Create Your Own Large Language Model

  create your own large language model: Build a Large Language Model (From Scratch) Sebastian Raschka, 2024-10-29 Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book 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! What's inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions 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. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA
  create your own large language model: Training Your Own Large Language Model StoryBuddiesPlay, 2024-04-26 Demystify the Power of Language with Large Language Models: Your Comprehensive Guide The ability to understand and generate human language is a cornerstone of human intelligence. Artificial intelligence (AI) is rapidly evolving, and Large Language Models (LLMs) are at the forefront of this revolution. These powerful AI tools can process and generate text with remarkable fluency, making them ideal for various applications. This comprehensive guide empowers you to step into the exciting world of LLMs and train your own! Whether you're a seasoned developer, an AI enthusiast, or simply curious about the future of language technology, this book equips you with the knowledge and tools to navigate the LLM landscape. Within these pages, you'll discover: The transformative potential of LLMs: Explore the various tasks LLMs can perform, from generating creative text formats to answering your questions in an informative way, and even translating languages. A step-by-step approach to LLM training: Learn how to define your project goals, identify the right data sources, and choose the optimal LLM architecture for your needs. Essential tools and techniques: Gain insights into popular frameworks like TensorFlow and PyTorch, and delve into practical aspects like data pre-processing and hyperparameter tuning. Fine-tuning and deployment strategies: Unleash the full potential of your LLM by tailoring it to specific tasks and seamlessly integrating it into your applications or workflows. The future of LLMs: Explore cutting-edge advancements like explainable AI and lifelong learning, and discover the potential impact of LLMs on various aspects of society. By the time you finish this guide, you'll be equipped to: Confidently define and plan your LLM project. Train your own LLM using powerful AI frameworks and techniques. Fine-tune your LLM for real-world applications. Deploy and integrate your LLM for seamless functionality. Contribute to the ever-evolving field of large language models. Don't wait any longer! Dive into the world of LLMs and unlock the power of language manipulation with this comprehensive guide. Get started on your LLM journey today!
  create your own large language model: A Beginner's Guide to Large Language Models Enamul Haque, 2024-07-25 A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts Step into the revolutionary world of artificial intelligence with A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts. Whether you're a curious individual or a professional seeking to leverage AI in your field, this book demystifies the complexities of large language models (LLMs) with engaging, easy-to-understand explanations and practical insights. Explore the fascinating journey of AI from its early roots to the cutting-edge advancements that power today's conversational AI systems. Discover how LLMs, like ChatGPT and Google's Gemini, are transforming industries, enhancing productivity, and sparking creativity across the globe. With the guidance of this comprehensive and accessible guide, you'll gain a solid understanding of how LLMs work, their real-world applications, and the ethical considerations they entail. Packed with vivid examples, hands-on exercises, and real-life scenarios, this book will empower you to harness the full potential of LLMs. Learn to generate creative content, translate languages in real-time, summarise complex information, and even develop AI-powered applications—all without needing a technical background. You'll also find valuable insights into the evolving job landscape, equipping you with the knowledge to pursue a successful career in this dynamic field. This guide ensures that AI is not just an abstract concept but a tangible tool you can use to transform your everyday life and work. Dive into the future with confidence and curiosity, and discover the incredible possibilities that large language models offer. Join the AI revolution and unlock the secrets of the technology that's reshaping our world. A Beginner's Guide to Large Language Models is your key to understanding and mastering the power of conversational AI. Introduction This introduction sets the stage for understanding the evolution of artificial intelligence (AI) and large language models (LLMs). It highlights the promise of making complex AI concepts accessible to non-technical readers and outlines the unique approach of this book. Chapter 1: Demystifying AI and LLMs: A Journey Through Time This chapter introduces the basics of AI, using simple analogies and real-world examples. It traces the evolution of AI, from rule-based systems to machine learning and deep learning, leading to the emergence of LLMs. Key concepts such as tokens, vocabulary, and embeddings are explained to build a solid foundation for understanding how LLMs process and generate language. Chapter 2: Mastering Large Language Models Delving deeper into the mechanics of LLMs, this chapter covers the transformer architecture, attention mechanisms, and the processes involved in training and fine-tuning LLMs. It includes hands-on exercises with prompts and discusses advanced techniques like chain-of-thought prompting and prompt chaining to optimise LLM performance. Chapter 3: The LLM Toolbox: Unleashing the Power of Language AI This chapter explores the diverse applications of LLMs in text generation, language translation, summarisation, question answering, and code generation. It also introduces multimodal LLMs that handle both text and images, showcasing their impact on various creative and professional fields. Practical examples and real-life scenarios illustrate how these tools can enhance productivity and creativity. Chapter 4: LLMs in the Real World: Transforming Industries Highlighting the transformative impact of LLMs across different industries, this chapter covers their role in healthcare, finance, education, creative industries, and business. It discusses how LLMs are revolutionising tasks such as medical diagnosis, fraud detection, personalised tutoring, and content creation, and explores the future of work in an AI-powered world. Chapter 5: The Dark Side of LLMs: Ethical Concerns and Challenges Addressing the ethical challenges of LLMs, this chapter covers bias and fairness, privacy concerns, misuse of LLMs, security threats, and the transparency of AI decision-making. It also discusses ethical frameworks for responsible AI development and presents diverse perspectives on the risks and benefits of LLMs. Chapter 6: Mastering LLMs: Advanced Techniques and Strategies This chapter focuses on advanced techniques for leveraging LLMs, such as combining transformers with other AI models, fine-tuning open-source LLMs for specific tasks, and building LLM-powered applications. It provides detailed guidance on prompt engineering for various applications and includes a step-by-step guide to creating an AI-powered chatbot. Chapter 7: LLMs and the Future: A Glimpse into Tomorrow Looking ahead, this chapter explores emerging trends and potential breakthroughs in AI and LLM research. It discusses ethical AI development, insights from leading AI experts, and visions of a future where LLMs are integrated into everyday life. The chapter highlights the importance of building responsible AI systems that address societal concerns. Chapter 8: Your LLM Career Roadmap: Navigating the AI Job Landscape Focusing on the growing demand for LLM expertise, this chapter outlines various career paths in the AI field, such as LLM scientists, engineers, and prompt engineers. It provides resources for building the necessary skillsets and discusses the evolving job market, emphasising the importance of continuous learning and adaptability in a rapidly changing industry. Thought-Provoking Questions, Simple Exercises, and Real-Life Scenarios The book concludes with practical exercises and real-life scenarios to help readers apply their knowledge of LLMs. It includes thought-provoking questions to deepen understanding and provides resources and tools for further exploration of LLM applications. Tools to Help with Your Exercises This section lists tools and platforms for engaging with LLM exercises, such as OpenAI's Playground, Google Translate, and various IDEs for coding. Links to these tools are provided to facilitate hands-on learning and experimentation.
