A generalized architectural blueprint for building efficient MLLMs. (IMAGE)
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A generalized architectural blueprint for building efficient MLLMs. This template achieves efficiency through a combination of component choices and data flow optimization. Key strategies include: (1) Lightweight vision encoder: Employing a smaller vision backbone to reduce the initial cost of feature extraction. (2) Vision token compression: A critical step that reduces the number of visual tokens, significantly decreasing the sequence length and computational load on the subsequent language model. (3) Efficient vision-language projector: Utilizing a low-parameter projector to align visual and textual modalities with minimal overhead. (4) Compact language model backbone: Using a compact LLM backbone (e.g., 1b ∼3b parameters) as the central reasoning component. It is crucial to note that this diagram illustrates the structural approach; further significant efficiency gains are achieved via model compression techniques such as quantization and pruning, which are applied to the weights of both the vision encoder and the language model. However, these techniques will not be explicitly discussed here in order to maintain clarity.
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Visual Intelligence
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