Optimizing Llm Applications
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Optimizing Llm Applications
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Author : George Anvil
language : en
Publisher: Independently Published
Release Date : 2023-09-06
Optimizing Llm Applications written by George Anvil and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-06 with categories.
Master the Secrets to Optimizing LLM Applications for Speed and Efficiency LLMs are the brains of AI-powered products. They can understand language, power chatbots, and translate languages. But in the real world, they can be slow and inefficient. This book shows you how to make LLMs perform better. Learn how to make LLMs perform with lightning speed and unbeatable efficiency Discover the latest techniques and strategies for parallelization, memory management, hardware acceleration, and more Gain hands-on experience with real-world use cases, from chatbots to language translation Who is this book for? Developers and data scientists who want to optimize LLM applications Anyone who wants to learn how to make LLMs perform faster and more efficiently What's inside? An introduction to the basics of LLMs A comprehensive overview of the latest optimization techniques Real-world use cases of LLM optimization Hands-on exercises to help you learn by doing Why should you buy this book? This is the most comprehensive guide to optimizing LLM applications available. It covers everything you need to know to get started with LLM optimization, from the basics to advanced topics. The book is packed with practical examples and exercises that will help you learn by doing. The author is an expert in the field of LLM optimization and has a proven track record of teaching others. Order your copy today and start optimizing your LLM applications
Mastering Llms And Gpus A Hands On Guide To Programming Optimization And Deployment
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Mastering Llms And Gpus A Hands On Guide To Programming Optimization And Deployment 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.
Tired of slow AI? Want to build groundbreaking applications powered by language? This book is your key! Mastering LLMs and GPUs: A Hands-on Guide to Programming, Optimization, and Deployment equips you with the practical skills to leverage the revolutionary power of Large Language Models (LLMs) and Graphics Processing Units (GPUs). Inside, you'll discover: The Fundamentals: Demystify LLMs, grasp their architectures, and understand how they leverage massive data to generate human-quality text, translate languages, and answer your questions in an informative way. GPU Powerhouse: Unlock the secrets of GPUs, the processing engines that accelerate LLM training compared to traditional CPUs. Learn how to harness their parallel processing capabilities for lightning-fast results. Become an LLM Programming Pro: Code Like a Master: Dive into the world of LLM programming with essential tools and libraries like CUDA or OpenCL. Write code that effectively unleashes the parallel processing power of GPUs. Optimize for Peak Performance: Master memory management strategies to ensure data is readily available for faster processing. Explore techniques for fine-tuning pre-trained LLMs, specializing them for specific tasks and maximizing their effectiveness. Deploy Your LLM Creations: Real-World Applications: Learn to integrate your trained and optimized LLM into applications or cloud platforms, making it accessible for real-world use cases. Practical Considerations: Gain insights into resource management and performance monitoring techniques to keep your LLM running smoothly. Mastering LLMs and GPUs is your comprehensive guide to building powerful language models. With hands-on exercises, clear explanations, and practical advice, you'll be well on your way to developing groundbreaking AI applications that transform the way we interact with language.
Building Llm Applications With Python A Practical Guide
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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 Large Language Model Llm Applications
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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
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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!
Building Llm Powered Applications
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Author : Valentina Alto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-22
Building Llm Powered Applications written by Valentina Alto and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Computers categories.
Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications 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.
Developing Apps With Gpt 4 And Chatgpt
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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.
Mastering Large Language Models With Python
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Author : Raj Arun R
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-04-12
Mastering Large Language Models With Python written by Raj Arun R and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-12 with Computers categories.
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
Large Language Model Based Solutions
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Author : Shreyas Subramanian
language : en
Publisher: John Wiley & Sons
Release Date : 2024-04-02
Large Language Model Based Solutions written by Shreyas Subramanian and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-02 with Computers categories.
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.
Applications Of Intelligent Optimization In Biology And Medicine
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Author : Aboul-Ella Hassanien
language : en
Publisher: Springer
Release Date : 2015-07-18
Applications Of Intelligent Optimization In Biology And Medicine written by Aboul-Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-18 with Technology & Engineering categories.
This volume provides updated, in-depth material on the application of intelligent optimization in biology and medicine. The aim of the book is to present solutions to the challenges and problems facing biology and medicine applications. This Volume comprises of 13 chapters, including an overview chapter, providing an up-to-date and state-of-the research on the application of intelligent optimization for bioinformatics applications, DNA based Steganography, a modified Particle Swarm Optimization Algorithm for Solving Capacitated Maximal Covering Location Problem in Healthcare Systems, Optimization Methods for Medical Image Super Resolution Reconstruction and breast cancer classification. Moreover, some chapters that describe several bio-inspired approaches in MEDLINE Text Mining, DNA-Binding Proteins and Classes, Optimized Tumor Breast Cancer Classification using Combining Random Subspace and Static Classifiers Selection Paradigms, and Dental Image Registration. The book will be a useful compendium for a broad range of readers—from students of undergraduate to postgraduate levels and also for researchers, professionals, etc.—who wish to enrich their knowledge on Intelligent Optimization in Biology and Medicine and applications with one single book.