Free Pruning

Free pruning techniques aim to reduce the size and computational cost of large language models (LLMs) and other neural networks without requiring retraining or access to the original training data. Current research focuses on developing efficient algorithms, often employing structured pruning methods targeting specific modules like multi-head attention and multi-layer perceptrons within Transformer architectures, or utilizing data-free approaches based on channel similarity or iterative pruning guided by metrics like perplexity or robustness. These advancements are significant because they enable deployment of smaller, faster models on resource-constrained devices while addressing privacy and security concerns associated with data-dependent pruning methods.