| cohere large language model: Large Language Models Oswald Campesato, 2024-10-02 This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher. |
| cohere 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 |
| cohere large language model: Introduction to Python and Large Language Models Dilyan Grigorov, |
| cohere 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.) |
| cohere large language model: Quick Start Guide to Large Language Models Sinan Ozdemir, 2024-09-26 The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field. --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| cohere large language model: Translation and Neoliberalism Ali Jalalian Daghigh, |
| cohere large language model: FastAPI Cookbook Giunio De Luca, 2024-08-02 Enhance your web development skills, from setting up your environment and advanced integrations to building robust, high-traffic web applications with practical, scalable solutions Key Features Explore FastAPI in depth, from basic setup to advanced features such as custom middleware and WebSockets Discover practical strategies to optimize app performance and handle high traffic Implement SQL and NoSQL integration techniques for versatile data management in FastAPI applications Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionFastAPI is a cutting-edge Python framework that is revolutionizing the way web apps and APIs are built. Known for its speed, simplicity, and scalability, FastAPI empowers developers to create high-performing applications with ease. This book will help you leverage FastAPI’s immense potential to handle high-traffic scenarios and integrate seamlessly with modern Python tools. The book begins by familiarizing you with the basics of setting up and configuring your FastAPI environment before moving to the intricacies of building RESTful APIs, managing data with SQL and NoSQL databases, and handling authentication and authorization. Next, you'll focus on advanced topics such as custom middleware, WebSocket communication, and integration with various Python libraries. Each chapter is meticulously crafted with practical recipes, progressing from foundational concepts to advanced features and best practices. The concluding chapters show you how to optimize performance, implement rate limiting, and execute background tasks, empowering you to become a proficient FastAPI developer. By the end of this book, you'll have gained the skills you need to migrate existing apps to FastAPI, and be equipped to tackle any challenge in the modern web development landscape, ensuring your apps are not only functional, but also efficient, secure, and scalable.What you will learn Explore advanced FastAPI functionalities such as dependency injection, custom middleware, and WebSockets Discover various types of data storage for powerful app functionality with SQL and NoSQL Implement testing and debugging practices for clean, robust code Integrate authentication and authorization mechanisms to secure web apps Acquire skills to seamlessly migrate existing applications to FastAPI Write unit and integration tests, ensuring reliability and security for your apps Deploy your FastAPI apps to production environments for real-world use Who this book is for This book is for Python developers looking to enhance their skills to build scalable, high-performance web apps using FastAPI. Professionals seeking practical guidance to create APIs and web apps that can handle significant traffic and scale as needed will also find this book helpful by learning from both foundational insights and advanced techniques. The book is also designed for anyone familiar with RESTful APIs, HTTP protocols, and database systems, as well as developers looking to migrate existing applications to FastAPI or explore its advanced features. |
| cohere large language model: CSS3 and SVG With Perplexity Oswald Campesato, 2024-10-10 This book provides an introduction to generative AI and how to use Perplexity to generate graphics code using various combinations of HTML, CSS3, and SVG. It covers various aspects of modern web development and AI technologies, with a particular emphasis on Generative AI, CSS3, SVG, JavaScript, HTML, and popular web features like 3D animations and gradients. By exploring these topics, readers will gain a deeper understanding of how AI can enhance web development processes and how to leverage AI models like Perplexity to streamline development workflows. Web developers, UI/UX designers, and software engineers seeking to blend traditional web development skills with the latest AI technologies will find this book to be a valuable resource. FEATURES: Covers generative AI fundamentals to advanced CSS3 and SVG techniques, offering comprehensive material on modern web development technologies Features both manually created and AI generated code samples, security issues, crafting prompts, and accessibility needs Balances theoretical knowledge and practical examples, so readers gain hands-on experience in implementing AI-driven design solutions using Perplexity-generated code Includes companion files with code, datasets, and images from the book -- available from the publisher for downloading (with proof of purchase) |
| cohere large language model: CSS3 and SVG with Gemini Oswald Campesato, 2024-07-14 This book is designed to equip you with the knowledge and skills necessary to navigate the intersection of web development and artificial intelligence (AI). It covers various aspects of modern web development and AI technologies, with a particular emphasis on Generative AI, CSS3, SVG, JavaScript, HTML, and popular web features like 3D animations and gradients. By exploring these topics, readers will gain a deeper understanding of how AI can enhance web development processes and how to leverage AI models like Google Gemini to streamline development workflows. Web developers, UI/UX designers, and software engineers seeking to blend traditional web development skills with the latest AI technologies will find this book to be a valuable resource. |