  create your own large language model: Large Language Models Projects Pere Martra,
  create your own large language model: Python Machine Learning Sebastian Raschka, 2015-09-23 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
  create your own large language model: Machine Learning with PyTorch and Scikit-Learn Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, 2022-02-25 This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
  create your own large language model: Large Language Models John Atkinson-Abutridy, 2024-10-17 This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more. At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction: •You will explore the fascinating world of LLMs, from its foundations to its most powerful applications •You will learn how to build your own simple applications with some of the LLMs Designed to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP. From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.
  create your own large language model: Supervised Machine Learning for Text Analysis in R Emil Hvitfeldt, Julia Silge, 2021-10-22 Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
  create your own large language model: Generative Deep Learning David Foster, 2019-06-28 Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
  create your own large language model: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
  create your own large language model: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
  create your own large language model: Large Language Model-Based Solutions Shreyas Subramanian, 2024-04-02 Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
  create your own large language model: Make: Volume 91 Dale Dougherty, 2024-10-29 In this issue of Make: we make friends — literally! Build your own companion robot with a Raspberry Pi 5, and then give it a voice using AI and a large language model running locally. No internet required! Or keep it simple and build a friendly bot with a micro:bit and a few servos. Next, get an overview of the latest new dev boards, including offerings from Adafruit, Seeed, Sparkfun, Pimoroni, and more, that use Raspberry Pi’s second-gen, double dual-core RP2350 chip. And, get started with new Arduino libraries and example projects for cheap ESP32+LCD boards. Special Bonus — Make: Guide to Boards 2025 You know Raspberry Pi and Arduino, but the waters run deep for microcontrollers and single board computers. From wearables, to Wi-Fi and Bluetooth, to AI capabilities, we show you 77 new boards that have exactly what you’re looking for to power your next project. Plus, 38+ projects: Embed tiny mirrors and mesh into your 3D prints to create sparkling fabrics Build an autotune kazoo Make a battery using your favorite sports drink Laser cut a creative ski chalet birdhouse for your feathered friends Use an Arduino for professional looking DMX lighting Make a walk-in camera obscura to project the outside world inside (and upside down) Expose spy tech with the budget K18 Bug Detector And much more!
  create your own large language model: Introduction to Python and Large Language Models Dilyan Grigorov,
  create your own large language model: Deep Learning for Natural Language Processing Jason Brownlee, 2017-11-21 Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.
  create your own large language model: Pretrain Vision and Large Language Models in Python Emily Webber, Andrea Olgiati, 2023-05-31 Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examples Key Features Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines Explore large-scale distributed training for models and datasets with AWS and SageMaker examples Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring Book Description Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization. With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you'll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models. You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines. By the end of this book, you'll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future. What you will learn Find the right use cases and datasets for pretraining and fine-tuning Prepare for large-scale training with custom accelerators and GPUs Configure environments on AWS and SageMaker to maximize performance Select hyperparameters based on your model and constraints Distribute your model and dataset using many types of parallelism Avoid pitfalls with job restarts, intermittent health checks, and more Evaluate your model with quantitative and qualitative insights Deploy your models with runtime improvements and monitoring pipelines Who this book is for If you're a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way.
  create your own large language model: Building Transformer Models with PyTorch 2.0 Prem Timsina, 2024-03-08 Your key to transformer based NLP, vision, speech, and multimodalities KEY FEATURES ● Transformer architecture for different modalities and multimodalities. ● Practical guidelines to build and fine-tune transformer models. ● Comprehensive code samples with detailed documentation. DESCRIPTION This book covers transformer architecture for various applications including NLP, computer vision, speech processing, and predictive modeling with tabular data. It is a valuable resource for anyone looking to harness the power of transformer architecture in their machine learning projects. The book provides a step-by-step guide to building transformer models from scratch and fine-tuning pre-trained open-source models. It explores foundational model architecture, including GPT, VIT, Whisper, TabTransformer, Stable Diffusion, and the core principles for solving various problems with transformers. The book also covers transfer learning, model training, and fine-tuning, and discusses how to utilize recent models from Hugging Face. Additionally, the book explores advanced topics such as model benchmarking, multimodal learning, reinforcement learning, and deploying and serving transformer models. In conclusion, this book offers a comprehensive and thorough guide to transformer models and their various applications. WHAT YOU WILL LEARN ● Understand the core architecture of various foundational models, including single and multimodalities. ● Step-by-step approach to developing transformer-based Machine Learning models. ● Utilize various open-source models to solve your business problems. ● Train and fine-tune various open-source models using PyTorch 2.0 and the Hugging Face ecosystem. ● Deploy and serve transformer models. ● Best practices and guidelines for building transformer-based models. WHO THIS BOOK IS FOR This book caters to data scientists, Machine Learning engineers, developers, and software architects interested in the world of generative AI. TABLE OF CONTENTS 1. Transformer Architecture 2. Hugging Face Ecosystem 3. Transformer Model in PyTorch 4. Transfer Learning with PyTorch and Hugging Face 5. Large Language Models: BERT, GPT-3, and BART 6. NLP Tasks with Transformers 7. CV Model Anatomy: ViT, DETR, and DeiT 8. Computer Vision Tasks with Transformers 9. Speech Processing Model Anatomy: Whisper, SpeechT5, and Wav2Vec 10. Speech Tasks with Transformers 11. Transformer Architecture for Tabular Data Processing 12. Transformers for Tabular Data Regression and Classification 13. Multimodal Transformers, Architectures and Applications 14. Explore Reinforcement Learning for Transformer 15. Model Export, Serving, and Deployment 16. Transformer Model Interpretability, and Experimental Visualization 17. PyTorch Models: Best Practices and Debugging
