Distilled Model

Distilled models aim to create smaller, faster, and more efficient versions of large, complex machine learning models while preserving their performance. Current research focuses on techniques like knowledge distillation, often employing various architectures (e.g., convolutional neural networks, transformers, LSTMs) and training strategies (e.g., data-free distillation, multi-step consistency distillation, contrastive learning) to achieve this goal. This research is significant because it addresses the challenges of deploying large models in resource-constrained environments and improves the accessibility and scalability of advanced AI technologies across diverse applications. The resulting distilled models offer a balance between performance and efficiency, making them valuable for both scientific exploration and practical deployment.