Lora Techniques For Large Language Model Adaptation
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Lora Techniques For Large Language Model Adaptation
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-13
Lora Techniques For Large Language Model Adaptation written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-13 with Computers categories.
"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
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Author : Oswald Campesato
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2024-12-26
Large Language Models For Developers written by Oswald Campesato and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-26 with Computers categories.
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
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Author : De-Shuang Huang
language : en
Publisher: Springer Nature
Release Date : 2025-07-21
Advanced Intelligent Computing Technology And Applications written by De-Shuang Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-21 with Computers categories.
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".
Building Conversational Generative Ai Apps With Langchain And Gpt
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Author : Mugesh S
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-06-04
Building Conversational Generative Ai Apps With Langchain And Gpt written by Mugesh S and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-04 with Computers categories.
TAGLINE Transform Text into Intelligent Conversations with LangChain and GPT. KEY FEATURES ● Build AI Chatbots with LangChain, Python and GPT models through hands-on guidance. ● Master Advanced Techniques like RAG, document embedding, and LLM fine-tuning. ● Deploy and Scale conversational AI systems for real-world applications. DESCRIPTION Conversational AI Apps are revolutionizing the way we interact with technology, enabling businesses and developers to create smarter, more intuitive applications that engage users in natural, meaningful ways. Building Conversational Generative AI Apps with LangChain and GPT is your ultimate guide to mastering AI-driven conversational systems. Starting with core concepts of generative AI and LLMs, you'll learn to build intelligent chatbots and virtual assistants, while exploring techniques like fine-tuning LLMs, retrieval-augmented generation (RAG), and document embedding. As you progress, you'll dive deeper into advanced topics such as vector databases and multimodal capabilities, enabling you to create highly accurate, context-aware AI agents. The book's step-by-step tutorials ensure that you develop practical skills in deploying and optimizing scalable conversational AI solutions. By the end, you'll be equipped to build AI apps that enhance customer engagement, automate workflows, and scale seamlessly. Unlock the potential of Building Conversational Generative AI Apps with LangChain and GPT and create next-gen AI applications today! WHAT WILL YOU LEARN ● Build and deploy AI-driven chatbots using LangChain and GPT models. ● Implement advanced techniques like retrieval-augmented generation (RAG) for smarter responses. ● Fine-tune LLMs for domain-specific conversational AI applications. ● Leverage vector databases for efficient knowledge retrieval and enhanced chatbot performance. ● Explore multimodal capabilities and document embedding for better context-aware responses. ● Optimize and scale conversational AI systems for large-scale deployments. WHO IS THIS BOOK FOR? This book is for developers, data scientists, and AI enthusiasts eager to build conversational applications using LangChain and GPT models. While a basic understanding of Python and machine learning concepts is beneficial, the book offers step-by-step guidance, making it accessible to both beginners and experienced practitioners. TABLE OF CONTENTS 1. Introduction to Conversational Generative AI 2. Natural Language Processing (NLP) Fundamentals 3. The Building Blocks of Rule-Based Chatbots 4. Statistical Language Models for Text Generation 5. Neural Network Architectures for Conversation 6. The Transformer Architecture Revolution 7. Unveiling ChatGPT and Architectures 8. Langchain Framework for Building Conversational AI 9. Exploring the LLM Landscape beyond GPT 10. The Transformative Impact of Conversational AI 11. Challenges and Opportunities in LLM Development Index
Generative Ai In Action
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Author : Amit Bahree
language : en
Publisher: Simon and Schuster
Release Date : 2024-10-29
Generative Ai In Action written by Amit Bahree and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Computers categories.
From the back cover: Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you'll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You'll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. About the reader: For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI.
Quick Start Guide To Large Language Models
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Author : Sinan Ozdemir
language : en
Publisher: Addison-Wesley Professional
Release Date : 2024-09-26
Quick Start Guide To Large Language Models written by Sinan Ozdemir and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-26 with Computers categories.
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Mastering Generative Ai Theory Techniques And Real World Applications
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Author : Dr. Mamatha B
language : en
Publisher: NC Publishers
Release Date : 2025-04-15
Mastering Generative Ai Theory Techniques And Real World Applications written by Dr. Mamatha B and has been published by NC Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Mastering Generative AI is a comprehensive textbook covering the theory, techniques, and real-world applications of generative models like GANs and transformers. It blends foundational concepts with practical coding exercises and ethical insights, making it ideal for students, researchers, and professionals in AI, data science, and related fields.
Building Applications With Large Language Models
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Author : Bhawna Singh
language : en
Publisher: Springer Nature
Release Date : 2024-11-29
Building Applications With Large Language Models written by Bhawna Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
This book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others. The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications. By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing. What You Will Learn Be able to answer the question: What are Large Language Models? Understand techniques such as prompt engineering, fine-tuning, RAG, and vector databases Know the best practices for effective implementation Know the metrics and frameworks essential for evaluating the performance of Large Language Models Who This Book Is For An essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs
Building Personality Driven Language Models
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Author : Karol Przystalski
language : en
Publisher: Springer Nature
Release Date : 2025-03-22
Building Personality Driven Language Models written by Karol Przystalski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-22 with Computers categories.
This book provides an innovative exploration into the realm of artificial intelligence (AI) by developing personalities for large language models (LLMs) using psychological principles. Aimed at making AI interactions feel more human-like, the book guides you through the process of applying psychological assessments to AIs, enabling them to exhibit traits such as extraversion, openness, and emotional stability. Perfect for developers, researchers, and entrepreneurs, this work merges psychology, philosophy, business, and cutting-edge computing to enhance how AIs understand and engage with humans across various industries like gaming and healthcare. The book not only unpacks the theoretical aspects of these advancements but also equips you with practical coding exercises and Python code examples, helping you create AI systems that are both innovative and relatable. Whether you’re looking to deepen your understanding of AI personalities or integrate them into commercial applications, this book offers the tools and insights needed to pioneer this exciting frontier.
Natural Language Processing And Chinese Computing
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Author : Derek F. Wong
language : en
Publisher: Springer Nature
Release Date : 2024-10-31
Natural Language Processing And Chinese Computing written by Derek F. Wong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-31 with Computers categories.
The five-volume set LNCS 15359 - 15363 constitutes the refereed proceedings of the 13th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024. The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Extraction and Knowledge Graph; Information Retrieval, Dialogue Systems, and Question Answering; Large Language Models and Agents; Machine Learning for NLP; Machine Translation and Multilinguality; Multi-modality and Explainability; NLP Applications and Text Mining; Sentiment Analysis, Argumentation Mining, and Social Media; Summarization and Generation.