Building Llm Powered Applications
DOWNLOAD
Download Building Llm Powered Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Building Llm Powered Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Building Llm Powered Applications
DOWNLOAD
Author : VALENTINA. ALTO
language : en
Publisher:
Release Date : 2024-05-22
Building Llm Powered Applications written by VALENTINA. ALTO and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Computers categories.
The book provides a solid theoretical foundation of what LLMs are, their architecture.
Building Llm Powered Applications
DOWNLOAD
Author : Valentina Alto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-22
Building Llm Powered Applications written by Valentina Alto and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Computers categories.
Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.
Llm Powered Application
DOWNLOAD
Author : Lou Jackson
language : en
Publisher: Independently Published
Release Date : 2024-06-23
Llm Powered Application written by Lou Jackson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-23 with Computers categories.
LLM-Powered Applications: Building the Future with Language Large Language Models (LLMs) are revolutionizing the way we interact with machines. These AI models, trained on massive amounts of text data, can understand and generate human-like language, opening doors to a new era of intelligent applications. Written by an expert in the field of AI and language processing, this book provides a balanced and informative view of LLMs. You'll gain a solid understanding of their capabilities, limitations, and the ethical considerations surrounding their development. This comprehensive guide dives deep into the world of LLM-powered applications. You'll explore how LLMs are transforming various industries, from software development and content creation to education and customer service. What's Inside: 1. Demystifying LLMs: Understand how these complex models work and their potential to revolutionize various fields. 2. Practical Applications: Discover inspiring ideas and real-world use cases for LLM technology across diverse industries. 3. Building with LLMs: Learn the essential tools, libraries, and techniques to develop your own LLM-powered applications 4. The Future Landscape: Explore the exciting possibilities and potential challenges that lie ahead for LLM development. This book is ideal for anyone interested in the future of technology and language. Whether you're a developer, entrepreneur, business leader, or simply curious about AI, this guide will equip you with the knowledge to harness the power of LLMs. Don't get left behind in the LLM revolution. This book empowers you to be at the forefront of this technological wave, shaping the future of how we interact with language and information. Become an LLM pioneer! Grab your copy of "LLM-Powered Applications" today and unlock the potential of this transformative technology!
Building Llm Applications With Python A Practical Guide
DOWNLOAD
Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Building Llm Applications With Python A Practical Guide written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This book equips you to harness the remarkable capabilities of Large Language Models (LLMs) using Python. Part I unveils the world of LLMs. You'll delve into their inner workings, explore different LLM types, and discover their exciting applications in various fields. Part II dives into the practical side of things. We'll guide you through setting up your Python environment and interacting with LLMs. Learn to craft effective prompts to get the most out of LLMs and understand the different response formats they can generate. Part III gets you building! We'll explore how to leverage LLMs for creative text generation, from poems and scripts to code snippets. Craft effective question-answering systems and build engaging chatbots – the possibilities are endless! Part IV empowers you to maintain and improve your LLM creations. We'll delve into debugging techniques to identify and resolve issues. Learn to track performance and implement optimizations to ensure your LLM applications run smoothly. This book doesn't shy away from the bigger picture. The final chapter explores the ethical considerations of LLMs, addressing bias and promoting responsible use of this powerful technology. By the end of this journey, you'll be equipped to unlock the potential of LLMs with Python and contribute to a future brimming with exciting possibilities.
Building Generative Ai Powered Apps
DOWNLOAD
Author : Aarushi Kansal
language : en
Publisher: Springer Nature
Release Date :
Building Generative Ai Powered Apps written by Aarushi Kansal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Demystifying Large Language Models
DOWNLOAD
Author : James Chen
language : en
Publisher: James Chen
Release Date : 2024-04-25
Demystifying Large Language Models written by James Chen and has been published by James Chen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-25 with Computers categories.
