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The Imperative for Efficiency in Model Adaptation The advent of large language models (LLMs) represents a paradigm shift in artificial intelligence, with foundation models pre-trained on vast datasets demonstrating remarkable Read More …
Part I: The Foundation – From Pre-Training to Specialization The evaluation of a fine-tuned Large Language Model (LLM) is intrinsically linked to the purpose and process of its creation. Understanding 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 …
Part 1: The Customization Triad: A Strategic Framework for LLM Adaptation 1.1 Introduction: Deconstructing the “vs.” The customization of Large Language Models (LLMs) is frequently framed as a choice between Read More …
Part 1: The Rationale for Parameter-Efficient Adaptation 1.1. The Adaptation Imperative: The “Fine-Tuning Crisis” The modern paradigm of natural language processing is built upon a two-stage process: large-scale, general-domain pre-training Read More …
I. Conceptual Foundations: Deconstructing State, Memory, and Context in Dialogue The efficacy of multi-turn conversational AI, from simple chatbots to complex generative agents, is predicated on its ability to comprehend Read More …
Executive Summary The proliferation of Large Language Models (LLMs) has been constrained by their immense computational and memory requirements, making efficient inference a critical area of research and development. Post-Training Read More …
Executive Summary This report charts the critical evolution of Large Language Models (LLMs) from reactive, stateless text predictors into proactive, reasoning agents. It argues that this transformation is achieved by Read More …
Section 1: Foundational Principles of Vector Data Management The advent of large-scale artificial intelligence has catalyzed a fundamental shift in how data is stored, managed, and queried. The architectural principles Read More …
The Anatomy of LLM Inference and Its Intrinsic Bottlenecks The deployment of Large Language Models (LLMs) in production environments has shifted the focus of the machine learning community from training-centric Read More …