  create your own large language model: The Complete Obsolete Guide to Generative AI David Clinton, 2024-08-20 The last book on AI you’ll ever need. We swear! AI technology moves so fast that this book is probably already out of date! But don’t worry—The Complete Obsolete Guide to Generative AI is still an essential read for anyone who wants to make generative AI into a tool rather than a toy. It shows you how to get the best out of AI no matter what changes come in the future. You’ll be able to use common automation and scripting tools to take AI to a new level, and access raw (and powerful) GPT models via API. Inside The Complete Obsolete Guide to Generative AI you will find: • Just enough background info on AI! What an AI model is how it works • Ways to create text, code, and images for your organization's needs • Training AI models on your local data stores or on the internet • Business intelligence and analytics uses for AI • Building your own custom AI models • Looking ahead to the future of generative AI Where to get started? How about creating exciting images, video, and even audio with AI. Need more? Learn to harness AI to speed up any everyday work task, including writing boilerplate code, creating specialized documents, and analyzing your own data. Push beyond simple ChatGPT prompts! Discover ways to double your productivity and take on projects you never thought were possible! AI—and this book—are here to show you how. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Everything you learn about Generative AI tools like Chat-GPT, Copilot, and Claude becomes obsolete almost immediately. So how do you decide where to spend your time—and your company’s money? This entertaining and unbelievably practical book shows you what you can (and should!) do with AI now and how to roll with the changes as they happen. About the book The Complete Obsolete Guide to Generative AI is a lighthearted introduction to Generative AI written for technology professionals and motivated AI enthusiasts. In it, you’ll get a quick-paced survey of AI techniques for creating code, text, images, and presentations, working with data, and much more. As you explore the hands-on exercises, you’ll build an intuition for how Generative AI can transform your daily work and communication—and maybe even learn how to make peace with your new robot overlords. What's inside • The big picture of Generative AI tools and tech • Creating useful text, code, and images • Writing effective prompts • AI-driven data analytics About the reader Written for developers, admins, and other IT pros. Some examples use simple Python code. About the author David Clinton is an AWS Solutions Architect, a Linux server administrator and a world-renowned expert on obsolescence. The technical editor on this book was Maris Sekar. Table of Contents 1 Understanding generative AI basics 2 Managing generative AI 3 Creating text and code 4 Creating with media resources 5 Feeding data to your generative AI models 6 Prompt engineering: Optimizing your experience 7 Outperforming legacy research and learning tools 8 Understanding stuff better 9 Building and running your own large language model 10 How I learned to stop worrying and love the chaos 11 Experts weigh in on putting AI to work A Important definitions and a brief history B Generative AI resources C Installing Python
  create your own large language model: Generative Deep Learning David Foster, 2022-06-28 Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
  create your own large language model: ChatGPT for Conversational AI and Chatbots Adrian Thompson, 2024-07-30 Explore ChatGPT technologies to create state-of-the-art chatbots and voice assistants, and prepare to lead the AI revolution Key Features Learn how to leverage ChatGPT to create innovative conversational AI solutions for your organization Harness LangChain and delve into step-by-step LLM application development for conversational AI Gain insights into security, privacy, and the future landscape of large language models and conversational AI Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionChatGPT for Conversational AI and Chatbots is a definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You’ll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you’ll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you’ll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. You’ll also cover popular use cases, such as using ChatGPT in conjunction with your own data. Later, the book focuses on creating a ChatGPT-powered chatbot that can comprehend and respond to queries directly from your unique data sources. The book then guides you through building chatbot UIs with ChatGPT API and some of the tools and best practices available. By the end of this book, you’ll be able to confidently leverage ChatGPT technologies to build conversational AI solutions.What you will learn Gain a solid understanding of ChatGPT and its capabilities and limitations Understand how to use ChatGPT for conversation design Discover how to use advanced LangChain techniques, such as prompting, memory, agents, chains, vector stores, and tools Create a ChatGPT chatbot that can answer questions about your own data Develop a chatbot powered by ChatGPT API Explore the future of conversational AI, LLMs, and ChatGPT alternatives Who this book is for This book is for tech-savvy readers, conversational AI practitioners, engineers, product owners, business analysts, and entrepreneurs wanting to integrate ChatGPT into conversational experiences and explore the possibilities of this game-changing technology. Anyone curious about using internal data with ChatGPT and looking to stay up to date with the developments in large language models will also find this book helpful. Some expertise in coding and standard web design concepts would be useful, along with familiarity with conversational AI terminology, though not essential.
  create your own large language model: Mastering Large Language Models Sanket Subhash Khandare, 2024-03-12 Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact
  create your own large language model: Hands-On Salesforce Data Cloud Joyce Kay Avila, 2024-08-09 Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data. Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks. This book will help you: Develop a plan to execute a CDP project effectively and efficiently Connect Data Cloud to external data sources and build out a Customer 360 Data Model Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure Use Salesforce Data Cloud capabilities for identity resolution and segmentation Create calculated, streaming, visualization, and predictive insights Use Data Graphs to power Salesforce Einstein capabilities Learn Data Cloud best practices for all phases of the development lifecycle
