Arxiver_insightSumm_t5_finetuned_model-GGUF Open-source Model - Freely Achieve Efficient Generation of Academic Paper Abstracts

Arxiver Insightsumm T5 Finetuned Model GGUF

Developed by mradermacher

A statically quantized model based on the T5 architecture, specialized for academic paper abstract generation tasks

Text Generation EnglishOpen Source License:Apache-2.0 #Paper Abstract Generation #T5 Fine-tuned Model #Quantized Efficient Inference

Downloads 131

Release Time : 5/9/2025

Model Overview

This is a fine-tuned T5 model specifically designed for generating insightful summaries of academic papers. The model has undergone static quantization, offering multiple quantized versions to suit different needs.

Model Features

Multiple Quantized Versions

Offers 11 quantized versions from Q2_K to f16, catering to different precision and performance requirements

Academic Abstract Generation

Specialized summarization capability optimized for academic paper content

Lightweight Deployment

The smallest quantized version is only 0.2GB, suitable for resource-constrained environments

Model Capabilities

Text Summarization

Academic Content Understanding

English Text Processing

Use Cases

Academic Research

Rapid Paper Reading

Generate concise summaries for lengthy academic papers

Helps researchers quickly grasp the core content of papers

Literature Review Assistance

Batch process multiple related papers to generate summaries

Accelerates the literature survey process

🚀 Arxiver_insightSumm_t5_finetuned_model

This project provides static quantizations of the Arxiver_insightSumm_t5_finetuned_model, offering various quantization types for summarization tasks.

🚀 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

Summarization: The model is designed for summarization tasks.
Multiple Quantization Types: Offers a variety of quantization types to meet different needs.

📦 Information

Property	Details
Base Model	mishikaa16/Arxiver_insightSumm_t5_finetuned_model
Language	en
Library Name	transformers
License	apache - 2.0
Quantized By	mradermacher
Tags	summarization, generated_from_trainer

📚 Documentation

About

Static quants of https://huggingface.co/mishikaa16/Arxiver_insightSumm_t5_finetuned_model. 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	0.2	lower quality
GGUF	Q3_K_L	0.2
GGUF	IQ4_XS	0.2
GGUF	Q4_K_S	0.2	fast, recommended
GGUF	Q4_K_M	0.2	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	0.3	fast, best quality
GGUF	f16	0.5	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: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

📄 License

This project is licensed under the apache - 2.0 license.

🙏 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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