TransMamba addresses the efficiency-performance tradeoff in large language models by unifying Transformer and Mamba architectures through shared parameter matrices.

• Solves key limitations: Overcomes Transformer's quadratic complexity for long sequences while fixing Mamba's contextual learning instability
• Flexible switching: Dynamically leverages strengths of both architectures based on computational needs
• Engineering breakthrough: Creates a unified framework that maintains performance while improving efficiency for long-context processing
• Practical implications: Enables more efficient LLMs that can handle longer sequences without sacrificing performance

TransMamba: Flexibly Switching between Transformer and Mamba