| cohere large language model: Building Data-Driven Applications with LlamaIndex Andrei Gheorghiu, 2024-05-10 Solve real-world problems easily with artificial intelligence (AI) using the LlamaIndex data framework to enhance your LLM-based Python applications Key Features Examine text chunking effects on RAG workflows and understand security in RAG app development Discover chatbots and agents and learn how to build complex conversation engines Build as you learn by applying the knowledge you gain to a hands-on project Book DescriptionDiscover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.What you will learn Understand the LlamaIndex ecosystem and common use cases Master techniques to ingest and parse data from various sources into LlamaIndex Discover how to create optimized indexes tailored to your use cases Understand how to query LlamaIndex effectively and interpret responses Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit Customize a LlamaIndex configuration based on your project needs Predict costs and deal with potential privacy issues Deploy LlamaIndex applications that others can use Who this book is for This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework. |
| cohere 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 |
| cohere large language model: The Predictive Edge Alejandro Lopez-Lira, 2024-07-11 Use ChatGPT to improve your analysis of stock markets and securities In The Predictive Edge: Outsmart the Market Using Generative AI and ChatGPT in Financial Forecasting, renowned AI and finance researcher Dr. Alejandro Lopez-Lira delivers an engaging and insightful new take on how to use large language models (LLMs) like ChatGPT to find new investment opportunities and make better trading decisions. In the book, you’ll learn how to interpret the outputs of LLMs to craft sounder trading strategies and incorporate market sentiment into your analyses of individual securities. In addition to a complete and accessible explanation of how ChatGPT and other LLMs work, you’ll find: Discussions of future trends in artificial intelligence and finance Strategies for implementing new and soon-to-come AI tools into your investing strategies and processes Techniques for analyzing market sentiment using ChatGPT and other AI tools A can’t-miss playbook for taking advantage of the full potential of the latest AI advancements, The Predictive Edge is a fully to-date and exciting exploration of the intersection of tech and finance. It will earn a place on the bookshelves of individual and professional investors everywhere. |
| cohere large language model: Python 3 and Machine Learning Using ChatGPT / GPT-4 Oswald Campesato, 2024-05-22 This book is designed to bridge the gap between theoretical knowledge and practical application in the fields of Python programming, machine learning, and the innovative use of ChatGPT-4 in data science. The book is structured to facilitate a deep understanding of several core topics. It begins with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. Next, it explores a variety of machine learning classifiers from kNN to SVMs. In later chapters, it discusses the capabilities of GPT-4, and how its application enhances traditional linear regression analysis. Finally, the book covers the innovative use of ChatGPT in data visualization. This segment focuses on how AI can transform data into compelling visual stories, making complex results accessible and understandable. It includes material on AI apps, GANs, and DALL-E. Companion files are available for downloading with code and figures from the text. FEATURES: Includes practical tutorials designed to provide hands-on experience, reinforcing learning through practice Provides coverage of the latest Python tools using state-of-the-art libraries essential for modern data scientists Companion files with source code, datasets, and figures are available for downloading |
| cohere large language model: Python 3 Data Visualization Using Google Gemini Oswald Campesato, 2024-03-30 This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhance their visualization skills, it explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by Gemini-generated code samples. Companion files with source code and figures are available for downloading. FEATURES: Covers Python-based data visualization libraries and techniques Includes practical examples and Gemini-generated code samples for efficient learning Integrates Google Gemini for advanced data visualization capabilities Sets up a conducive development environment for a seamless coding experience Includes companion files for downloading with source code and figures |
| cohere large language model: Generative AI in Action Amit Bahree, 2024-10-29 Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you’ve been searching for. It introduces both AI’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: • A practical overview of of generative AI applications • Architectural patterns, integration guidance, and best practices for generative AI • The latest techniques like RAG, prompt engineering, and multi-modality • The challenges and risks of generative AI like hallucinations and jailbreaks • How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside • Best practices for deploying Generative AI apps • Production-quality RAG • Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book’s GitHub repository B Responsible AI tools |
| cohere large language model: Transformers for Natural Language Processing and Computer Vision Denis Rothman, 2024-02-29 The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AI Key Features Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Purchase of the print or Kindle book includes a free eBook in PDF format Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.What you will learn Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM 2 models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V Who this book is for This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field. Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution. |
