April 2026 monthly summary for sgl-lang repository. Key accomplishment: Implemented Kimi K25 EPD support for multimodal processing by integrating Kimi K25-specific handling, including adjustments to grid dimensions and image processing. The work is captured in commit 42ffb168b3118a18713002b93c4e48dfb8257475 with PR #22269 and a signed-off contribution. This expands model compatibility, enabling production-ready deployment paths for Kimi K25-based multimodal workloads and aligns with the roadmap to broaden EPD-enabled capabilities.

February 2026 monthly summary for kvcache-ai/sglang focused on strengthening MoE quantization robustness and integration to deliver more reliable, efficient, and scalable models. Delivered enhancements to compressed-tensors MoEs, expanded support for fused MoE layers, and ensured consistent quantization schemes across layers, with improved integration of linear layers into the MoE framework to boost model performance in production.

January 2026 monthly summary for kvcache-ai/sglang focused on stabilizing quantization for BailingMoEModelNextN. Implemented a quantization stability fix in the eh_proj layer that mitigates quantization-induced training instability and performance regressions. The change aligns with PR #17808, captured in commit 0e4d9ddbd6b5146e6664899c93255777e01d758e, and includes a sign-off by LHXuuu.

December 2025: Implemented Dynamic Weights Mapping for Quantization Loader in kvcache-ai/sglang, introducing WeightsMapper to map weight names in quantization configurations and enable dynamic updates to module names for robust quantization loading across diverse model architectures. This work improves compatibility with the sglang model structure and reduces manual intervention when deploying quantized models. Additionally, fixed issues in qwenvl compressed tensors quantization weight loader, stabilizing loading behavior across architectures (commit 712f44ee2b5bdd7a04740d3c6d12398a4f4d1d29; #11914).

November 2025 monthly summary: Delivered one bug fix and one feature across two repositories, delivering tangible business value and strengthening the codebase. Key outcomes include removing a duplicate import to improve maintainability in kvcache-ai/sglang, and enabling compressed tensor support for the VLLM Ascend engine with W8A8 formats, including a new configuration class and quantization updates for static and dynamic weights. These changes align with the LLM Compressor workflow and prepare the stack for more efficient, hardware-accelerated deployments on Ascend. The work demonstrates strong code hygiene, configuration-driven design, and cross-team collaboration.