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Letian Ruan

PROFILE

Letian Ruan

During June 2026, contributed to the mirage-project/mirage repository by delivering end-to-end DFlash support for Kimi-K2.6, focusing on advanced attention mechanisms and efficient context management. Developed the DFlash path across multiple MPK stages, integrating non-causal attention, RMSNorm with RoPE, and a paged KV-cache to enable longer context and improved throughput. Implemented a dedicated DFlash KV-store and a reusable DFlashBuilder for draft materialization, supporting future demos and scalable inference. Refactored development scaffolding to streamline shipping readiness while maintaining robust test coverage. The work leveraged CUDA, PyTorch, and C++ to ensure maintainable, cache-friendly execution and reproducible alignment with HF dumps.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

1Total
Bugs
0
Commits
1
Features
1
Lines of code
898
Activity Months1

Work History

June 2026

1 Commits • 1 Features

Jun 1, 2026

June 2026—Mirage project monthly recap: Delivered end-to-end DFlash support for Kimi-K2.6 with advanced attention and KV-cache integration, enabling longer context, improved throughput, and memory efficiency. Implemented the DFlash path across multiple MPK stages including non-causal attention core (K3), RMSNorm with RoPE, and a paged KV-cache for context management; introduced full DecoderLayer flow and final forward alignment. Added DFlash KV-store with a dedicated L4 materialization path and a reusable DFlashBuilder to assemble target drafts from weights, supporting future K2.6 demos. Established end-to-end MPK task lifecycle wiring (dflash_attention, dflash_norm_rope, dflash_kv_store) with test-mode validation and full-stack alignment to HF dumps; resolved bottlenecks with split-input K3 to separate context-KV and block-KV paths. Cleaned up and moved dev harness/tests/docs out of the tree for shipping readiness, while preserving a robust test scaffold and documentation for reproducibility. Achievements translated into business value: longer, more capable inference with predictable behavior, scalable context management, and a clean, maintainable codebase ready for K2.6 rollout.

Activity

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Quality Metrics

Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%

Skills & Technologies

Programming Languages

C++Python

Technical Skills

CUDADeep LearningGPU ProgrammingMachine LearningPyTorch

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

mirage-project/mirage

Jun 2026 Jun 2026
1 Month active

Languages Used

C++Python

Technical Skills

CUDADeep LearningGPU ProgrammingMachine LearningPyTorch