| cohere large language model: Large Language Models Uday Kamath, Kevin Keenan, Garrett Somers, Sarah Sorenson, 2024 Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. |
| cohere large language model: Python 3 Using ChatGPT / GPT-4 Oswald Campesato, 2023-12-12 This book is intended primarily for people who want to learn both Python 3 and how to use ChatGPT with Python. Chapter One begins with an introduction to fundamental aspects of Python programming, including various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. Later, the book covers loops, conditional logic, and reserved words in Python. You will also see how to handle user input, manage exceptions, and work with command-line arguments. Next, the text transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of ChatGPT, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin. Code samples and figures from the book are available for downloading. In essence, the book provides a modest bridge between the worlds of Python programming and AI, aiming to equip readers with the knowledge and skills to navigate both domains confidently. FEATURES Includes a chapter on how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin Covers basic concepts of Python 3 such as loops, conditional logic, reserved words, user input, manage exceptions, work with command-line arguments, and more Includes companion files for downloading with source code and figures |
| cohere large language model: AI Applications and Strategies in Teacher Education Keeley, Krista LaRue, 2024-10-10 Artificial intelligence is revolutionizing teacher education by offering innovative applications and strategies to enhance the learning experience for educators and students. From personalized learning platforms to intelligent tutoring systems, AI can transform traditional teaching methods. These intelligent technologies streamline administrative tasks while supporting the development of essential skills like critical thinking and faculty collaboration. As teacher education programs continue to integrate AI tools, future educators learn to harness data-driven insights and create engaging, effective learning environments. Exploring these applications further emphasizes the potential of AI to positively reshape the teacher education sphere. AI Applications and Strategies in Teacher Education explores the landscape of AI in training and supporting educators. The book serves educators seeking insights into effective utilization of AI in a professional setting and the integration of AI in teaching practices. This book covers topics such as educational technologies, higher education, and diversity and equity, and is a useful resource for academicians, teachers, professors, education professionals, data scientists, computer engineers, and researchers. |
| cohere 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. |
| cohere large language model: GPT-4 For Developers Oswald Campesato, 2024-01-30 This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading. FEATURES Offers an all-encompassing view of ChatGPT and GPT-4, from basics to advanced topics, including functionalities, capabilities, and limitations Contains Python 3.x code samples demonstrating the application of GPT-4 in real-world scenarios Provides a forward-looking perspective on Generative AI and its integration with data visualization and DALL-E Includes companion files with source code, data sets, and figures |
| cohere 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.) |
| cohere large language model: CSS3 and SVG with GPT-4 Oswald CAMPESATO, 2024-07-14 This book is designed to equip you with the knowledge and skills necessary to navigate the intersection of web development and artificial intelligence (AI). It covers various aspects of modern web development and AI technologies, with a particular emphasis on Generative AI, CSS3, SVG, JavaScript, HTML, and popular web features like 3D animations and gradients. By exploring these topics, readers will gain a deeper understanding of how AI can enhance web development processes and how to leverage AI models like GPT-4 to streamline development workflows. Web developers, UI/UX designers, and software engineers seeking to blend traditional web development skills with the latest AI technologies will find this book to be a valuable resource. |
| cohere large language model: Generative Intelligence and Intelligent Tutoring Systems Angelo Sifaleras, |
| cohere 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. |
| cohere large language model: Generative AI with LangChain Ben Auffarth, 2023-12-22 2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily. |
| cohere large language model: Digital Transformation in Higher Education, Part A Miltiadis D. Lytras, Andreea Claudia Serban, Afnan Alkhaldi, Sawsan Malik, Tahani Aldosemani, 2024-10-28 Digital Transformation in Higher Education is a pivotal reference through the transformative power of emerging technologies in academia. Addressing the dual nature of technology as both a challenge and an opportunity, this book presents a rich overview of strategies for integrating digital technology-driven advancements. |
