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SmolVLM: Small Yet Mighty Vision Language Model
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A tartalmat a Dr. Satya Mallick biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Dr. Satya Mallick vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.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.
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