Fine Tune Large Language Model (LLM) on a Custom Dataset with QLoRA

QLoRA fine-tuning efficiently customizes large language models for specific tasks using minimal computational resources. It quantizes model weights to 4-bit precision and updates only small adapter matrices during training, reducing memory requirements while maintaining performance. The tutorial uses Microsoft's Phi-2 model on a dialogue summarization dataset, demonstrating significant improvements. This approach allows users with limited GPU resources to effectively fine-tune powerful language models for specialized applications.