| cohere large language model: CSS3 and SVG with Claude 3 Oswald Campesato, 2024-10-07 This book explores the synergy between Claude3, a cutting-edge AI model, and Web technologies such as HTML, CSS3, and SVG, providing both novices and experienced developers with the tools to create visually stunning Web graphics and animations. It introduces the fundamental concepts of Generative AI, the key players in the field, and the distinctive features of Claude3, while also mastering the art of prompt engineering to interact effectively with AI models. The book covers creating dynamic 3D animations with CSS3, including effects like glowing, image fading, and rotating, and demonstrates how to leverage Claude3 to generate sophisticated SVG graphics and animations, thereby enhancing Web designs with advanced visual effects. FEATURES Covers generative AI fundamentals to advanced CSS3 and SVG techniques, offering comprehensive material on modern web development technologies Features both manually created and AI generated code samples, security issues, crafting prompts, and accessibility needs Balances theoretical knowledge and practical examples, so readers gain hands-on experience in implementing AI-driven design solutions using Claude-generated code Includes companion files with code, datasets, and images from the book -- available from the publisher for downloading (with proof of purchase) TABLE OF CONTENTS 1: The Generative AI Landscape. 2: Prompt Engineering. 3: Introduction to CSS3. 4: CSS3 3D Animation. 5: CSS3 and Claude 3. 6: Introduction to SVG. 7: SVG and Claude 3. Index. |
| cohere large language model: Praxiseinstieg Large Language Models Sinan Ozdemir, 2024-05-14 Der Schnellstart in die praktische Arbeit mit LLMs Das Buch bietet einen Überblick über zentrale Konzepte und Techniken von LLMs wie z.B. ChatGPT und zeigt das Potenzial von Open-Source- und Closed-Source-Modellen Es erläutert, wie Large Language Models funktionieren und wie sie für Aufgaben des Natural Language Processing (NLP) genutzt werden Auch für interessierte Nicht-Data-Scientists mit Python-Kenntnissen verständlich Themen z.B.: die ChatGPT-API, Prompt-Engineering, Chatbot-Personas, Cloud-Bereitstellung; deckt auch GPT-4 ab Large Language Models (LLMs) wie ChatGPT sind enorm leistungsfähig, aber auch sehr komplex. Praktikerinnen und Praktiker stehen daher vor vielfältigen Herausforderungen, wenn sie LLMs in ihre eigenen Anwendungen integrieren wollen. In dieser Einführung räumt Data Scientist und KI-Unternehmer Sinan Ozdemir diese Hürden aus dem Weg und bietet einen Leitfaden für den Einsatz von LLMs zur Lösung praktischer Probleme des Natural Language Processings. Sinan Ozdemir hat alles zusammengestellt, was Sie für den Einstieg benötigen: Schritt-für-Schritt-Anleitungen, Best Practices, Fallstudien aus der Praxis, Übungen und vieles mehr. Er stellt die Funktionsweise von LLMs vor und unterstützt Sie so dabei, das für Ihre Anwendung passende Modell und geeignete Datenformate und Parameter auszuwählen. Dabei zeigt er das Potenzial sowohl von Closed-Source- als auch von Open-Source-LLMs wie GPT-3, GPT-4 und ChatGPT, BERT und T5, GPT-J und GPT-Neo, Cohere sowie BART. Lernen Sie die Schlüsselkonzepte kennen: Transfer Learning, Feintuning, Attention, Embeddings, Tokenisierung und mehr Nutzen Sie APIs und Python, um LLMs an Ihre Anforderungen anzupassen Beherrschen Sie Prompt-Engineering-Techniken wie Ausgabe-Strukturierung, Gedankenketten und Few-Shot-Prompting Passen Sie LLM-Embeddings an, um eine Empfehlungsengine mit eigenen Benutzerdaten neu zu erstellen Konstruieren Sie multimodale Transformer-Architekturen mithilfe von Open-Source-LLMs Optimieren Sie LLMs mit Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) Deployen Sie Prompts und benutzerdefinierte, feingetunte LLMs in die Cloud |
| cohere large language model: Artificial Intelligence in Science Challenges, Opportunities and the Future of Research OECD, 2023-06-26 The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. |
| cohere large language model: The Generative AI Practitioner’s Guide Arup Das, David Sweenor, 2024-07-20 Generative AI is revolutionizing the way organizations leverage technology to gain a competitive edge. However, as more companies experiment with and adopt AI systems, it becomes challenging for data and analytics professionals, AI practitioners, executives, technologists, and business leaders to look beyond the buzz and focus on the essential questions: Where should we begin? How do we initiate the process? What potential pitfalls should we be aware of? This TinyTechGuide offers valuable insights and practical recommendations on constructing a business case, calculating ROI, exploring real-life applications, and considering ethical implications. Crucially, it introduces five LLM patterns—author, retriever, extractor, agent, and experimental—to effectively implement GenAI systems within an organization. The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications bridges critical knowledge gaps for business leaders and practitioners, equipping them with a comprehensive toolkit to define a business case and successfully deploy GenAI. In today’s rapidly evolving world, staying ahead of the competition requires a deep understanding of these five implementation patterns and the potential benefits and risks associated with GenAI. Designed for business leaders, tech experts, and IT teams, this book provides real-life examples and actionable insights into GenAI’s transformative impact on various industries. Empower your organization with a competitive edge in today’s marketplace using The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications. Remember, it’s not the tech that’s tiny, just the book!™ |
| cohere large language model: Artificial intelligence in science Alistair Nolan, 2024-01-05 The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions. This publication is aimed at a broad readership, including policy makers, the public, and stakeholders in all areas of science. It is written in non-technical language and gathers the perspectives of prominent researchers and practitioners. The book examines various topics, including the current, emerging, and potential future uses of AI in science, where progress is needed to better serve scientific advancements, and changes in scientific productivity. Additionally, it explores measures to expedite the integration of AI into research in developing countries. A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance. ABOUT THE AUTHOR Alistair Nolan is a Senior Policy Analyst in the OECD’s Directorate for Science, Technology and Innovation. Prior to the OECD, Mr. Nolan led a range of industry-related analytic and technical assistance projects with the United Nations. Over a number of years at the OECD Alistair has been involved in work on skills and education assessment, entrepreneurship, private sector development and policy evaluation. Alistair is currently coordinating various streams of OECD work on artificial intelligence, and is overseeing the work on AI diffusion under the AI-WIPS project. Mr. Nolan oversaw preparation of the 2017 publication The Next Production Revolution: Implications for Governments and Business, which examines a variety of emerging technologies, their impacts and policy implications, and which was referenced at the start of the 2017 G7 Taormina Action Plan. Mr. Nolan led work on 2020 publication The Digitalisation of Science, Technology and Innovation : Key Developments and Policies, which among other topics addresses the role of AI in advanced production. |
