Lora Techniques For Large Language Model Adaptation
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LoRA Techniques for Large Language Model Adaptation
"LoRA Techniques for Large Language Model Adaptation" "LoRA Techniques for Large Language Model Adaptation" offers a comprehensive deep dive into the principles, mechanics, and practicalities of adapting large language models (LLMs) using Low-Rank Adaptation (LoRA). Beginning with an insightful overview of the evolution and scaling of LLMs, the book systematically addresses the challenges inherent in adapting foundation models, highlighting why traditional fine-tuning methods often fall short in efficiency and scalability. Drawing on real-world use cases and the burgeoning adoption of LoRA across both research and industry, it situates readers at the cutting edge of parameter-efficient fine-tuning techniques. The work stands out for its rigorous treatment of the mathematical and engineering foundations underpinning LoRA. Through detailed explorations of low-rank matrix decomposition, formal parameter mappings, and empirical strategies for rank selection, readers gain a robust understanding of both the theoretical expressivity and practical impact of LoRA compared to other adaptation techniques. The text moves beyond the abstract, offering actionable guidance for integrating LoRA into modern transformer architectures, optimizing training for scalability and resource constraints, and leveraging composable and hybrid approaches to meet diverse adaptation goals. Bridging theory and application, the book culminates in advanced chapters on operationalizing LoRA in real-world settings, evaluating adaptation effectiveness, and innovating for next-generation language models. It presents a rich collection of strategies for serving LoRA-augmented models in production, maintaining long-term adaptability, and meeting the needs of privacy-conscious environments. Through tutorials, case studies, and a survey of open-source tools, "LoRA Techniques for Large Language Model Adaptation" provides a definitive resource for machine learning practitioners, researchers, and engineers seeking to master the art and science of efficient large model adaptation.
Large Language Models for Developers
Author: Oswald Campesato
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2024-12-26
This book offers a thorough exploration of Large Language Models (LLMs), guiding developers through the evolving landscape of generative AI and equipping them with the skills to utilize LLMs in practical applications. Designed for developers with a foundational understanding of machine learning, this book covers essential topics such as prompt engineering techniques, fine-tuning methods, attention mechanisms, and quantization strategies to optimize and deploy LLMs. Beginning with an introduction to generative AI, the book explains distinctions between conversational AI and generative models like GPT-4 and BERT, laying the groundwork for prompt engineering (Chapters 2 and 3). Some of the LLMs that are used for generating completions to prompts include Llama-3.1 405B, Llama 3, GPT-4o, Claude 3, Google Gemini, and Meta AI. Readers learn the art of creating effective prompts, covering advanced methods like Chain of Thought (CoT) and Tree of Thought prompts. As the book progresses, it details fine-tuning techniques (Chapters 5 and 6), demonstrating how to customize LLMs for specific tasks through methods like LoRA and QLoRA, and includes Python code samples for hands-on learning. Readers are also introduced to the transformer architecture’s attention mechanism (Chapter 8), with step-by-step guidance on implementing self-attention layers. For developers aiming to optimize LLM performance, the book concludes with quantization techniques (Chapters 9 and 10), exploring strategies like dynamic quantization and probabilistic quantization, which help reduce model size without sacrificing performance. FEATURES • Covers the full lifecycle of working with LLMs, from model selection to deployment • Includes code samples using practical Python code for implementing prompt engineering, fine-tuning, and quantization • Teaches readers to enhance model efficiency with advanced optimization techniques • Includes companion files with code and images -- available from the publisher
Advanced Intelligent Computing Technology and Applications
The 12-volume set CCIS 2564-2575, together with the 28-volume set LNCS/LNAI/LNBI 15842-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 523 papers presented in these proceedings books were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".