AI Model Optimization More Flexible Than Ever

Pruna 0.3.0 introduces a significant update to its internal framework, enhancing the flexibility and scalability of algorithm management. This refactor addresses previous limitations where algorithm groups, like cachers or quantizers, were rigidly structured, hindering the integration of new algorithms and their flexible application. Now, classifications act as supplementary metadata, allowing for a modular and composable design that supports new optimization techniques and custom pipelines without structural constraints. The update includes a more streamlined configuration interface, enabling users to define algorithm and hyperparameter settings efficiently using list and dictionary-style configurations. The algorithm execution order is now determined by compatibility rules and constraints, making the system more adaptable and capable of dynamically resolving valid combinations. Users can upgrade to this version without altering existing interfaces, and updated documentation provides guides and tutorials to facilitate the transition.