This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models. That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, exploring advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning, as well as RLHF (Reinforcement Learning with Human Feedback) for detoxifying LLMs to make them aligned with human values and ethical norms. Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life. Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models. By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals. Table of Contents 1. INTRODUCTION 1.1 What is AI, ML, DL, Generative AI and Large Language Model 1.2 Lifecycle of Large Language Models 1.3 Whom This Book Is For 1.4 How This Book Is Organized 1.5 Source Code and Resources 2. PYTORCH BASICS AND MATH FUNDAMENTALS 2.1 Tensor and Vector 2.2 Tensor and Matrix 2.3 Dot Product 2.4 Softmax 2.5 Cross Entropy 2.6 GPU Support 2.7 Linear Transformation 2.8 Embedding 2.9 Neural Network 2.10 Bigram and N-gram Models 2.11 Greedy, Random Sampling and Beam 2.12 Rank of Matrices 2.13 Singular Value Decomposition (SVD) 2.14 Conclusion 3. TRANSFORMER 3.1 Dataset and Tokenization 3.2 Embedding 3.3 Positional Encoding 3.4 Layer Normalization 3.5 Feed Forward 3.6 Scaled Dot-Product Attention 3.7 Mask 3.8 Multi-Head Attention 3.9 Encoder Layer and Encoder 3.10 Decoder Layer and Decoder 3.11 Transformer 3.12 Training 3.13 Inference 3.14 Conclusion 4. PRE-TRAINING 4.1 Machine Translation 4.2 Dataset and Tokenization 4.3 Load Data in Batch 4.4 Pre-Training nn.Transformer Model 4.5 Inference 4.6 Popular Large Language Models 4.7 Computational Resources 4.8 Prompt Engineering and In-context Learning (ICL) 4.9 Prompt Engineering on FLAN-T5 4.10 Pipelines 4.11 Conclusion 5. FINE-TUNING 5.1 Fine-Tuning 5.2 Parameter Efficient Fine-tuning (PEFT) 5.3 Low-Rank Adaptation (LoRA) 5.4 Adapter 5.5 Prompt Tuning 5.6 Evaluation 5.7 Reinforcement Learning 5.8 Reinforcement Learning Human Feedback (RLHF) 5.9 Implementation of RLHF 5.10 Conclusion 6. DEPLOYMENT OF LLMS 6.1 Challenges and Considerations 6.2 Pre-Deployment Optimization 6.3 Security and Privacy 6.4 Deployment Architectures 6.5 Scalability and Load Balancing 6.6 Compliance and Ethics Review 6.7 Model Versioning and Updates 6.8 LLM-Powered Applications 6.9 Vector Database 6.10 LangChain 6.11 Chatbot, Example of LLM-Powered Application 6.12 WebUI, Example of LLM-Power Application 6.13 Future Trends and Challenges 6.14 Conclusion REFERENCES ABOUT THE AUTHOR
Building Large Language Model Llm Applications
DOWNLOAD
Author : Anand Vemula
language : en
Publisher: Independently Published
Release Date : 2024-05-24
Building Large Language Model Llm Applications written by Anand Vemula and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-24 with Computers categories.
"Building LLM Apps" is a comprehensive guide that equips readers with the knowledge and practical skills needed to develop applications utilizing large language models (LLMs). The book covers various aspects of LLM application development, starting from understanding the fundamentals of LLMs to deploying scalable and efficient solutions. Beginning with an introduction to LLMs and their importance in modern applications, the book explores the history, key concepts, and popular architectures like GPT and BERT. Readers learn how to set up their development environment, including hardware and software requirements, installing necessary tools and libraries, and leveraging cloud services for efficient development and deployment. Data preparation is essential for training LLMs, and the book provides insights into gathering and cleaning data, annotating and labeling data, and handling imbalanced data to ensure high-quality training datasets. Training large language models involves understanding training basics, best practices, distributed training techniques, and fine-tuning pre-trained models for specific tasks. Developing LLM applications requires designing user interfaces, integrating LLMs into existing systems, and building interactive features such as chatbots, text generation, sentiment analysis, named entity recognition, and machine translation. Advanced LLM techniques like prompt engineering, transfer learning, multi-task learning, and zero-shot learning are explored to enhance model capabilities. Deployment and scalability strategies are discussed to ensure smooth deployment of LLM applications while managing costs effectively. Security and ethics in LLM apps are addressed, covering bias detection, fairness, privacy, security, and ethical considerations to build responsible AI solutions. Real-world case studies illustrate the practical applications of LLMs in various domains, including customer service, healthcare, and finance. Troubleshooting and optimization techniques help readers address common issues and optimize model performance. Looking towards the future, the book highlights emerging trends and developments in LLM technology, emphasizing the importance of staying updated with advancements and adhering to ethical AI practices. "Building LLM Apps" serves as a comprehensive resource for developers, data scientists, and business professionals seeking to harness the power of large language models in their applications.
