On Thursday, AI hosting platform Hugging Face surpassed 1 million AI model listings for the first time, marking a milestone in the rapidly expanding field of machine learning. An AI model is a computer program (often using a neural network) trained on data to perform specific tasks or make predictions. The platform, which started as a chatbot app in 2016 before pivoting to become an open source hub for AI models in 2020, now hosts a wide array of tools for developers and researchers.
The machine-learning field represents a far bigger world than just large language models (LLMs) like the kind that power ChatGPT. In a post on X, Hugging Face CEO Clément Delangue wrote about how his company hosts many high-profile AI models, like “Llama, Gemma, Phi, Flux, Mistral, Starcoder, Qwen, Stable diffusion, Grok, Whisper, Olmo, Command, Zephyr, OpenELM, Jamba, Yi,” but also “999,984 others.”
The reason why, Delangue says, stems from customization. “Contrary to the ‘1 model to rule them all’ fallacy,” he wrote, “smaller specialized customized optimized models for your use-case, your domain, your language, your hardware and generally your constraints are better. As a matter of fact, something that few people realize is that there are almost as many models on Hugging Face that are private only to one organization—for companies to build AI privately, specifically for their use-cases.”
Hugging Face’s transformation into a major AI platform follows the accelerating pace of AI research and development across the tech industry. In just a few years, the number of models hosted on the site has grown dramatically along with interest in the field. On X, Hugging Face product engineer Caleb Fahlgren posted a chart of models created each month on the platform (and a link to other charts), saying, “Models are going exponential month over month and September isn’t even over yet.”
The power of fine-tuning
As hinted by Delangue above, the sheer number of models on the platform stems from the collaborative nature of the platform and the practice of fine-tuning existing models for specific tasks. Fine-tuning means taking an existing model and giving it additional training to add new concepts to its neural network and alter how it produces outputs. Developers and researchers from around the world contribute their results, leading to a large ecosystem.