  create your own large language model: Generative AI Ravindra Das, 2024-10-10 The cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks. The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website. But Generative AI can also be used for the good to combat Phishing Attacks. This is the topic of this book. In this, we cover the following: A review of phishing A review of AI, Neural Networks, and Machine Learning A review of Natural Language Processing, Generative AI, and the Digital Person A proposed solution as to how Generative AI can combat phishing attacks as they relate to Privileged Access accounts
  create your own large language model: THE AMAZING AI – VENGERS Siddharth Bhargava, 2024-05-17 Crack the AI Code: Your Easy Guide to the Future Unlock the world of Artificial Intelligence (AI) with this friendly guide designed for non-technical readers. Gain a clear understanding of AI fundamentals, including machine learning, neural networks, large language models, and how they power cutting-edge technologies. Learn how to communicate effectively with AI tools using simple prompt engineering techniques and explore the exciting ways AI is transforming how we live and work. Whether you're a curious learner or a forward-thinking professional, this book is your passport to a future powered by AI.
  create your own large language model: Unlocking the Power of Auto-GPT and Its Plugins Wladislav Cugunov, 2024-09-13 Harness the revolutionary power of Auto-GPT and its plugins to transform your projects with advanced AI capabilities Key Features Discover the untapped power of Auto-GPT, opening doors to limitless AI possibilities Craft your own AI applications, from chat assistants to speech companions, with step-by-step guidance Explore advanced AI topics like Docker configuration and LLM integration for cutting-edge AI development Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnlocking the Power of Auto-GPT and Its Plugins reveals how Auto-GPT is transforming the way we work and live, by breaking down complex goals into manageable subtasks and intelligently utilizing the internet and other tools. With a background as a self-taught full stack developer and key contributor to Auto-GPT’s Inner Team, the author blends unconventional thinking with practical expertise to make Auto-GPT and its plugins accessible to developers at all levels. This book explores the potential of Auto-GPT and its associated plugins through practical applications. Beginning with an introduction to Auto-GPT, it guides you through setup, utilization, and the art of prompt generation. You'll gain a deep understanding of the various plugin types and how to create them. The book also offers expert guidance on developing AI applications such as chat assistants, research aides, and speech companions, while covering advanced topics such as Docker configuration, continuous mode operation, and integrating your own LLM with Auto-GPT. By the end of this book, you'll be equipped with the knowledge and skills needed for AI application development, plugin creation, setup procedures, and advanced Auto-GPT features to fuel your AI journey.What you will learn Develop a solid understanding of Auto-GPT's fundamental principles Hone your skills in creating engaging and effective prompts Effectively harness the potential of Auto-GPT's versatile plugins Tailor and personalize AI applications to meet specific requirements Proficiently manage Docker configurations for advanced setup Ensure the safe and efficient use of continuous mode Integrate your own LLM with Auto-GPT for enhanced performance Who this book is for This book is for developers, data scientists, and AI enthusiasts interested in leveraging the power of Auto-GPT and its plugins to create powerful AI applications. Basic programming knowledge and an understanding of artificial intelligence concepts are required to make the most of this book. Familiarity with the terminal will also be helpful.
  create your own large language model: c't Working with AI c't-Redaktion, 2024-01-24 The special issue of c't KI-Praxis provides tests and practical instructions for working with chatbots. It explains why language models make mistakes and how they can be minimised. This not only helps when you send questions and orders to one of the chatbots offered online. If you do not want to or are not allowed to use the cloud services for data protection reasons, for example, you can also set up your own voice AI. The c't editorial team explains where to find a suitable voice model, how to host it locally and which service providers can host it. The fact that generative AI is becoming increasingly productive harbours both opportunities and risks. Suitable rules for the use of AI in schools, training and at work help to exploit opportunities and minimise risks.
  create your own large language model: The Visual Revolution Guidebook Roz Morris, 2024-08-22 The visual economy is here and we are all broadcasters now! In today’s fast-paced world of constant media, moving images and digital presence, broadcasting is no longer the preserve of the privileged few. When every visual choice has the power to make or break reputations, success in this highly competitive economy hinges on mastering the skills of visual communication. The Visual Revolution Guidebook is your essential toolkit for navigating and leveraging this new visual-centric landscape and understanding its dynamics in the modern business environment. Media expert Roz Morris delves deep into the strategies and skills you need to stand out and flourish amidst the constant imagery of the modern 24-hour media cycle. Through illuminating case studies, proven approaches and user-friendly, practical advice and checklists, you’ll be equipped with an impressive range of advanced media skills, including how to: > Craft a compelling online presence. > Produce impactful promotional videos. > Fine-tune presentation skills across diverse media platforms. > Shine in media interviews with confidence and precision. > Harness the incredible potential of the metaverse. > Understand the evolving role of influencers. Whether you’re an entrepreneur, marketer or business leader seeking to understand and excel in modern business communication, this is more than just a must-read – it’s an expert and indispensable roadmap to thriving in the visual revolution.
  create your own large language model: Copilot for Microsoft 365 Jess Stratton,
  create your own large language model: Strategy Guide for Automation Magnus Glantz, 2023-08-11 Learn how to develop and implement a sustainable and scalable automation strategy KEY FEATURES ● Get familiar with the essential elements of a successful automation strategy. ● Understand how to incorporate emerging technologies into your automation strategy to improve efficiency and productivity. ● Learn how to design and implement a secure, reliable, and scalable IT automation architecture. DESCRIPTION Automation can be a powerful tool to streamline and scale a business effectively. By automating repetitive tasks, businesses can save time, reduce errors, and improve overall efficiency, allowing them to focus on more strategic and value-added activities. This book is an essential guide to automation. It highlights the importance of automation and provides guidance on how to implement it effectively. The book will help you learn how to set clear and achievable automation goals that align with your overall business strategy. It will also help you gain insights into selecting the right automation tools and technologies for your specific needs. Additionally, the book will walk you through the