Agentscope A Guide To Building Multi Agent Llm Applications
DOWNLOAD
Author : StoryBuddiesPlay
language : en
Publisher: StoryBuddiesPlay
Release Date : 2024-05-14
Agentscope A Guide To Building Multi Agent Llm Applications written by StoryBuddiesPlay and has been published by StoryBuddiesPlay this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Computers categories.
Unleash the power of collaboration with AgentScope, a comprehensive platform designed to streamline the development of multi-agent Large Language Model (LLM) applications. This in-depth guide equips you with everything you need to know to leverage AgentScope's functionalities and build intelligent, scalable AI systems. Embrace the Future of AI: Multi-Agent Collaboration Made Easy AgentScope empowers you to construct a team of specialized LLMs, each with its own strengths and expertise. Imagine a system where one agent analyzes customer reviews for sentiment, another identifies key themes, and a third generates a comprehensive report – all working together seamlessly. This is the power of multi-agent LLMs, and AgentScope simplifies the process of bringing it to life. Dive Deep into AgentScope: From Agent Definition to Orchestrated Workflows This comprehensive guide takes you on a journey through the functionalities of AgentScope. Learn how to define and configure your agents, specifying their roles, LLM models, and communication protocols. Explore how to orchestrate tasks, ensuring a smooth workflow where subtasks are completed in the correct order and dependencies are managed effectively. Conquer Challenges: Error Handling, Security, and Explainability The guide doesn't shy away from the real-world considerations of multi-agent systems. Address potential errors and exceptions with AgentScope's robust error handling mechanisms. Safeguard your LLM application with built-in security features like authentication and data encryption. Foster trust and transparency by incorporating Explainable AI (XAI) techniques to understand the decision-making processes within your multi-agent system. Scale to New Heights: Optimizing Performance for Large Tasks As your LLM application tackles more complex tasks and works with ever-growing datasets, AgentScope provides the tools you need to maintain optimal performance. Discover strategies for resource allocation, communication optimization, and utilizing scalable LLM architectures. Employ monitoring and analytics to identify bottlenecks and ensure your multi-agent system continues to function efficiently. A Glimpse into the Future: Pioneering Applications with AgentScope Look ahead and explore the exciting potential of multi-agent LLM systems. Imagine AI-powered scientific discovery, personalized education, intelligent content creation, and advanced conversational AI for businesses – these are just a few possibilities on the horizon. AgentScope equips you to be a part of this revolution, empowering you to build groundbreaking applications that leverage the power of collaborative intelligence. Start Building Today: Unleash the Potential of Multi-Agent LLMs with AgentScope This guide provides a roadmap for your journey into the world of multi-agent LLM development with AgentScope. With its user-friendly interface, comprehensive documentation, and expansive capabilities, AgentScope makes complex AI development accessible. So, what are you waiting for? Start building the future of AI today!
Developing Apps With Gpt 4 And Chatgpt
DOWNLOAD
Author : Olivier Caelen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-07-10
Developing Apps With Gpt 4 And Chatgpt written by Olivier Caelen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-10 with Computers categories.
This book provides an ideal guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete cover the main features and benefits of GPT-4 and GPT-3.5 models and explain how they work. You'll also get a step-by-step guide for developing applications using the OpenAI Python library, including text generation, Q&A, and smart assistants. Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must. You'll learn: Fundamentals and benefits of GPT-4 and GPT-3.5 models, including the main features and how they work How to integrate these models into Python-based applications, leveraging natural language processing capabilities and overcoming specific LLM-related challenges Examples of applications demonstrating the OpenAI API in Python for tasks including text generation, question answering, content summarization, classification, and more Advanced LLM topics such as prompt engineering, fine-tuning models for specific tasks, RAG, plug-ins, LangChain, LlamaIndex, GPTs, and assistants Olivier Caelen is a machine learning researcher at Worldline and teaches machine learning courses at the University of Brussels. Marie-Alice Blete, a software architect and data engineer in Worldline's R&D department, is interested in performance and latency issues associated with AI solutions.
Python Machine Learning By Example
DOWNLOAD
Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-31
Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Computers categories.
Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas Key Features Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions Implement ML models, such as neural networks and linear and logistic regression, from scratch Purchase of the print or Kindle book includes a free PDF copy Book DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learn Follow machine learning best practices throughout data preparation and model development Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning Develop and fine-tune neural networks using TensorFlow and PyTorch Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP Build classifiers using support vector machines (SVMs) and boost performance with PCA Avoid overfitting using regularization, feature selection, and more Who this book is for This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.