This project fine-tunes Meta’s Llama-2 for financial and economic sentiment classification. By combining Prompt Engineering, Prompt Tuning, and Parameter-Efficient Fine-Tuning (PEFT with LoRA), we achieved a significant performance boost: Baseline accuracy (without fine-tuning): 33.3% After fine-tuning: 84.7% - View it on GitHub