Worked on enhancing the stability of input processing in the Blaizzy/mlx-vlm repository, focusing on robust error handling within the model’s inference pipeline. Addressed a critical issue where the Thinker input path could trigger an UnboundLocalError if visual features were missing, which previously led to runtime crashes in feature-sparse scenarios. The solution involved initializing the Thinker.visual_embeds_multiscale variable to None when no visual features are present, ensuring safer batch and inference workflows. Utilized Python for model development and error handling, resulting in improved reliability and robustness of the model’s input processing without introducing new features during the period.