base_model:
- Qwen/Qwen2.5-7B-Instruct
AllSparkv2: A Language-centric Progressive Omni-modal Learning Framework
Run Shao, and Haifeng Li. School of Geosciences and Info-physics, Central South University
Introduction
AllSparkv2 is a progressive multimodal learning framework that decouples cross-modal general knowledge from modality-specific knowledge at both the architecture and training strategy levels. Inspired by Piaget's Theory of Cognitive Development, AllSparkv2 introduces the Modal Mixture of Experts (M-MoE) architecture, where dedicated experts handle different modalities to decouple the parameter space, and new modality experts inherit cross-modal general knowledge by initializing from existing ones. In training, a hierarchical modality learning strategy is implemented, starting with vision as the initial modality, followed by point clouds as the successive modality. AllSparkv2 undergoes full-parameter training on vision for powerful cross-modal general knowledge, while only modality-specific experts are trained for point clouds, preserving existing knowledge. Experimental results demonstrate that AllSparkv2 can progressively integrate new modalities while preventing catastrophic forgetting and enhancing cross-modal performance.
Note
We provide this model in four different sizes: 0.5B, 1B, 3B, and 7B. You can find them at the following links:
If you're using AllSparkv2 in your research or applications, please cite using this BibTeX:
License
This repository is under BSD 3-Clause License.