Building And Fine Tuning Llms From Scratch
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Build A Large Language Model From Scratch
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Author : Sebastian Raschka
language : en
Publisher: Simon and Schuster
Release Date : 2024-10-29
Build A Large Language Model From Scratch written by Sebastian Raschka 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: Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you'll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you'll really understand it because you built it yourself! About the reader: Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs.
Building Llms With Pytorch
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Author : Anand Trivedi
language : en
Publisher: BPB Publications
Release Date : 2025-03-13
Building Llms With Pytorch written by Anand Trivedi and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
DESCRIPTION PyTorch has become the go-to framework for building cutting-edge large language models (LLMs), enabling developers to harness the power of deep learning for natural language processing. This book serves as your practical guide to navigating the intricacies of PyTorch, empowering you to create your own LLMs from the ground up. You will begin by mastering PyTorch fundamentals, including tensors, autograd, and model creation, before diving into core neural network concepts like gradients, loss functions, and backpropagation. Progressing through regression and image classification with convolutional neural networks, you will then explore advanced image processing through object detection and segmentation. The book seamlessly transitions into NLP, covering RNNs, LSTMs, and attention mechanisms, culminating in the construction of Transformer-based LLMs, including a practical mini-GPT project. You will also get a strong understanding of generative models like VAEs and GANs. By the end of this book, you will possess the technical proficiency to build, train, and deploy sophisticated LLMs using PyTorch, equipping you to contribute to the rapidly evolving landscape of AI. WHAT YOU WILL LEARN ● Build and train PyTorch models for linear and logistic regression. ● Configure PyTorch environments and utilize GPU acceleration with CUDA. ● Construct CNNs for image classification and apply transfer learning techniques. ● Master PyTorch tensors, autograd, and build fundamental neural networks. ● Utilize SSD and YOLO for object detection and perform image segmentation. ● Develop RNNs and LSTMs for sequence modeling and text generation. ● Implement attention mechanisms and build Transformer-based language models. ● Create generative models using VAEs and GANs for diverse applications. ● Build and deploy your own mini-GPT language model, applying the acquired skills. WHO THIS BOOK IS FOR Software engineers, AI researchers, architects seeking AI insights, and professionals in finance, medical, engineering, and mathematics will find this book a comprehensive starting point, regardless of prior deep learning expertise. TABLE OF CONTENTS 1. Introduction to Deep Learning 2. Nuts and Bolts of AI with PyTorch 3. Introduction to Convolution Neural Network 4. Model Building with Custom Layers and PyTorch 2.0 5. Advances in Computer Vision: Transfer Learning and Object Detection 6. Advanced Object Detection and Segmentation 7. Mastering Object Detection with Detectron2 8. Introduction to RNNs and LSTMs 9. Understanding Text Processing and Generation in Machine Learning 10. Transformers Unleashed 11. Introduction to GANs: Building Blocks of Generative Models 12. Conditional GANs, Latent Spaces, and Diffusion Models 13. PyTorch 2.0: New Features, Efficient CUDA Usage, and Accelerated Model Training 14. Building Large Language Models from Scratch
Test Yourself On Build A Large Language Model From Scratch
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Author :
language : en
Publisher: Simon and Schuster
Release Date : 2025-07-22
Test Yourself On Build A Large Language Model From Scratch written by 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 2025-07-22 with Computers categories.
Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! Sebastian Raschka’s bestselling book Build a Large Language Model (From Scratch) is the best way to learn how Large Language Models function. It uses Python and the PyTorch deep learning library. It’s a unique way to learn this subject, which some believe is the only way to truly learn: you build a model yourself. Even with the clear explanations, diagrams, and code in the book, learning a complex subject is still hard. This Test Yourself guide intends to make it a little easier. The structure mirrors the structure of Build a Large Language Model (From Scratch), focusing on key concepts from each chapter. You can test yourself with multiple-choice quizzes, questions on code and key concepts, and questions with longer answers that push you to think critically. The answers to all questions are provided. Depending on what you know at any point, this Test Yourself guide can help you in different ways. It will solidify your knowledge if used after reading a chapter. But it will also benefit you if you digest it before reading. By testing yourself on the main concepts and their relationships you are primed to navigate a chapter more easily and be ready for its messages. We recommend using it before and after reading, as well as later when you have started forgetting. Repeated learning solidifies our knowledge and integrates it with related knowledge already in our long-term memory. What's inside • Questions on code and key concepts • Critical thinking exercises requiring longer answers • Answers for all questions About the reader For readers of Build a Large Language Model (From Scratch) who want to enhance their learning with exercises and self-assessment tools. About the author Curated from Build a Large Language Model (From Scratch)
Building Generative Ai Agents
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Author : Tom Taulli
language : en
Publisher: Springer Nature
Release Date : 2025-06-15
Building Generative Ai Agents written by Tom Taulli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-15 with Computers categories.
