Fine-Tuning Large Language Models for Task Specific Data

Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing tasks. Fine-tuning techniques, however, can improve their efficiency even more for domain-specific applications. In particular, an ecommerce dataset sourced from Wayfair is used in this study to investigate the fine-tuning of LLaMA 2 and Falcon 7B models utilizing Parameter-Efficient Fine-Tuning (PEFT) and Quantized Low-Rank Adaptation (QLoRA) techniques. Comparing the performance [1] of these models and assessing their efficacy with Claude 3, a sophisticated assessment tool, is the main goal. Our method entails the construction of bespoke datasets, sophisticated preprocessing, and the use of fine-tuning approaches to optimize the models for the demands of certain tasks. When it comes to producing precise and contextually appropriate responses, LLaMA 2 performs better than Falcon 7B, according to quantitative measurements and qualitative research. This study emphasizes how crucial customized fine-tuning methods are to improving LLM performance in particular fields.