Leim Gruber contributed targeted enhancements to kvcache-ai/ktransformers and ggml-org/llama.cpp, focusing on documentation clarity and model configuration detection. In ktransformers, Leim improved the DeepSeek-R1 workflow by updating Markdown-based tutorials and refining prompt handling logic in C++, addressing user confusion and clarifying model testing behavior. Later, in llama.cpp, Leim implemented a layer-count heuristic in C++ to detect GigaChat3 lite variants, adding comprehensive comments to support maintainability and reduce misconfiguration risks. The work demonstrated depth in algorithm design and modeling, delivering well-documented, maintainable code that improved usability and feature parity for both standard and lite model deployments.