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1. The Efficiency Imperative and the Shift to Sparse Activation The evolution of large language models (LLMs) has been governed for nearly a decade by the scaling laws of dense Read More …
Executive Summary and Strategic Recommendations The deployment of state-of-the-art Large Language Models (LLMs) is fundamentally constrained by their extreme scale, resulting in prohibitive computational costs, vast memory footprints, and limited Read More …
Section 1: The Principle and Genesis of Knowledge Distillation 1.1. The Imperative for Model Efficiency: Computational Constraints in Modern AI The field of artificial intelligence has witnessed remarkable progress, largely Read More …
Section 1: The Imperative for Model Compression in the Era of Large-Scale AI 1.1 The Paradox of Scale in Modern AI The contemporary landscape of artificial intelligence is dominated by Read More …
Introduction: The Shifting Landscape of Sequence Modeling The field of sequence modeling was fundamentally reshaped in 2017 with the introduction of the Transformer architecture. Its core innovation, the self-attention mechanism, Read More …
Section 1: The Principle of Conditional Computation and the Genesis of Mixture of Depths The development of the Mixture of Depths (MoD) architecture represents a significant milestone in the ongoing Read More …
Part I: Foundational Principles of Sparse Architectures Section 1: Introduction – The Scaling Imperative and the Rise of Conditional Computation The trajectory of progress in large language models (LLMs) has Read More …
Introduction to Model Over-Parameterization and the Imperative for Efficiency The Challenge of Scaling Deep Learning Models The contemporary landscape of artificial intelligence is dominated by a paradigm of scale. The Read More …