Fine Tuned Model

Fine-tuning pre-trained models adapts their capabilities to specific downstream tasks by training them on a smaller, task-relevant dataset. Current research emphasizes improving the efficiency and robustness of fine-tuning, focusing on techniques like parameter-efficient fine-tuning (e.g., LoRA, adapters) to reduce computational costs and mitigate issues like catastrophic forgetting and overfitting. This work is crucial for advancing various fields, from improving the accuracy and reliability of large language models in specific domains (e.g., medical diagnosis, financial analysis) to enhancing the performance of image generation and other machine learning models with limited data. The goal is to leverage the power of pre-trained models while addressing their limitations for practical applications.