process of creating a sustainable and scalable automation strategy. With the skills you learn in this book, you will be able to measure and monitor the success of your automation program, so you can accurately evaluate the impact of your investments. WHAT YOU WILL LEARN ● Get a comprehensive understanding of modern IT automation. ● Understand what a successful IT automation strategy includes. ● Stay up-to-date on the latest trends in IT automation. ● Identify flawed and faulty IT automation strategies. ● Gain insights into topics such as security, HA/DR, technology selection, and more. WHO THIS BOOK IS FOR This book is for leaders, architects, and automation creators who want to understand the role of IT automation in the modern enterprise. TABLE OF CONTENTS Part 1: Introduction 1. Success of Automation 2. Ways to Redefine Automation 3. Key Elements of Implementing Automation Strategy Part 2: Creating Successful Automation Strategy 4. Things that Matter: Budget and Ownership 5. Performance Monitoring of Automation Strategy 6. Selecting Right Tools and Platform 7. Approach to Automation Skill Development 8. Key Processes for Development and Cross-team Collaboration 9. Catering for a Digitized Future Part 3: Automation for Architecture that Matters 10. Scaling Up Automation to Organization-wide 11. Establishing High Availability and Disaster Recovery 12. Security and Separation of Duty Requirements 13. Explore Automation-as-a-Service (AaaS)
  create your own large language model: Mastering Large Language Models with Python Raj Arun R, 2024-04-12 A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
  create your own large language model: Prompt Engineering for LLMs John Berryman, Albert Ziegler, 2024-11-04 Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs. Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications. Understand LLM architecture and learn how to best interact with itDesign a complete prompt-crafting strategy for an applicationGather, triage, and present context elements to make an efficient promptMaster specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG
  create your own large language model: Augmenting Public Relations David Phillips, 2024-10-21 Augmenting Public Relations examines how existing technologies used in Public Relations (PR) are being significantly augmented because of the advent of Artificial Intelligence (AI). The book describes the opportunities and pitfalls of AI, recent and emerging technologies, and projections in their development, offering an introduction to practitioners on how they, too, can create their own AI-enhanced tools. The developments in augmented, virtual and meta-reality, aided by AI, have now become serious contenders for commercial communication, and the ability to harness this visual capability is explained in some detail. As is the ability for practitioners to automatically monitor and feed websites using Application Programming Interfaces (APIs). The book also considers computer games as a form of communication, and the evolving application of games supported by AI. In recent years, the PR monitoring industry has deployed AI to search for content of interest to clients across a vast range of media. It throws up huge amounts of data to be managed. The book explores how such resources can be harnessed for intelligence gathering and activity deployment in easy-to-understand language. The book also covers a range of other activities from ‘brain to computer communication’ to chatbots, including applications used by the Internet of Things, Security Issues and Crisis Management, and the crucial subject of Ethics. Examining a range of new practices for the PR industry, and covering both principles and applications, this book will be of great value to students, academics and practitioners alike.
  create your own large language model: Practical Threat Detection Engineering Megan Roddie, Jason Deyalsingh, Gary J. Katz, 2023-07-21 Go on a journey through the threat detection engineering lifecycle while enriching your skill set and protecting your organization Key Features Gain a comprehensive understanding of threat validation Leverage open-source tools to test security detections Harness open-source content to supplement detection and testing Book DescriptionThreat validation is an indispensable component of every security detection program, ensuring a healthy detection pipeline. This comprehensive detection engineering guide will serve as an introduction for those who are new to detection validation, providing valuable guidelines to swiftly bring you up to speed. The book will show you how to apply the supplied frameworks to assess, test, and validate your detection program. It covers the entire life cycle of a detection, from creation to validation, with the help of real-world examples. Featuring hands-on tutorials and projects, this guide will enable you to confidently validate the detections in your security program. This book serves as your guide to building a career in detection engineering, highlighting the essential skills and knowledge vital for detection engineers in today's landscape. By the end of this book, you’ll have developed the skills necessary to test your security detection program and strengthen your organization’s security measures.What you will learn Understand the detection engineering process Build a detection engineering test lab Learn how to maintain detections as code Understand how threat intelligence can be used to drive detection development Prove the effectiveness of detection capabilities to business leadership Learn how to limit attackers' ability to inflict damage by detecting any malicious activity early Who this book is for This book is for security analysts and engineers seeking to improve their organization’s security posture by mastering the detection engineering lifecycle. To get started with this book, you’ll need a basic understanding of cybersecurity concepts, along with some experience with detection and alert capabilities.
  create your own large language model: Transforming Conversational AI Michael McTear,
  create your own large language model: ChatGPT eBook GURMEET SINGH DANG,
  create your own large language model: Hands-On Large Language Models Jay Alammar, Maarten Grootendorst, 2024-09-11 AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)
  create your own large language model: Reimagining Intelligent Computer-Assisted Language Education Stevkovska, Marija, Klemenchich, Marijana, Kavakl? Uluta?, Nurdan, 2024-10-18 Reimagining language education through intelligent technologies and computer assistance marks a shift in how we approach language learning in the digital age. With advancements in artificial intelligence and machine learning, there is potential to transform traditional methods into personalized educational experience. Intelligent systems now offer adaptive learning pathways that cater to individual proficiency levels, learning styles, and progress rates, making language education more accessible and effective. These technologies beg further exploration to effectively provide real-time feedback and support, creating a more engaging and responsive educational experience. Reimagining Intelligent Computer-Assisted Language Education explores fundamental aspects of educational technology to improve language teaching and learning. It reimagines educational practice for language teaching and learning through the integration of educational technology for making the language teaching and learning process more efficient and engaging, while improving learner performance and progress. This book covers topics such as artificial intelligence, language education, and academic writing, and is a useful resource for education professionals, language learners, computer engineers, academicians, scientists, and researchers.