| cohere large language model: Systems, Software and Services Process Improvement Murat Yilmaz, |
| cohere large language model: Transforming Conversational AI Michael McTear, |
| cohere large language model: The Developer's Playbook for Large Language Model Security Steve Wilson, 2024-09-03 Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization |
| cohere large language model: Beyond Quantity Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter, Alexander Waibel, 2023-11-30 How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately? |
| cohere large language model: Formal Methods for the Analysis of Biomedical Ontologies Guo-Qiang Zhang, Rashmie Abeysinghe, Licong Cui, 2022-11-08 The book synthesizes research on the analysis of biomedical ontologies using formal concept analysis, including through auditing, curation, and enhancement. As the evolution of biomedical ontologies almost inevitably involves manual work, formal methods are a particularly useful tool for ontological engineering and practice, particularly in uncovering unexpected bugs and content materials. The book first introduces simple but formalized strategies for discovering undesired and incoherent patterns in ontologies before exploring the application of formal concept analysis for semantic completeness. The book then turns to formal concept analysis, a classical approach used in the mathematical treatment of orders and lattices, as an ontological engineering principle, focusing on the structural property of ontologies with respect to its conformation to lattice or not (non-lattice). The book helpfully covers the development of more efficient algorithms for non-lattice detection and extraction required by exhaustive lattice/non-lattice analysis. The book goes on to highlight the power and utility of uncovering non-lattice structure for debugging ontologies and describes methods that leverage the linguistic information in concept names (labels) for ontological analysis. It also addresses visualization and performance evaluation issues before closing with an overview and forward-looking perspectives on the field. This book is intended for graduate students and researchers interested in biomedical ontologies and their applications. It can be a useful supplement for courses on knowledge representation and engineering and also provide readers with a reference for related scientific publications and literature to assist in identifying potential research topics. All mathematical concepts and notations used in this book can be found in standard discrete mathematics textbooks, and the appendix at the end of the book provides a list of key ontological resources, as well as annotated non-lattice and lattice examples that were discovered using the authors' methods, demonstrating how bugs are fixed by converting non-lattices to lattices with minimal edit changes. |
| cohere large language model: Google Gemini for Python Oswald Campesato, 2024-03-07 This book provides a bridge between the worlds of Python 3 programming and Generative AI, aiming to equip readers with the skills to navigate both domains with confidence. It begins with an introduction to fundamental aspects of Python programming, which include various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. In addition, you will learn about loops, functions, data structures, NumPy, Pandas, conditional logic, and reserved words in Python. Further chapters show how to handle user input, manage exceptions, and work with command-line arguments. The text then transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including Bard (now called “Gemini”) and its competitors, are presented to give readers an understanding of the current AI landscape. The book discusses the capabilities of Bard, its strengths, weaknesses, and potential applications. Finally, you will learn how to generate a variety of Python 3 code samples via Bard. FEATURES: Includes a chapter on how to generate a variety of Python 3 code samples via Gemini Covers basic concepts of Python 3 such as loops, conditional logic, reserved words, user input, manage exceptions, work with command-line arguments, and more Includes companion files for downloading with source code and figures |
| cohere large language model: The New And Complete Dictionary Of The English Language John Ash, 1775 |
| cohere large language model: Integrating Generative AI in Education to Achieve Sustainable Development Goals Doshi, Ruchi, Dadhich, Manish, Poddar, Sandeep, Hiran, Kamal Kant, 2024-06-03 A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AIs rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education. |
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Cohere’s mission is to simplify healthcare by enabling patients, physicians, and health plans to efficiently collaborate on getting the right care, at the right time, at the right place, and with the …
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Cohere Health simplifies healthcare by enabling patients, physicians, and health plans to collaborate on getting the right care, at the right time, at the right place, and at the right cost.
