Joni Juvonen enhanced hardware compatibility and training reliability for large language models by contributing to the mosaicml/composer and mosaicml/llm-foundry repositories. Joni enabled TE FusedAttention to run on AMD hardware by removing the FP8 buffer export requirement, streamlining precision handling in PyTorch and improving deployment flexibility. To address NaN issues during FSDP meta initialization for Hugging Face models, Joni introduced a custom parameter initialization for RMSNorm and related layers, adding targeted tests and configuration updates. This work, implemented in Python and YAML, demonstrated a deep understanding of model initialization, hardware acceleration, and performance optimization in deep learning frameworks.