May 2025 monthly summary for HabanaAI/vllm-hpu-extension: Delivered a focused AWQ quantization enhancement by introducing conditional module skipping to avoid converting selected layers that may cause compatibility or performance issues. Implemented logic to skip modules during AWQ quantization, updated AWQHPUConfig to accept a skip-list of modules, and added a helper to determine skip eligibility. Commit reference: 4a049ab346c92d73ca79260213605f0ea9a852fa (add module skip logic (#180)). No major bugs fixed this month. Overall impact: increases deployment reliability and performance by enabling selective quantization, broadening model compatibility on the HPU extension. Technologies/skills demonstrated: Python, configuration design, refactoring, and clean commit hygiene with traceable changes.

March 2025 work summary for HabanaAI/vllm-hpu-extension: Delivered stability improvements for HPU tensor operations and fixed a critical schema error related to g_idx device alignment. The bug fix ensures g_idx is moved to the 'hpu' device before comparison, improving correctness and compatibility for HPU workflows. Impact includes reduced runtime errors, smoother HPU deployments, and stronger cross-device reliability. Technologies demonstrated include Python, tensor device management, and Habana HPU APIs.