Cohere & Humana prior authorization expansion
Cohere’s prior authorization platform helps providers and patients get to ‘yes’ more quickly. Cohere’s unique blend of real-time analytics and deep, evidence-based clinical intelligence …
Our Solutions - Cohere Health
Cohere combines responsible AI and machine learning (ML), deep, evidence-based clinical expertise and real-time analytics to digitize the process, support CMS-0057-F compliance …
Cohere Health & Humana expand partnership for physicians
BOSTON, MA (October 24, 2022) – Today Cohere Health, a leader in intelligent utilization management (UM) technology, announced that Humana Inc. (NYSE: HUM) will expand its use …
Cohere PaaS™ Intelligent Prior Authorization | Cohere Health®
Cohere’s clinical intelligence platform gives your utilization management team the power to in-source prior authorization to achieve administrative efficiencies, improve provider satisfaction, …
Streamline critical business tasks across your enterprise with ...
Cohere allows developers and enterprises to build large language model (LLM)-powered applications by offering simple-to-use models on AWS to deploy them securely and privately. …
Ke Qiu PERSONAL INTELLIGENT ASSISTANT BASED ON …
Using Local Data and Large Language Model Year 2024 Language English Pages 60 Name of Supervisor Magnus Sundell As large language models (LLMs) become increasingly capable of …
IN THE UNITED STATES DISTRICT COURT FOR THE …
Feb 13, 2025 · Cohere copies, uses, and disseminates Publishers’ news and magazine articles to build and deliver a commercial service that mimics, undercuts, and competes with lawful …
When Less is More: Investigating Data Pruning for
Cohere alexwang@cohere.com Marzieh Fadaee Cohere for AI marzieh@cohere.com Sara Hooker Cohere for AI sarahooker@cohere.com Abstract Large volumes of text data have …
IN THE UNITED STATES DISTRICT COURT FOR THE …
Feb 13, 2025 · Cohere copies, uses, and disseminates Publishers’ news and magazine articles to build and deliver a commercial service that mimics, undercuts, and competes with lawful …
From One to Many: Expanding the Scope of Toxicity …
luiza@cohere.com Patrick Lewis Cohere patrick@cohere.com Sara Hooker Cohere For AI sarahooker@cohere.com Beyza Ermis Cohere For AI beyza@cohere.com Abstract To date, …
=A Survey on Large Language Model (LLM) Security and
language modeling and autoregressive prediction) to un- derstand and process human language, by modeling the contextualized textsemantics andprobabilities fromlarge
Elo Uncovered: Robustness and Best Practices in Language …
in Language Model Evaluation ... Cohere for AI1 Cohere2 {meriem,edward,beyza,sarahooker,marzieh}@cohere.com Abstract In …
=A Survey on Large Language Model (LLM) Security and
The •
Assessing DxGPT: Diagnosing Rare Diseases with Various …
May 8, 2024 · utilizes GPT-4, but this study also compares its performance with other large language models, including Claude 3, Gemini 1.5 Pro, Llama, Mistral, Mixtral, and Cohere …
Artificial Analysis AI Review
Large 2 (Nov ’24) Llama 3.1 Instruct 405B GPT-4o (Nov ’24) GPT-4o mini Grok Beta Gemini 1.5 Flash (Sep ’24) DeepSeek-V2.5 (Dec ’24) Cohere Command R+ Jamba 1.5 Large Llama 3.3 …
On the application of Large Language Models for language …
Jul 18, 2023 · massive research collaborations (e.g. BLOOM: the BigScience Large Open-science Open-access Multilingual Language Model [29]), or to democratise LLMs for wider use in web …
Abstract - arXiv.org
model, arctic-embed-l outperforming closed source embedding models such as Cohere’s embed-v3 and Open AI’s text-embed-3-large. In addition to the details of our training recipe, we have …
Quick Start Guide to Large Language Models: Strategies and …
