Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts

The Symphony-MoE framework represents a significant advancement in the field of AI by addressing the limitations of traditional Mixture-of-Experts (MoE) models, which typically draw from a single pre-trained model. This approach often leads to a lack of diversity among experts, limiting overall performance. Symphony-MoE innovatively integrates experts from multiple pre-trained models, such as Qwen2.5-Coder and Qwen2, utilizing a layer-aware fusion strategy to align parameters effectively. This two-stage framework not only harmonizes the models but also overcomes the challenges posed by their disparate parameter spaces. Experimental results indicate that this method achieves an MoE model that significantly surpasses existing baselines, showcasing its potential to enhance scalability and efficiency in AI applications.