Illuminating the Insides of Mlx Models (github.com)

A significant breakthrough in AI interpretability research has emerged with the introduction of the mlx library, which allows users to engage deeply with large language models (LLMs) on Apple Silicon devices. This library enables rapid sampling from models with billions of parameters while facilitating the manipulation and inspection of internal model states through the use of hooks. This innovative approach, combining techniques from the TransformerLens and Penzai libraries, provides researchers with interactive tools to visualize and control model behaviors, enhancing our understanding of how LLMs "think" and make decisions internally. Key features of mlx include tools like Logit Lens, which reveals intermediate computational states, and Activation Patching, enabling users to examine how internal representations influence output. This capability allows for unique explorations of how models bind entities to attributes and the underlying mechanisms of their decision-making processes. Moreover, the Contrastive Steering technique provides users with the ability to adjust model outputs based on contrasting prompts, revealing how subtle changes can shift sentiment or formality in generated text. By making this level of detailed interpretability accessible, the mlx library significantly contributes to the ongoing quest to demystify complex AI systems, offering a valuable resource for both researchers and developers in the AI/ML community.