The dawn of AI agents is upon us. Tech visionaries like Bill Gates, Andrew Ng, and Vinod Khosla have highlighted the monumental potential of this powerful technology. This book will provide the knowledge and tools necessary to build generative AI agents using the most popular frameworks, such as AutoGen, LangChain, LangGraph, CrewAI, and Haystack. Recent breakthroughs in large language models have opened up unprecedented possibilities. After years of gradual progress in machine learning and deep learning, we are now witnessing novel approaches capable of understanding, reasoning, and generating content in ways that promise to revolutionize nearly every industry. This platform shift is as significant as the advent of mainframes, PCs, cloud computing, mobile technology, and social media. It’s why the world’s largest technology companies – like Microsoft, Apple, Google, and Meta – are making enormous investments in this category. While chatbots like ChatGPT, Claude, and Gemini have demonstrated remarkable potential, the years ahead will see the rise of generative AI agents capable of executing complex tasks on behalf of users. These agents already exhibit capabilities such as running test suites, searching the web for documentation, writing software, answering questions based on vast organized information, and performing intricate web-based tasks across multiple domains. They can autonomously investigate cybersecurity incidents and address complex customer support needs. By integrating skills, knowledge bases, planning frameworks, memory, and feedback loops, these systems can handle many tasks and improve over time. Building Generative AI Agents serves as a high-quality guide for developers to understand when and where AI agents can be useful, their advantages and disadvantages, and practical advice on designing, building, deploying, and monitoring them. What You Will Learn The foundational concepts, capabilities, and potential of AI agents. Recent innovations in large language models that have enabled the development of AI agents. How to build AI agents for launching a product, creating a financial plan, handling customer service, and using Retrieval Augmented Generation (RAG). Essential frameworks for building generative AI agents, including AutoGen, LangChain, LangGraph, CrewAI, and Haystack. Step-by-step guidance on designing, building, and deploying AI agents. Insights into the future of AI agents and their potential impact on various industries. Who This Book Is For Experienced software developers
Strategic Blueprint For Enterprise Analytics
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Author : Liang Wang
language : en
Publisher: Springer Nature
Release Date : 2024-04-12
Strategic Blueprint For Enterprise Analytics written by Liang Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.
This book is a comprehensive guide for professionals, leaders, and academics seeking to unlock the power of data and analytics in the modern business landscape. It delves deeply into the strategic, architectural, and managerial aspects of implementing enterprise analytics (EA) systems in large enterprises. The book is meticulously structured into three parts. Part 1 lays the foundation for adaptable architecture in EA. Part 2 explores technical considerations: data, cloud platforms, and AI solutions. The final part focuses on strategy execution, investment, and risk management. Acting as a comprehensive guide, the book enables the creation of robust EA capabilities that foster growth, optimize operations, and keep pace with EA's dynamic world. Whether readers are leaders harnessing data's potential, practitioners navigating analytics, or academics exploring this evolving domain, this book provides insights and knowledge to guide readers toward a thriving, data-driven future.
Building Intelligent Applications With Generative Ai
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Author : Yattish Ramhorry
language : en
Publisher: BPB Publications
Release Date : 2024-08-22
Building Intelligent Applications With Generative Ai written by Yattish Ramhorry and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-22 with Computers categories.
