Worked on the meta-llama/llama-cookbook repository to address a stability issue in distributed model training workflows. Focused on deep learning and distributed computing, the developer identified and fixed a typo in the FSDP auto-wrapping policy within the finetuning script, ensuring that the MllamaCrossAttentionDecoderLayer was correctly included in the wrap policy. This adjustment prevented incorrect application of FSDP to vision models, improving resource allocation and reducing training instability. The solution was implemented in Python and documented with a traceable commit, allowing for quick reversion if necessary. The work enhanced the reliability of model training and fine-tuning processes.