June 2026: Focused on improving onboarding clarity for the HPC course through a targeted FAQ update. Delivered the HPC Course Prerequisites FAQ Update in ZJUSCT/HPC101, improving user understanding and accessibility while tightening prerequisite alignment with course content.

May 2026 monthly summary for ZJUSCT/HPC101: Delivered key features and fixes that enhance student planning, UX, and deployment reliability. Key updates include HPC101 course resources and registration flow refinements; instant navigation with a dark-mode flash fix; Mermaid diagram support and a JS-based VM emulator; and a revamped CI/CD workflow using peaceiris/actions-gh-pages for GitHub Pages deployment. Major bugs fixed include the dark-mode flash issue and replacing a nonfunctional gh-deploy command with a reliable GitHub Actions solution. Overall impact: clearer course information, smoother user interactions, and a robust, repeatable deployment process that reduces maintenance time. Technologies demonstrated: frontend UX enhancements, JS tooling for diagrams and VM emulation, comprehensive documentation updates, and GitHub Actions automation.

April 2026 focused on delivering a scalable, memory-efficient Mixture of Experts (MoE) SFT backend with LoRA support for ktransformers, along with robust compatibility, validation, and architectural enhancements. The work enables large-scale MoE fine-tuning with reduced memory footprint and improved throughput, and lays groundwork for broader MoE support across Qwen3/Qwen3.5-style architectures.

Month 2026-03: Focused on CPU-optimized inference paths and numerical reliability for KTransformers, delivering AVX2-only support for BF16 and FP8 and enabling broader deployment on AVX2-capable CPUs. Implemented performance-enhancing features, tightened FP8 dequantization semantics, and expanded developer documentation to support AVX2-only environments. Also added build safeguards to avoid accidentally enabling AVX512 paths on AVX2-only machines. The changes position KTransformers for high-throughput inference in CPU-centric deployments and improve developer onboarding for AVX2 setups.

January 2026 monthly summary for kvcache-ai/ktransformers. Delivered stability and performance improvements for ML activation paths through Enhanced exp_avx512 Polynomial Evaluation. Addressed numerical instability in exp_avx512 act_fn, improving reliability of model inference/training on AVX512 hardware.

December 2025: Delivered critical architecture and RL-oriented improvements in kvcache-ai/ktransformers, with a strong emphasis on model compatibility, flexibility for experimentation, and developer-facing documentation. Key outcomes include GLM-4.6V weight conversion support, LoraModel adapter layer toggling for RL tasks, and improved DPO tutorials/docs to accelerate adoption and correct usage. No critical bugs reported; the month focused on feature delivery and knowledge transfer, enhancing business value by enabling faster model deployment and more robust experimentation pipelines.

November 2025 monthly summary: Delivered two high-impact features across sgLang and ktransformers to improve Qwen3-VL deployment performance and weight handling robustness. No major bugs fixed this month. Overall impact: smoother conditional generation with explicit MoE configuration support and more robust weight conversion workflows, enabling safer migrations and faster iteration. Technologies demonstrated: KTransformers integration, Qwen3-VL MoE support, model configuration management, fused-weights handling and configuration validation.

July 2025 monthly summary for ZJUSCT/HPC101: Delivered focused documentation and branding enhancements that strengthen user planning, workflow adherence, and performance optimization. Key features delivered include comprehensive HPC usage and capacity guidelines with Slurm guidance and GPU provisioning, and the introduction of Slurm GPU jobs; Lab Scheduling and Workflow Documentation updates to align timelines with new schedules and extend critical deadlines; OJ and Nsight Compute Tools Documentation to help verify submissions and optimize CUDA performance; and Branding assets refresh including a transparent-background logo. These efforts improve onboarding, operational reliability, and user productivity, and demonstrate strong documentation discipline, cross-team collaboration, and CI-friendly content maintenance.

June 2025 monthly summary for ZJUSCT/HPC101: Focused on improving cluster access and course documentation, with specific alignment to Lab 1 deadlines on learning@zju. Delivered consolidated documentation updates to clarify SSH login username format, usage guidelines for resource allocation and data storage, added explicit login node identifier 'hpc101', and synchronized the Lab 1 deadline with the learning@zju platform. Major bug fix: corrected Lab 1 deadline to match the platform, reducing user ambiguity and support tickets. Impact: improved user onboarding, faster access to the cluster, and better compliance with platform workflows. Technologies/skills demonstrated: documentation engineering, version control, SSH/login node conventions, resource usage and data storage guidance.

May 2025 monthly summary for ZJUSCT/HPC101: Key enhancements to MiniCluster Lab setup with Docker/NFS integration, BLAS/CBLAS and HPL configuration guidance, and performance-oriented tuning tips, plus refined Lab1 documentation. Security and bonus documentation revisions tighten login policy, warn about password-based access, prohibit collection of public keys, and clarify bonus scope (SPACK not an extra bonus). These efforts improve onboarding, reproducibility, and security for HPC labs, enabling faster, safer performance testing.