This paper aims to improve Llama 2’s performance by using personalized and modified datasets. Despite the impressive capabilities of large language models (LLMs) such as Llama 2, their effectiveness may be limited in specialized domains. The proposed method entails fine-tuning Llama 2 on custom datasets to optimize performance efficiently. The study focuses on the impact of quantization-aware low-rank adapter layers (QLoRA) on a single GPU’s resource-efficient fine-tuning performance. Furthermore, the study looks into the design of instruction datasets to guide the model toward desired behaviors. When Llama 2 is fine-tuned with QLoRA, performance improves significantly across tasks such as text summarization, question answering, and natural language generation in a variety of domains. The paper concludes by highlighting the broader implications of the findings.