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README.md

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-# mmMamba-linear Model Card
+# mmMamba-hybrid Model Card
 
 ## Introduction
 We propose mmMamba, the first decoder-only multimodal state space model achieved through quadratic to linear distillation using moderate academic computing resources. Unlike existing linear-complexity encoder-based multimodal large language models (MLLMs), mmMamba eliminates the need for separate vision encoders and underperforming pre-trained RNN-based LLMs. Through our seeding strategy and three-stage progressive distillation recipe, mmMamba effectively transfers knowledge from quadratic-complexity decoder-only pre-trained MLLMs while preserving multimodal capabilities. Additionally, mmMamba introduces flexible hybrid architectures that strategically combine Transformer and Mamba layers, enabling customizable trade-offs between computational efficiency and model performance.