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arXiv · 2409.06754

Scaling Law Hypothesis for Multimodal Model

Qingyun Sun·Zhen Guo·PIN AI Team
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Abstract

We propose a scaling law hypothesis for multimodal models processing text, audio, images, and video within a shared token and embedding space. Our framework predicts model performance based on modality-specific compression and tokenization efficiency, extending established scaling laws from text-based decoder models to mixed-modality systems. We explore whether leveraging more training data in multiple modalities can reduce the size of the multimodal model, enabling efficient deployment on resource-constrained devices.

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Qingyun Sun, Zhen Guo, PIN AI Team. 2024-09-10. Scaling Law Hypothesis for Multimodal Model. https://arxiv.org/abs/2409.06754

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