Praise for Quick Start Guide to Large Language Models “By balancing the potential of both open- and closed-source models, ... Cohere Open-Source Prompt Engineering Building a Q/A Bot …
The economic trade-offs of large language models: A case …
and model improvement. Large Language Models (LLMs) are a natural t for this technology, as they have achieved high ... and Cohere (Cohere, 2023a). These strategies can lead to an …
arXiv:2406.08598v3 [cs.CL] 11 Feb 2025
As Large Language Models (LLMs) continue to evolve, evaluating them remains a persistent challenge. Many recent evaluations use LLMs as judges to score outputs from other LLMs, …
Bias of AI-Generated Content: An Examination of News …
Key words: AI-generated content (AIGC), large language model (LLM), generative AI, ChatGPT, bias, gender bias, racial bias, prompt Introduction Large language models (LLMs), such as …
COHERE COMMAND R - BEST-IN-CLASS (RAG) AI TOOL IN …
A Large Language Model (LLM) optimized for enterprise-grade retrieval-augmented generation (RAG) and multilingual tasks. Enhances enterprise search, automated workflows, customer …
A Survey on Large Language Model (LLM) Security and …
language modeling and autoregressive prediction) to un-derstand and process human language, by modeling the contextualized textsemantics andprobabilities fromlarge amounts of text data. …
Large language models for data extraction from unstructured …
Large language models for data ... Cohere command (Cohere, Toronto, ON). The RoBERTa base model, a nowadays less sophisticated transformer- based model published in 2019, was used …
gptstudio: Use Large Language Models Directly in your …
Description Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see ...
Large Language Models: A Bird's Eye View - phontron.com
NLP With Large Language Models (Prompting) ... Mostly through APIs such as GPT, Cohere, PaLM. From Zero to ChatGPT Lots of web text davinci Lots of GitHub code Lots of annotated …
=A Survey on Large Language Model (LLM) Security and
language modeling and autoregressive prediction) to un- derstand and process human language, by modeling the contextualized textsemantics andprobabilities fromlarge
[Cohere for AI] Generating Images with Multimodal …
Cohere for AI 1. 2 LLM In-context learning (Greater) sensitivity to input prompts Zero-shot abilities ... 🐟 GILL Generating Images with Large Language Models. 5 🐟 GILL: A More General …
SuperCLUE: A Comprehensive Chinese Large Language …
user-model interactions with user-reported ratings from a model battle platform LangYa Leaderboard 3. On this plat-form, users can communicate with two anonymous mod-els and …
Exploring the Landscape of Large Language Models: A …
Transformers), Falcon, and Cohere, are trained on extensive datasets that encompass a variety of textual sources. This training facilitates the acquisition of complex language patterns, idiomatic …
SLM-Mod: Small Language Models Surpass LLMs at Content …
For a language model Mand a comment Talong with its context C, and subred-dit rules R,we prompt the model with a prompt p k where k∈{0,2,4}represents the number of in-context …
Evaluating Embedding APIs for Information Retrieval - arXiv.org
into offering access to large language models through APIs. One particular type, suitable for dense retrieval, is a semantic embedding ser- ... .3 Cohere also provides a multilingual model, …
=A Survey on Large Language Model (LLM) Security and …
Model Date Provider Open-Source Params Tunability gpt-4[187] 2023.03 OpenAI - gpt-3.5-turbo 2021.09 OpenAI 175B gpt-3[24] 2020.06 OpenAI 175B cohere-medium[156] 2022.07 Cohere …
Develop an End-to- End RAG Solution with Azure AI
Mistral Large, Cohere Command R+ 100s of Open models from HuggingFace Open models from Meta, Databricks and Snowflake, Nvidia Open and propriety • Core42 JAIS Arabic ...
