Developed by Hugging Face, SmolVLM-256M is a multimodal model based on the Idefics3 architecture, designed for efficient image and text input processing. It can answer questions about images, describe visual content, or transcribe text, requiring less than 1GB of GPU memory for inference. The model excels in multimodal tasks while maintaining a lightweight architecture, making it suitable for deployment on edge devices. Its training data is sourced from The Cauldron and Docmatix datasets, covering a range of content including document understanding and image description, showcasing its broad application potential. Currently, this model is freely available on the Hugging Face platform, aiming to empower developers and researchers with robust multimodal processing capabilities.