Monthly summary for 2025-10 focusing on CSCfi/csc-user-guide improvements and quantization documentation enhancements. Delivered comprehensive updates to ML-LLM quantization docs, including direct GitHub subdirectory links for examples, corrected/standardized links, clarified PTQ/QAT explanations, updated code samples for Bitsandbytes and GPTQ, and standardized capitalization of quantization method names. This work improves developer onboarding, reduces support time, and aligns docs with current tooling and practices.

September 2025 monthly summary for CSCfi/csc-user-guide focused on strengthening LLM quantization guidance through targeted documentation improvements and cross-tool navigation. Delivered a consolidated, developer-friendly reference that helps practitioners choose between PTQ and QAT, while surfacing tool-specific sections (BitsAndBytes, GPTQ, AWQ), updated examples, external tutorials, and improved repository references to streamline onboarding and reduce support overhead.

August 2025: Focused on documentation quality and enabling ML quantization workflows in CSCfi/csc-user-guide. Key feature delivered: Quantization documentation enhancements for ml-llm.md, consolidating definitions, PTQ/QAT explanations, and guidance to locate pre-quantized models on Hugging Face. No major bugs fixed this month. Business impact: reduced onboarding time, clearer guidance for selecting and deploying quantized models, enabling faster and safer ML deployments. Technologies and skills demonstrated: technical documentation, ML quantization concepts (PTQ/QAT), Git-based documentation updates, and cross-team collaboration.