Parameter Efficient Fine-Tunning of LLMs: Application to Machine Translation from English to Portuguese

Abstract

Fine-tuning Large Language Models (LLMs) for specific tasks, such as machine translation, is a computationally expensive process that often requires substantial hardware resources. Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), offer a resource-efficient alternative by significantly reducing the number of trainable parameters and memory requirements. In this work, we compare the performance and memory efficiency of LoRA and QLoRA on English-Portuguese translation tasks, utilizing two cutting edge LLMs, Meta LLaMA 3.1 8B and Mistral 7B. Our experiments demonstrate that both LoRA and QLoRA achieve substantial memory savings. Moreover, this work underscores the practical advantages of LoRA and QLoRA in resource-constrained environments, providing a foundation for further optimization and experimentation in machine translation using large language models.