SmolVLM is a groundbreaking model by Hugging Face that requires significantly less GPU power, approximately half of what similar models need, making it highly efficient.
The architecture of SmolVLM was extensively modified, resulting in the model needing only 5.02 GB of RAM. This is considerably less than models like InternVL2 2B, demonstrating its efficiency.
SmolVLM utilizes a novel image compression method to enhance performance while minimizing RAM usage, processing images in 81 visual tokens for optimal efficiency.
As a multimodal model, SmolVLM can handle both visual and text input, proving useful for businesses looking to reduce costs associated with deploying large language models.
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