  create your own large language model: AI Unraveled - Master GPT-x, Gemini, Generative AI, LLMs, Prompt Engineering: A simplified Guide For Everyday Users Etienne Noumen, Dive into the revolutionary world of Artificial Intelligence with 'AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence'. This comprehensive guide is your portal to understanding AI's most intricate concepts and cutting-edge developments. Whether you're a curious beginner or an AI enthusiast, this book is tailored to unveil the complexities of AI in a simple, accessible manner. What's Inside: Fundamental AI Concepts: Journey through the basics of AI, machine learning, deep learning, and neural networks. AI in Action: Explore how AI is reshaping industries and society, diving into its applications in computer vision, natural language processing, and beyond. Ethical AI: Tackle critical issues like AI ethics and bias, understanding the moral implications of AI advancements. Industry Insights: Gain insights into how AI is revolutionizing industries and impacting our daily lives. The Future of AI: Forecast the exciting possibilities and challenges that lie ahead in the AI landscape. Special Focus on Generative AI & LLMs: Latest AI Trends: Stay updated with the latest in AI, including ChatGPT, Google Bard, GPT-4, Gemini, and more. Interactive Quizzes: Test your knowledge with engaging quizzes on Generative AI and Large Language Models (LLMs). Practical Guides: Master GPT-4 with a simplified guide, delve into advanced prompt engineering, and explore the nuances of temperature settings in AI. Real-World Applications: Learn how to leverage AI in various sectors, from healthcare to cybersecurity, and even explore its potential in areas like aging research and brain implants. For the AI Enthusiast: Prompt Engineering: Uncover secrets to crafting effective prompts for ChatGPT/Google Bard. AI Career Insights: Explore lucrative career paths in AI, including roles like AI Prompt Engineers. AI Investment Guide: Navigate the world of AI stocks and investment opportunities. Your Guide to Navigating AI: Do-It-Yourself Tutorials: From building custom ChatGPT applications to running LLMs locally, this book offers step-by-step guides. AI for Everyday Use: Learn how AI can assist in weight loss, social media, and more. 'AI Unraveled' is more than just a book; it's a resource for anyone looking to grasp the complexities of AI and its impact on our world. Get ready to embark on an enlightening journey into the realm of Artificial Intelligence! More Topics Covered: Artificial Intelligence, Machine Learning, Deep Learning, NLP, AI Ethics, Robotics, Cognitive Computing, ChatGPT, OpenAI, Google Bard, Generative AI, LLMs, AI in Healthcare, AI Investments, and much more. GPT-4 vs Gemini: Pros and Cons Mastering GPT-4: Simplified Guide For everyday Users Advance Prompt Engineering Techniques: [Single Prompt Technique, Zero-Shot and Few-Shot, Zero-Shot and Few-Shot, Generated Knowledge Prompting, EmotionPrompt, Chain of Density (CoD), Chain of Thought (CoT), Validation of LLMs Responses, Chain of Verification (CoVe), Agents - The Frontier of Prompt Engineering, Prompt Chaining vs Agents, Tree of Thought (ToT), ReAct (Reasoning + Act), ReWOO (Reasoning WithOut Observation), Reflexion and Self-Reflection, Guardrails, RAIL (Reliable AI Markup Language), Guardrails AI, NeMo Guardrails] Understanding Temperature in GPT-4: A Guide to AI Probability and Creativity Retrieval-Augmented Generation (RAG) model in the context of Large Language Models (LLMs) like GPT-4 Prompt Ideas for ChatGPT/Google Bard How to Run ChatGPT-like LLMs Locally on Your Computer in 3 Easy Steps ChatGPT Custom Instructions Settings for Power Users Examples of bad and good ChatGPT prompts Top 5 Beginner Mistakes in Prompt Engineering Use ChatGPT like a PRO Prompt template for learning any skill Prompt Engineering for ChatGPT The Future of LLMs in Search What is Explainable AI? Which industries are meant for XAI? ChatGPT Best Tips, Cheat Sheet LLMs Utilize Vector DB for Data Storage The Limitation Technique in Prompt Responses Use ChatGPT to learn new subjects Prompts to proofread anything Topics: Artificial Intelligence Education Machine Learning Deep Learning Reinforcement Learning Neural networks Data science AI ethics Deepmind Robotics Natural language processing Intelligent agents Cognitive computing AI Apps AI impact AI Tech ChatGPT Open AI Safe AI Generative AI Discriminative AI Sam Altman Google Bard NVDIA Large Language Models (LLMs) PALM GPT Explainable AI GPUs AI Stocks AI Podcast Q* AI Certification AI Quiz RAG How to access the AI Unraveled print and audiobook: Amazon print book: https://amzn.to/3xvCfWR Audible at Amazon : https://www.audible.com/pd/B0BXMJ7FK5/?source_code=AUDFPWS0223189MWT-BK-ACX0-343437&ref=acx_bty_BK_ACX0_343437_rh_us (Use Promo code: 37YT3B5UYUYZW) Audiobook at Google: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM Amazon eBook: https://amzn.to/3KbshkO Google eBook: https://play.google.com/store/books/details?id=oySuEAAAQBAJ Apple eBook: http://books.apple.com/us/book/id6445730691
  create your own large language model: Mastering Transformers Savaş Yıldırım, Meysam Asgari- Chenaghlu, 2024-06-03 Explore transformer-based language models from BERT to GPT, delving into NLP and computer vision tasks, while tackling challenges effectively Key Features Understand the complexity of deep learning architecture and transformers architecture Create solutions to industrial natural language processing (NLP) and computer vision (CV) problems Explore challenges in the preparation process, such as problem and language-specific dataset transformation Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTransformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You’ll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you’ll focus on using vision transformers to solve computer vision problems. Finally, you’ll discover how to harness the power of transformers to model time series data and for predicting. By the end of this transformers book, you’ll have an understanding of transformer models and how to use them to solve challenges in NLP and CV.What you will learn Focus on solving simple-to-complex NLP problems with Python Discover how to solve classification/regression problems with traditional NLP approaches Train a language model and explore how to fine-tune models to the downstream tasks Understand how to use transformers for generative AI and computer vision tasks Build transformer-based NLP apps with the Python transformers library Focus on language generation such as machine translation and conversational AI in any language Speed up transformer model inference to reduce latency Who this book is for This book is for deep learning researchers, hands-on practitioners, and ML/NLP researchers. Educators, as well as students who have a good command of programming subjects, knowledge in the field of machine learning and artificial intelligence, and who want to develop apps in the field of NLP as well as multimodal tasks will also benefit from this book’s hands-on approach. Knowledge of Python (or any programming language) and machine learning literature, as well as a basic understanding of computer science, are required.
  create your own large language model: Assessing Policy Effectiveness using AI and Language Models Chandrasekar Vuppalapati,
Free AI Image Generator - Bing Image Creator
Follow these steps to create a high-quality prompt: Be Specific: Include as many relevant details as possible. For example, instead of just "astronaut," provide context and visual cues.

