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Optimizing Large Language Models for Edge Devices : = A Comparative Study on Reputation Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Optimizing Large Language Models for Edge Devices :/
Reminder of title:	A Comparative Study on Reputation Analysis.
Author:	Rahman, Mohammad Wali Ur.
Description:	1 online resource (91 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-05.
Contained By:	Masters Abstracts International85-05.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380843867

Optimizing Large Language Models for Edge Devices : = A Comparative Study on Reputation Analysis.
Rahman, Mohammad Wali Ur.

Optimizing Large Language Models for Edge Devices :A Comparative Study on Reputation Analysis. - 1 online resource (91 pages)

Source: Masters Abstracts International, Volume: 85-05.

Thesis (M.S.)--The University of Arizona, 2023.

Includes bibliographical references

The widespread adoption of social media platforms has led to an exponential surge in user-generated data, shaping the reputations of companies and public figures on a global scale. Consequently, reputation management through automated analysis has become indispensable. Recent advances in large-scale language models, such as BERT (Bidirectional Encoder Representations from Transformers), have shown remarkable success in Natural Language Processing (NLP) tasks. However, deploying such models on resource-constrained edge devices remains challenging due to their size and computational demands.In this thesis, we focus on optimizing large language models for edge devices with limited resources by conducting a comparative study. Our investigation centers around reputation analysis, a critical NLP task, to assess the feasibility and efficiency of deploying transformer-based models on edge devices. We fine-tune MobileBERT, a compact version of BERT, on the reputation polarity task using the RepLab 2013 dataset. To enable their deployment on edge devices, we utilize TensorFlow-Lite models and apply quantization techniques for model optimization.The experimental results demonstrate the efficacy of our approach. The quantized MobileBERT models showcase comparable performance to the original BERT model, with only a marginal 4.1\\% drop in accuracy. More importantly, the optimized models exhibit a significant reduction in model size, boasting a 160x smaller footprint, and achieve faster inference times. These results validate the practical applicability of transformer-based reputation analysis on edge devices, making them an attractive solution for real-world deployments.In conclusion, this thesis showcases the potential of optimizing large language models for resource-constrained edge devices. Through a rigorous comparative study, we demonstrate that transformer-based models can be effectively deployed for reputation analysis on edge devices without compromising performance. This research paves the way for leveraging advanced NLP techniques in real-world applications, where efficient edge device implementation is essential, opening new possibilities for a wide range of edge computing scenarios beyond reputation analysis.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380843867Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Social media platformsIndex Terms--Genre/Form:

554714
Electronic books.

Optimizing Large Language Models for Edge Devices : = A Comparative Study on Reputation Analysis.
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520 $a The widespread adoption of social media platforms has led to an exponential surge in user-generated data, shaping the reputations of companies and public figures on a global scale. Consequently, reputation management through automated analysis has become indispensable. Recent advances in large-scale language models, such as BERT (Bidirectional Encoder Representations from Transformers), have shown remarkable success in Natural Language Processing (NLP) tasks. However, deploying such models on resource-constrained edge devices remains challenging due to their size and computational demands.In this thesis, we focus on optimizing large language models for edge devices with limited resources by conducting a comparative study. Our investigation centers around reputation analysis, a critical NLP task, to assess the feasibility and efficiency of deploying transformer-based models on edge devices. We fine-tune MobileBERT, a compact version of BERT, on the reputation polarity task using the RepLab 2013 dataset. To enable their deployment on edge devices, we utilize TensorFlow-Lite models and apply quantization techniques for model optimization.The experimental results demonstrate the efficacy of our approach. The quantized MobileBERT models showcase comparable performance to the original BERT model, with only a marginal 4.1\\% drop in accuracy. More importantly, the optimized models exhibit a significant reduction in model size, boasting a 160x smaller footprint, and achieve faster inference times. These results validate the practical applicability of transformer-based reputation analysis on edge devices, making them an attractive solution for real-world deployments.In conclusion, this thesis showcases the potential of optimizing large language models for resource-constrained edge devices. Through a rigorous comparative study, we demonstrate that transformer-based models can be effectively deployed for reputation analysis on edge devices without compromising performance. This research paves the way for leveraging advanced NLP techniques in real-world applications, where efficient edge device implementation is essential, opening new possibilities for a wide range of edge computing scenarios beyond reputation analysis.
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