Evolving AI models: adoption patterns of transformers and diffusers

Abstract This study investigates the development, adoption, and implications of artificial intelligence (AI) models by analysing a comprehensive dataset of over 316,000 models hosted on the Hugging Face platform. Focusing on two dominant model architectures - transformers and diffusion models - it examines their distribution across tasks, user engagement patterns, and practical applications in domains such as natural language processing, computer vision, audio processing, and generative media. The research highlights the growing prominence of generative AI, the role of open-source platforms in shaping model accessibility, and the divergence in use trends between foundational and emerging AI tools. Drawing on correlations between downloads, likes, citations, and model size, the paper discusses how each library’s community-driven dynamics shape their respective strengths. Finally, the paper discusses implications for business strategy and adoption, encompassing practical considerations like infrastructure requirements and ethical challenges, and underscores the potential for these evolving model ecosystems to drive innovative, human-centric AI solutions across diverse sectors.