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SmolVLM: Small Yet Mighty Vision Language Model
Manage episode 509724918 series 3693358
Dr. Satya Mallick에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Dr. Satya Mallick 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
In this episode of Artificial Intelligence: Papers and Concepts, we explore SmolVLM, a family of compact yet powerful vision language models (VLMs) designed for efficiency.
Unlike large VLMs that require significant computational resources, SmolVLM is engineered to run on everyday devices like smartphones and laptops.
We dive into the research paper SmolVLM: Redefining Small and Efficient Multimodal Models and a related HuggingFace blog post, discussing key design choices such as optimized vision-language balance, pixel shuffle for token reduction, and learned positional tokens to improve stability and performance.
We highlight how SmolVLM avoids common pitfalls such as excessive text data and chain-of-thought overload, achieving impressive results— outperforming models like idefics-80b, which is 300 times larger—while using minimal GPU memory (as low as 0.8GB for the 256M model).
The episode also covers practical applications, including running SmolVLM in a browser, mobile apps like HuggingSnap, and specialized uses like BioVQA for medical imaging. This episode underscores SmallVLM’s role in democratizing advanced AI by making multimodal capabilities accessible and efficient.
Resources:
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- OpenCV University - Start your AI Career today!
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