Create - Minecraft Mods - CurseForge
Welcome to Create, a mod offering a variety of tools and blocks for Building, Decoration and Aesthetic Automation. The added elements of tech are designed to leave as many design …

CREATE Definition & Meaning - Merriam-Webster
The meaning of CREATE is to bring into existence. How to use create in a sentence.

Your Home for How-To - CreateTV
Create TV brings together the best is public television how-to and lifestyle programs for around-the-clock broadcast.

CREATE Definition & Meaning | Dictionary.com
Create definition: to cause to come into being, as something unique that would not naturally evolve or that is not made by ordinary processes.. See examples of CREATE used in a sentence.

CREATE | English meaning - Cambridge Dictionary
CREATE definition: 1. to make something new, or invent something: 2. to show that you are angry: 3. to make…. Learn more.

CREATE definition and meaning | Collins English Dictionary
The lights create such a glare it's next to impossible to see anything behind them. [ VERB noun ] Criticizing will only destroy a relationship and create feelings of failure.

Scratch - Imagine, Program, Share
Scratch is a free programming language and online community where you can create your own interactive stories, games, and animations.

Create - Definition, Meaning & Synonyms - Vocabulary.com
Jun 9, 2025 · To create simply means to make or bring into existence. Bakers create cakes, ants create problems at picnics, and you probably created a few imaginary friends when you were …

create verb - Definition, pictures, pronunciation and usage notes ...
create to make something exist or happen, especially something new that did not exist before: Scientists disagree about how the universe was created. make or create? Make is a more …

Large Language Models: the basics - Department of …
•Large Language Model (LLM) – GPT-3.5 •use case: multi-purpose & emergent ability 5. LM: Probability of Next Word •LMs can be used in many applications, e.g. Speech Recognition •n …

Large Knowledge Model: Perspectives and Challenges
reasoning. As highlighted later, even in the era of large models, there persists a positive correlation between the complexity level of data representations and the reasoning proficiency …

Language models and linguistic theories beyond words
Tvelopment of large language models is mainly a feat of engineering and so far has ... language in their own work. An article in our May 2023 issue proposes drawing inspiration

Introduction to Transformers: an NLP Perspective - arXiv.org
Transformers have dominated empirical machine learning models of natural language pro-cessing. In this paper, we introduce basic concepts of Transformers and present key tech …

1 Large Language Models and Games: A Survey and …
Pre-trained Transformer 2 (GPT-2) model was released [1]. GPT-2 demonstrated convincingly that transformer models trained on large text corpora could not only generate sur-prisingly high …

GPT4All: An Ecosystem of Open Source Compressed …
GPT4All model could be trained in about eight hours on a Lambda Labs DGX A100 8x 80GB for a total cost of ∼$100. 2.4 Model Evaluation We performed a preliminary evaluation of our model …

CSET - Controlling Large Language Model Outputs- A Primer
“Language model” is a general category describing a class of AI models that generate natural language text outputs. Large language models, or LLMs, are particularly powerful language …

Techniques to Make Large Language Models Smaller: An …
Techniques to Make Large Language Models Smaller: An Explainer By Kyle A. Miller and Andrew J. Lohn Large language models (LMs) are often difficult and expensive to train and use.1 For …

DB-GPT: Large Language Model Meets Database - Tsinghua …
Keywords Large language model · Database 1 Introduction Large language models (LLMs) are pre-trained with a super large model capacity (e.g., over 170 billion network param-eters in …

Large Language Model for Vulnerability Detection: Emerging …
Large Language Model for Vulnerability Detection: Emerging Results and Future Directions ICSE-NIER’24, April 14–20, 2024, Lisbon, Portugal Table 2: Results of ChatGPT with diverse …

AIGC In China: Current Developments And Future Outlook
Language Model, to Large-scale Pre-trained Model. We suggest that the foundations of AIGC technology lie in Large Language Models (LLM), pre-trained models, Multimodal Models, and …

Abstract arXiv:2309.07124v2 [cs.CL] 9 Oct 2023
Warning: This paper contains examples of potentially harmful language. Yuhui Li ♠, Fangyun Wei‡, Jinjing Zhao†, Chao Zhang , Hongyang Zhang♣∗ ♠Peking University, ‡Microsoft …

European Commission | Choose your language | Choisir une …
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Evolving Code with A Large Language Model - arXiv.org
Large language models (LLMs), along with other Foundational Models, have disrupted conventional expectations of Artificial Intelligence systems. An LLM, with a chatbot or Natural …

A Survey on ChatGPT: AI–Generated Contents, Challenges, …
(GPT), a transformer-based large language model (LLM) that can understand human languages and create human-like text (e.g., stories and articles) [9], as shown in Fig. 1. With recent …

Large Language Model Agent for Fake News Detection
tonomously design their own problem-solving plans [4], FactAgent enables LLMs to adhere to a structured workflow, emulating human ... Large Language Model Agent for Fake News …

When Large Language Models Meet Personalization: …
development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language …

Guidance for Effective and Responsible Use of AI in Research
these terms. For your own security and confidentiality of your data, it is more prudent to assume that whatever is fed into a query is owned by that company. Use of AI in Graduate Student …

MegaScale: Scaling Large Language Model Training to More …
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs Ziheng Jiang1,∗ Haibin Lin1,∗ Yinmin Zhong2,∗ Qi Huang1 Yangrui Chen1 Zhi Zhang1 Yanghua Peng1 Xiang …

CONCEPTUAL DESIGN GENERATION USING LARGE …
In this paper, we leverage a pre-trained generative model called GPT-3 to generate design solutions conditioned on inputs of design problems. GPT-3 is an autoregressive language …

