A Framework for Resource-Efficient Large Language Model Fine-tuning

A Framework for Resource-Efficient Large Language Model Fine-tuning

Author: Prabu Arjunan

Email: prabuarjunan@gmail.com

Senior Technical Marketing Engineer

Abstract
This paper proposes a systematic framework for the fine-tuning process of Large Language Models. The proposed method addresses the computational challenges that naturally arise in LLM fine-tuning by incorporating a comprehensive architecture that effectively integrates intelligent resource management along with adaptive training strategies. Our framework introduces dynamic resource allocation mechanisms along with efficient data processing pipelines, thereby making LLM fine-tuning more accessible to organizations with limited computational resources. The methodology outlined focuses on model quality, optimizes resource use, and has to do with systematic process management and intelligent scheduling.

The framework aims to showcase how intelligent resource management and adaptive training strategies can make LLM fine-tuning more accessible to organizations with limited computational resources. The architecture proposed here represents a structured approach toward the resolution of these challenges while keeping the quality standards of the model intact.

Keywords: Large Language Models, Fine-tuning, Resource Management, Deep Learning, Neural Networks, Adaptive Training