The research explores the integration of generative AI in multimedia content production using a fine-tuned Llama 2 model for text generation and the Stable Diffusion algorithm for image synthesis. The research analyses the fine-tuned Llama 2-7b-chat model's adaptability to specific content generation contexts, enhanced by a unique dataset and QLoRa, a Quantized Low-Rank Adaptation for parameter-efficient fine-tuning, achieving significant reductions in training loss and nuanced quality in the generated content. Notably, the model's evaluation yielded an impressive perplexity score of 1.49, indicating advanced predictive performance. Additionally, stable diffusion's ability to transform textual descriptions into intricate images, highlighting its potential in AI-mediated content creation is demonstrated. The experiments and qualitative analyses reveal improvements in efficiency and creativity, emphasizing the collaborative potential of these models to revolutionize multidisciplinary content generation. The research underscores the transformative impact of fine-tuned generative models on content creation and offers insights into the broader implications for future AI research, while acknowledging the critical need for ethical considerations in the deployment of such technologies.

Generative AI Llama 2 Stable Diffusion QLoRa Parameter-Efficient fine-tuning