TalkingAboutLargeLanguageModels - arXiv.org
work before we speak of them in language sug-gestive of human capabilities and patterns of be-haviour. To sharpen the issue, let’s compare two very short conversations, one between Alice …

Large Decision Models - IJCAI
3 Large Decision Models In natural language processing (NLP), large language models (LLMs) refer to Transformer-based models with a vast num-ber of parameters, e.g., BERT [Kenton and …

On Protecting the Data Privacy of Large Language Models …
Abstract—Large language models (LLMs) are complex artifi-cial intelligence systems capable of understanding, generating and translating human language. They learn language patterns by …

Online edition (c)2009 Cambridge UP - Stanford University
Apr 1, 2009 · ent terms, we have a language model. The notion of a language model is LANGUAGE MODEL inherently probabilistic. A language model is a function that puts a …

Understanding Large Language Models - Springer
designed to provide a crucial overall understanding of large language models. In this book, you will do the following: • Learn the history of AI and NLP leading up to large language models • …

Large Language Models and Intelligence Analysis
Large Language Models and Intelligence Analysis LLMs are deep neural networks that have been trained on very large corpora of text, sourced primarily from text-rich sites on the Internet such …

Large Language Models in Education: Vision and …
Large models refer to models with a massive number of parameters and computational capabilities [22]. LLMs are one type of large models, often involving billions of parameters. The …

DB-GPT: Large Language Model Meets Database - Tsinghua …
Keywords Large language model · Database 1 Introduction Large language models (LLMs) are pre-trained with a super large model capacity (e.g., over 170 billion network param-eters in …

Foundation Models for Decision Making: Problems, Methods, …
4.2 Vision and Language as Task Specifiers14 4.3 Learning Representations for Sequential Decision Making14 5 Large Language Models as Agents and Environments17 5.1 Interacting …

Abstract - arXiv.org
• We develop a cross-lingual neural codec language model VALL-E X with large multi-lingual multi-speaker multi-domain unclean speech data. VALL-E X is a conditional cross-lingual …

Create A Country 2 - Mr. Kersey
Create a country of your own. Use your imagination. The sky is the limit. Have fun. Want your country to be underground? Go for it. Want your people to speak only in monosyllabic grunts? …

Lecture 3: Language models - University of Illinois Urbana …
Building a probability model consists of two steps: 1. Defining the model 2. Estimating the model’s parameters (= training/learning ) Models (almost) always make independence …

Large Foundation Models for Power Systems - arXiv.org
Abstract—Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating …

Could a Large Language Model be Conscious? - arXiv.org
2. Evidence for consciousness in large language models? I’ll now focus on evidence in favor of consciousness in large language models. I’ll put my requests for evidence in a certain …

Large Language Model Programs - arXiv.org
Large Language Model Programs Figure 1. An illustration of our two-part LLM program example. The first part (left) of our program filters out irrelevant paragraphs from a large set. This is …

A Survey on Large Language Models for Code Generation
1 A Survey on Large Language Models for Code Generation JUYONG JIANG∗, The Hong Kong University of Science and Technology (Guangzhou), China FAN WANG∗, The Hong Kong …

arXiv:2406.04692v1 [cs.CL] 7 Jun 2024
Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. Ensure your response is well-structured, coherent, …

Phi-2: The surprising power of small language models - NeurIPS
The surprising Power of Small Language Models • Can these emergent abilities be achieved at a smaller scale? • Our line of work with the Phi models aims to answer this question • SLMs that …

AI AI Bias: Large Language Models Favor Their Own …
those generated by the large language models (LLMs). We additionally tested a range of recent open models as selectors only: Llama3 8B and 70B models, Mixtral 8x7B and 8x22B, and a …

Steps for Creating a Specialized Corpus and Developing an …
The first important step in creating a corpus is thinking about your teaching context, your students’ language needs, and how the corpus will be used. This will help determine what materials the …

arXiv:2310.10826v3 [cs.GT] 2 Jul 2024
Mechanism Design for Large Language Models* Paul Dütting† Vahab Mirrokni† Renato Paes Leme† Haifeng Xu‡ Song Zuo† Abstract Weinvestigate auction mechanisms for AI-generated …

Can ChatGPT Forecast Stock Price Movements? - arXiv.org
Apr 6, 2023 · Return Predictability and Large Language Models ∗ Alejandro Lopez-Lira and Yuehua Tang University of Florida First Version: April 6, 2023; This Version: September 13, …

CHAPTER N-gram Language Models - Stanford University
language model ducing language models or LMs. A language model is a machine learning model LM that predicts upcoming words. More formally, a language model assigns a prob-ability to …

“A good pun is its own reword”: Can Large Language
004 Although large language models (LLMs) have 005 been widely explored on various tasks of nat-006 ural language understanding and generation, 007 their ability to understand puns has …

Ontology engineering with Large Language Models - arXiv.org
matically translating natural language sentences into Description Logic. Since Large Language Models (LLMs) are the best tools for translations, we fine-tuned a GPT-3 model to convert …

Graphologue: Exploring Large Language Model Responses …
Graphologue: Exploring Large Language Model Responses with Interactive Diagrams UIST ’23, October 29-November 1, 2023, San Francisco, CA, USA translate their high-level design …

“A good pun is its own reword”: Can Large Language
“A good pun is its own reword”: Can Large Language Models Understand Puns? Anonymous ACL submission Abstract 001 As one of the common rhetorical devices, puns 002 play a vital role in …

Unveiling Security, Privacy, and Ethical Concerns of ChatGPT
large language model for natural language processing. GPT has exhibited exceptional performance across a wide range of complex language tasks, positioning it as a formidable …

Large Language Model (LLM) for Telecommunications: A …
that the inputs to the model are purely text, and the model generates purely text as outputs, even if the model can accept inputs in other modalities, such as GPT-4V and GPT-4o. When …

Regulating Large Language Models - Center for Democracy …
Feb 13, 2024 · 4 Participants disagreed on whether Large Language Models were indeed able to create derivative or “emergent” information. 5 Biderman, Stella, et al. “Emergent and …