Zh Base 2048 GGUF

Developed by mradermacher

This is a multilingual abstract generation model supporting English, Russian, and Chinese, based on the T5 architecture with multiple quantized versions available.

Text Generation Supports Multiple LanguagesOpen Source License:Apache-2.0 #Trilingual abstract generation #T5 lightweight #Low-resource deployment

Downloads 50

Release Time : 1/10/2025

Model Overview

This model is a multilingual text summarization model supporting English, Russian, and Chinese. It is a statically quantized version based on the T5 architecture, offering various quantization options from 2-bit to 16-bit floating point to suit different hardware environments and performance requirements.

Model Features

Multilingual support

Supports abstract generation in three languages: English, Russian, and Chinese

Multiple quantized versions

Offers 12 different quantization levels from Q2_K to f16 to meet various hardware and performance needs

Efficient inference

Quantized versions significantly reduce model size and improve inference speed while maintaining good generation quality

Static quantization

Current version uses static quantization technology, with potential future releases of weighted/importance matrix quantized versions

Model Capabilities

Text summarization

Multilingual processing

Text compression

Use Cases

Content summarization

News summarization

Automatically compress long news articles into brief summaries

Generates concise news summaries retaining key information

Academic paper summarization

Automatically generate abstracts for research papers

Extracts core content of papers into concise abstracts

Multilingual processing

Cross-language content summarization

Generate summaries for documents in different languages

Supports abstract generation for English, Russian, and Chinese documents

🚀 T5 Summary Model (en_ru_zh_base_2048)

A static quantized model for summarization supporting English, Russian, and Chinese.

🚀 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

Multilingual Support: Supports summarization in English, Russian, and Chinese.
Static Quantization: Provides static quantized versions of the model.

📚 Documentation

📄 Model Information

Property	Details
Base Model	utrobinmv/t5_summary_en_ru_zh_base_2048
Language	English, Russian, Chinese
Library Name	transformers
License	apache - 2.0
Quantized By	mradermacher
Tags	summarization, text2text - generation, t5

🔍 About

This is a static quantized version of utrobinmv/t5_summary_en_ru_zh_base_2048. Weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

📦 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	Q2_K	0.2
GGUF	Q3_K_S	0.2
GGUF	Q3_K_M	0.3	lower quality
GGUF	Q3_K_L	0.3
GGUF	IQ4_XS	0.3
GGUF	Q4_K_S	0.3	fast, recommended
GGUF	Q4_K_M	0.3	fast, recommended
GGUF	Q5_K_S	0.3
GGUF	Q5_K_M	0.3
GGUF	Q6_K	0.3	very good quality
GGUF	Q8_0	0.4	fast, best quality
GGUF	f16	0.7	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 mradermacher/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.

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