Sigma introduces a specialized architecture for the system domain that significantly improves inference efficiency through differential rescaling of attention components.

• DiffQKV attention optimizes Query, Key, and Value components based on their varying impacts
• Creates more efficient representation capacity in language models
• Specifically designed for system domain applications
• Balances performance enhancements with computational efficiency

This engineering innovation matters because it addresses a critical challenge in LLM deployment: maintaining high performance while reducing computational overhead—essential for practical applications where resources may be limited.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models