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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/open-source-llmops-solutions
课程评论：没有评论
课程名称：开源 LLMOps 解决方案 课程概述：本课程旨在让学员掌握大语言模型（LLM）的基础知识，并通过部署基于开源模型的解决方案进行实践。课程结束时，学员将能够使用先进的开源 LLM 创建 AI 应用程序，采用代码优先的方法。 内容大纲： 1. **入门开源生态系统** 本模块将教你如何利用预训练的自然语言处理模型构建 NLP 应用程序。我们将探索流行的开源模型，如 BERT，并学习如何使用 HuggingFace Transformers 等库访问这些模型，执行文本分类、问答和文本生成等任务。关键技能包括使用大语言模型合成扩增数据集，通过向模型输入示例并提取生成的文本来创建更多的训练数据。通过动手练习，学员将在 Python 中构建基本的 NLP 流水线，完成情感分析等任务。课程结束时，学员将获得使用最先进的 NLP 技术创建语言应用的实践经验。 2. **使用本地 LLM（来自 LLamafile 到 Whisper.cpp）** 本模块将指导你如何在本地运行语言模型，确保数据隐私，避免延迟和费用，使用 Mixtral 模型和 llamafile。 3. **应用项目** 在本模块中，学员将使用 Transformers.js 和 ONNX 在浏览器中运行模型。你将获得将模型移植到 ONNX 运行时的经验，并学习如何在浏览器中进行部署。同时，还将使用 Cosmopolitan 项目构建一个可在不同系统之间轻松移植的短语生成器。 4. **总结与最终挑战** 本模块将专注于完成几个外部实验和动手示例，使学员能够熟练操作本地 LLM，使用 Python 通过 API 连接，并使用 Rust 编程语言构建解决方案。 通过完成本课程，学员将能够掌握开源 LLM 的使用技巧，设计并实施有效的 AI 应用程序。
Name:Getting Started with Open Source Ecosystem
Description:In this module, you will learn how to leverage pre-trained natural language processing models to build NLP applications. We will explore popular open source models like BERT. You will learn how to access these models using libraries like HuggingFace Transformers and use them for tasks like text classification, question answering, and text generation. A key skill will be using large language models to synthetically augment datasets. By feeding the model examples and extracting the text it generates, you can create more training data. Through hands-on exercises, you will build basic NLP pipelines in Python that use pre-trained models to perform tasks like sentiment analysis. By the end of the module, you'll have practical experience using state-of-the-art NLP techniques to create capable language applications.
Name:Using Local LLMs from LLamafile to Whisper.cpp
Description:In this module, you run language models locally. Keep data private. Avoid latency and fees. Use Mixtral model and llamafile.
Name:Applied Projects
Description:In this module, you will use models in the browser with Transformers.js and ONNX. You will gain experience on porting models to the ONNX runtime and experience how to put them on the browser. You will also use the Cosmopolitan project to build a phrase generator that is easily portable on different systems.
Name:Recap and Final Challenges
Description:In this module, you will focus on completing several external labs and hands-on examples that will allow you to feel comfortable running local LLMs, connect to them with APIs using Python as well as building solutions with the Rust programming language
Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach. You will start by gaining an in-depth understanding of how LLMs work, including model architectures like transformers and advancements like sparse expert models. Hands-on labs will walk you through launching clou