DESCRIPTION Building Intelligent Applications with Generative AI is a comprehensive guide that unlocks the power of generative AI for building cutting-edge applications. This book covers a wide range of use cases and practical examples, from text generation and conversational agents to creative media generation and code completion. These examples are designed to help you capitalize on the potential of generative AI in your applications. Through clear explanations, step-by-step tutorials, and real-world case studies, you will learn how to prepare data and train generative AI models. You will also explore different generative AI techniques, including large language models like GPT-4, ChatGPT, Llama 2, and Google’s Gemini, to understand how they can be applied in various domains, such as content generation, virtual assistants, and code generation. With a focus on practical implementation, this book also examines ethical considerations, best practices, and future trends in generative AI. Further, this book concludes by exploring ethical considerations and best practices for building responsible GAI applications, ensuring you are harnessing this technology for good. By the end of this book, you will be well-equipped to leverage the power of GAI to build intelligent applications and unleash your creativity in innovative ways. KEY FEATURES ● Learn the fundamentals of generative AI and the practical usage of prompt engineering. ● Gain hands-on experience in building generative AI applications. ● Learn to use tools like LangChain, LangSmith, and FlowiseAI to create intelligent applications and AI chatbots. WHAT YOU WILL LEARN ● Understand generative AI (GAI) and large language models (LLMs). ● Explore real-world GAI applications across industries. ● Build intelligent applications with the ChatGPT API. ● Explore retrieval augmented generation with LangChain and Gemini Pro. ● Create chatbots with LangChain and Streamlit for data retrieval. WHO THIS BOOK IS FOR This book is for developers, data scientists, AI practitioners, and tech enthusiasts who are interested in leveraging generative AI techniques to build intelligent applications across various domains. TABLE OF CONTENTS 1. Exploring the World of Generative AI 2. Use Cases for Generative AI Applications 3. Mastering the Art of Prompt Engineering 4. Integrating Generative AI Models into Applications 5. Emerging Trends and the Future of Generative AI 6. Building Intelligent Applications with the ChatGPT API 7. Retrieval Augmented Generation with Gemini Pro 8. Generative AI Applications with Gradio 9. Visualize your Data with LangChain and Streamlit 10. Building LLM Applications with Llama 2 11. Building an AI Document Chatbot with Flowise AI 12. Best Practices for Building Applications with Generative AI 13. Ethical Considerations of Generative AI
Building Neo4j Powered Applications With Llms
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Author : Ravindranatha Anthapu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-20
Building Neo4j Powered Applications With Llms written by Ravindranatha Anthapu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-20 with Computers categories.
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilities Key Features Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j Apply best practices for graph exploration, modeling, reasoning, and performance optimization Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEmbark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j. As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI’s most persistent challenges—mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses. Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you’ll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud. By the end of this book, you’ll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications.What you will learn Design, populate, and integrate a Neo4j knowledge graph with RAG Model data for knowledge graphs Integrate AI-powered search to enhance knowledge exploration Maintain and monitor your AI search application with Haystack Use LangChain4j and Spring AI for recommendations and personalization Seamlessly deploy your applications to Google Cloud Platform Who this book is for This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy.
Building Llms For Production
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Author : Louis-François Bouchard
language : en
Publisher: Towards AI, Inc.
Release Date : 2024-05-21
Building Llms For Production written by Louis-François Bouchard and has been published by Towards AI, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-21 with Computers categories.
“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit." - Jerry Liu, Co-founder and CEO of LlamaIndex (THE BOOK WAS UPDATED ON OCTOBER 2024) With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python. What's Inside this 470-page Book (Updated October 2024)? - Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning - Roadmap for Building Production-Ready Applications using LLMs - Fundamentals of LLM Theory - Simple-to-Advanced LLM Techniques & Frameworks - Code Projects with Real-World Applications - Colab Notebooks that you can run right away Community access and our own AI Tutor Table of Contents - Chapter I Introduction to Large Language Models - Chapter II LLM Architectures & Landscape - Chapter III LLMs in Practice - Chapter IV Introduction to Prompting - Chapter V Retrieval-Augmented Generation - Chapter VI Introduction to LangChain & LlamaIndex - Chapter VII Prompting with LangChain - Chapter VIII Indexes, Retrievers, and Data Preparation - Chapter IX Advanced RAG - Chapter X Agents - Chapter XI Fine-Tuning - Chapter XII Deployment and Optimization Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way.
Llms In Production
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Author : Christopher Brousseau
language : en
Publisher: Simon and Schuster
Release Date : 2025-02-11
Llms In Production written by Christopher Brousseau 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 2025-02-11 with Computers categories.
Learn how to put Large Language Model-based applications into production safely and efficiently. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. Table of Contents 1 Words’ awakening: Why large language models have captured attention 2 Large language models: A deep dive into language modeling 3 Large language model operations: Building a platform for LLMs 4 Data engineering for large language models: Setting up for success 5 Training large language models: How to generate the generator 6 Large language model services: A practical guide 7 Prompt engineering: Becoming an LLM whisperer 8 Large language model applications: Building an interactive experience 9 Creating an LLM project: Reimplementing Llama 3 10 Creating a coding copilot project: This would have helped you earlier 11 Deploying an LLM on a Raspberry Pi: How low can you go? 12 Production, an ever-changing landscape: Things are just getting started A History of linguistics B Reinforcement learning with human feedback C Multimodal latent spaces
Building Llm Powered Applications
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Author : Valentina Alto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-22
Building Llm Powered Applications written by Valentina Alto and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Computers categories.
Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.