Language Models and Generative AI Workloads NVIDIA …
NVIDIA H100 NVL for Large Language Model Deployment is ideal for deploying massive LLMs like ChatGPT at scale. The new H100 NVL with 94GB of memory with Transformer Engine …
A Survey on Large Language Model (LLM) Security and …
Model Date Provider Open-Source Params Tunability gpt-4[63] 2023.03 OpenAI 1.7T gpt-3.5-turbo 2021.09 OpenAI 175B gpt-3[24] 2020.06 OpenAI 175B cohere-medium[161] 2022.07 …
HolisticEvaluationofLanguageModels - arXiv.org
J1-Large v1 Anthropic- LM v4-s3 BLOOM T0++ Cohere Xlarge v20220609 v20220720 Cohere Large v20220720 Cohere Medium Cohere Small v20220720 GPT- NeoX GPT-J T5 UL2 OPT …
HolisticEvaluationofLanguageModels - OpenReview
J1-Large v1 Anthropic- LM v4-s3 BLOOM T0++ Cohere Xlarge v20220609 v20220720 Cohere Large v20220720 Cohere Medium Cohere Small v20220720 GPT- NeoX GPT-J T5 UL2 OPT …
RLHF Can Speak Many Languages: Unlocking Multilingual …
1Cohere For AI, 2Cohere {johndang,arash,kelly,juliakreutzer,ahmet,sarahooker}@cohere.com Abstract Preference optimization techniques have be-come a standard nal stage for training …
arXiv:2301.13848v1 [cs.CL] 31 Jan 2023
Cohere xlarge v20220609 Cohere 52.4B 7 Cohere(2022) Anthropic-LM v4-s3 Anthropic 52B 3 Bai et al.(2022) Table 1: List of large language models we benchmarked with human evaluation. …
Meta-Learning of Prompt Generation for Lightweight Prompt …
Language Model as a Service As large-scale language models (LMs) show astonishing per-formances across different tasks, several compa-nies train such models and provide them as …
A Comprehensive Evaluation of Large Language Models in …
Jan 24, 2024 · Keywords: Large Language Model, Gene-gene Interaction, KEGG Pathway, Biomedical Text Mining A major development in 1. INTRODUCTION ... [10], while the Cohere …
Conceptual structure coheres in human cognition but not in …
The model was deemed successful because the choice computed in this way often aligned with the choices of native English speakers. Such a procedure was not just a useful way for …
Ì ULER: What’s the Real Context Size of Your Long-Context …
hibit large performance drops as the context length increases. While these models all claim context sizes of 32K tokens or greater, only half of them can maintain satisfactory performance …
GPT-NeoX-20B: An Open-Source Autoregressive Language …
GPT-NeoX-20B: An Open-Source Autoregressive Language Model Sid Black* Stella Biderman * Eric Hallahan * Quentin Anthony Leo Gao Laurence Golding Horace He ... sion in research …
Understanding Privacy Risks of Embeddings Induced by Large …
sions. The advancement of large language models enhances their ability to capture and represent complex semantics more effectively, such that an increasing number of businesses (e.g., …
GOODTRIEVER : Adaptive Toxicity Mitigation with Retrieval …
Cohere For AI luiza@cohere.com ... With the widespread adoption of large language model systems such as ChatGPT (OpenAI,2022; Liu et al.,2023) and OpenAssistant (Köpf et al., …
A Prompt Engineering Approach and a Knowledge Graph …
A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers George Hannah1*†, Rita T. Sousa2*†, Ioannis …
A Survey of GPT-3 Family Large Language Models Including …
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4 Katikapalli Subramanyam Kalyan Akmmus AI, Trichy, India Email: kalyan@akmmusai.pro, Website: …
From One to Many: Expanding the Scope of Toxicity …
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models Luiza Pozzobon† Cohere for AI luiza@cohere.com Patrick Lewis Cohere patrick@cohere.com
Direct Preference Optimization: Your Language Model is …
Your Language Model is Secretly a Reward Model Rafael Rafailov * 1Archit Sharma Eric Mitchell Stefano Ermon1 2 Chris Manning 1Chelsea Finn Abstract While large-scale unsupervised …
Correspondence: kelly@cohere - arXiv.org
1Cohere 2Cohere For AI Correspondence: kelly@cohere.com Abstract Quantization techniques are widely used to improve inference speed and deployment of large language models. While …
Educational Evaluation with Large Language Models (LLMs): …
Background Advanced Large Language Models (LLMs), such as ChatGPT-4, have gained ... such as ChatGPT, Claude, Cohere, Gemini, LLaMa and Mistral to streamline evaluation and …
From One to Many: Expanding the Scope of Toxicity …
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models Luiza Pozzobon† Cohere for AI luiza@cohere.com Patrick Lewis Cohere patrick@cohere.com
