During January 2025, Li Tang focused on enhancing the developer experience for Habana-backed models by refactoring and expanding documentation in the huggingface/optimum-habana repository. Li updated Markdown-based READMEs to clarify inference precision options, including BF16 and FP8, and provided detailed guidance for both single- and multi-card configurations. The work included refining LoRA finetuning examples and aligning documentation with current experimental settings, aiming to reduce setup friction and accelerate onboarding. Leveraging skills in technical writing and documentation, Li’s contributions improved clarity and usability for developers working with image-to-text and text-feature-extraction workflows, though the scope was limited to documentation updates.