Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and Multi-Domain Testing

TL;DR

This paper introduces a resource-efficient method for fine-tuning a large Arabic language model using Quantized Low-Rank Adaptation on limited hardware, improving performance across multiple Arabic NLP tasks.

Contribution

It presents a novel adaptation process for Arabic LLMs using QLoRA with low hardware requirements, addressing linguistic challenges and demonstrating effective multi-domain performance.

Findings

Significant reduction in GPU memory usage during training

Improved accuracy on Arabic NLP tasks

Robustness to input perturbations

Abstract

This paper presents a novel approach to fine-tuning the Qwen2-1.5B model for Arabic language processing using Quantized Low-Rank Adaptation (QLoRA) on a system with only 4GB VRAM. We detail the process of adapting this large language model to the Arabic domain, using diverse datasets including Bactrian, OpenAssistant, and Wikipedia Arabic corpora. Our methodology involves custom data preprocessing, model configuration, and training optimization techniques such as gradient accumulation and mixed-precision training. We address specific challenges in Arabic NLP, including morphological complexity, dialectal variations, and diacritical mark handling. Experimental results over 10,000 training steps show significant performance improvements, with the final loss converging to 0.1083. We provide comprehensive analysis of GPU memory usage, training dynamics, and model evaluation across various Arabic language tasks, including text classification, question answering, and dialect identification. The fine-tuned model demonstrates robustness to input perturbations and improved handling of Arabic-specific linguistic phenomena. This research contributes to multilingual AI by demonstrating a resource-efficient approach for creating specialized language models, potentially democratizing access to advanced NLP technologies for diverse linguistic communities. Our work paves the way for future research in low-resource language adaptation and efficient fine-tuning of large language models.