
Worked on refactoring and aligning Mamba-based models within the fla-org/flash-linear-attention repository to match the mamba_ssm reference implementation. Focused on removing unused parameters, standardizing configuration interfaces, and updating naming conventions for time step parameters to reduce configuration drift. Enhanced code maintainability and production readiness by adding regression tests and improving code hygiene using Python and PyTorch. Addressed critical configuration issues and implemented unit tests for inference decoding, ensuring reliable model behavior. The work emphasized model optimization and software engineering best practices, resulting in improved integration and reduced risk for future deployments while maintaining alignment with upstream references.
April 2026: Refactor and alignment of Mamba-based models to mamba_ssm reference. Removed unused parameters, standardized dt naming (dt, dt_rank), updated config interfaces, and added tests for inference decoding. Fixed critical config issues and prepared code for production deployment.
April 2026: Refactor and alignment of Mamba-based models to mamba_ssm reference. Removed unused parameters, standardized dt naming (dt, dt_rank), updated config interfaces, and added tests for inference decoding. Fixed critical config issues and prepared code for production deployment.

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