For 2026-04 in modelscope/ms-swift, delivered key features across training, inference, and deployment pipelines, with robust fixes to distributed training, data handling, and multi-modal capabilities. This month focused on hardening the training loop, improving rollout efficiency, and expanding Gemma4 template capabilities to handle audio, video, and image inputs, driving reliability, scalability, and business value.

March 2026 monthly summary for modelscope/ms-swift focusing on delivered features, stability fixes, and business impact. Highlights include GRPO enhancements with top-k logits cleanup and QLora integration, OPSD algorithm support, stabilizing Megatron GKD (load teacher and top-k fixes), weight sync and resume improvements, and documentation/stability upgrades (WandB logging fix, TRL bump, and default adjustments). These efforts improved training reliability, performance, and developer usability across distributed training workflows.

February 2026 monthly summary for modelscope/ms-swift focusing on RLHF training configuration and library version updates, training efficiency improvements, and GRPOTrainer reward processing and sampling enhancements. Upgraded dependencies and training infrastructure to improve reliability, efficiency, and maintainability in distributed training workflows. Key delivery includes updated library versions (trl 0.28, vllm 0.15.1) and liger-kernel 0.7.0, together with robust fixes for generation batching, reward handling, and training data pipelines. These changes reduce training friction, improve experiment reproducibility, and enable smoother deployments of RLHF-based models.

February 2026? Wait—the month is 2026-01 per input. Monthly summary for 2026-01 focused on delivering the DeepSeek-OCR-2 integration in ms-swift and stabilizing MoE training in the GKD trainer. Delivered architecture and loader for the DeepSeek-OCR-2 model, with template adjustments and testing framework updates to support the new model. Also fixed MoE teacher initialization logic to ensure proper weight loading and expected functionality in the GKD trainer. These contributions improve model deployment readiness for OCR workloads, increase training reliability, and enable smoother experimentation and faster iteration for future enhancements.

Month: 2025-11 — Focused on stabilizing the Triton kernel integration in jeejeelee/vllm. Implemented robust error handling that catches both ImportError and AttributeError during Triton kernel imports, improving reliability and user feedback. This change reduces runtime failures in production inference and lowers support overhead by surfacing clearer errors earlier in the pipeline.