Large Language Models Projects: Apply and Implement Strategies for Large Language Models
This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain.
The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions.
This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing.
What You Will Learn
Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings
Who This Book Is For
Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks
ISBN-10:303168026X.356页。PDF。12 MB英语。2024。ISBN-10:303168026X。356页。12 MB英文。2024年。ISBN-10：303168026X.356页。PDF，12 MB英文；ISBN-10；303168026X-356页。PDF.12 MB英文2024年。ISBN-10:303168026X。356页。PDF。12 MB英语。2024年，ISBN-10:303168026X.356页。PDF.12 MB这本书为您提供了使用OpenAI和拥抱脸库模型的实践体验。你将使用各种工具，在小项目上工作，逐渐应用你获得的新知识。 这本书分为三个部分。第一部分介绍技术和库。在这里，您将通过小示例探索不同的技术，为下一节中的项目构建做准备。您将学习在大型语言模型世界中使用通用库。涵盖的主题和技术包括聊天机器人、代码生成、OpenAI API、拥抱脸、向量数据库、LangChain、微调、PEFT微调、软提示微调、LoRA、QLoRA、评估模型和直接偏好优化。第二部分重点介绍项目。您将创建项目，了解设计决策。每个项目可能有不止一个可能的实现，因为通常不只有一个好的解决方案。您还将探索LLMOps相关主题。第三部分深入探讨了企业解决方案。大型语言模型不是一个独立的解决方案；在大型企业环境中，它们是难题的一部分。您将探索如何构建能够改造拥有数千名员工的组织的解决方案，突出大型语言模型在这些新解决方案中发挥的主要作用。 本书使您能够自信地导航和实施大型语言模型，使您能够应对不断发展的语言处理环境中的各种挑战。 你将学到什么 通过使用OpenAI和拥抱脸库中的模型获得实践经验使用与大型语言模型相关的基本库，涵盖聊天机器人、代码生成、OpenAI API、拥抱脸和矢量数据库等主题使用LLM创建和实施项目，同时理解所涉及的设计决策了解大型语言模型在大型企业环境中的作用 这本书是写给谁的 数据分析师、数据科学、Python开发人员和软件专业人员，他们对学习NLP、LLM的基础以及为各种任务构建现代LLM应用程序的过程感兴趣本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。
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