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所在平台: Udemy |
课程主页: https://www.udemy.com/course/building-llms-like-chatgpt-from-scratch-and-cloud-deployment/
课程评论：没有评论
课程名称：从零开始构建类似ChatGPT的大型语言模型及云部署 课程概述：大型语言模型如GPT-4、Llama和Mistral已经不再是科幻小说中的概念，它们正引领技术的新前沿，应用于先进的聊天机器人和开创性的科学发现。然而，对于大多数人来说，它们仍然是一个“黑箱”。虽然许多人可以使用API，但真正理解这些模型如何从内部运作的技能却十分稀缺和宝贵。本课程旨在揭开这一神秘的面纱，带领您从头开始逐行编写一个强大的现代大型语言模型，特别是高效强大的Mistral 7B架构。 该课程不仅仅是高层次的概述，而是一次深入的实践工程之旅，旨在编写一个完整的LLM。我们将理论与实际生产级代码相结合，您将不仅仅学习Grouped-Query Attention，还会亲自实现它；您将构建KV缓存，以加速模型推断。我们相信，真正掌握的最佳方式就是构建。 在课程中，您将从基础概念开始，逐步了解引领变换器革命的原理，涵盖每一个关键组件。最终，您将学习如何将自己构建的模型部署到真实世界中，使用行业标准的高性能vLLM推理引擎在Runpod平台上进行部署。 完成本课程后，您将从LLM用户转变为LLM架构师，掌握区分专家与普通人的第一原则知识，赋予您在AI前沿进行构建、调试和创新的能力。 您将学习并理解： - LLM的起源：从RNN到推动变换器机制发展的演变。 - 变换器的揭秘：深入探讨变换器架构为何能有效，并了解训练与推断之间的关键差异。 - Mistral 7B蓝图：如何架构一个完整的大型语言模型，复制最先进模型的全球结构。 - 从头开始的核心机制： - 令牌化：将原始文本转化为模型能够理解的格式。 - 旋转位置编码（RoPE）：实现现代技术以注入位置意识。 - 分组查询注意力（GQA）：编码Mistral等模型高效的创新。 - 滑动窗口注意力（SWA）：实现允许处理更长序列的注意力变体。 - KV缓存：构建用于推断时快速文本生成的核心组件。 - 从头到尾的模型构建：将所有组成部分，包括单个注意力头和完整的变换器块，组合成功能齐全的LLM。 - 让您的模型焕发生机：实现文本生成逻辑，以使您的模型能够创建连贯的语言。 - 生产级部署：使用在Runpod云平台上速度极快的vLLM引擎，进行自定义模型的实际部署。 如果您是准备超越API，真正理解改变世界的技术的开发人员、机器学习工程师或研究人员，则本课程正是为您量身定做。我们期待着引导您成为真正的LLM专家。让我们开始构建吧！
Large Language Models like GPT-4, Llama, and Mistral are no longer science fiction; they are the new frontier of technology, powering everything from advanced chatbots to revolutionary scientific discovery. But to most, they remain a "black box." While many can use an API, very few possess the rare and valuable skill of understanding how these incredible models work from the inside out.What if you could peel back the curtain? What if you could build a powerful, modern Large Language Model, not just by tweaking a few lines of code, but by writing it from the ground up, line by line?This course is not another high-level overview. It's a deep, hands-on engineering journey to code a complete LLM-specifically, the highly efficient and powerful Mistral 7B architecture-from scratch in PyTorch. We bridge the gap between abstract theory and practical, production-grade code. You won't just learn what Grouped-Query Attention is; you'll implement it. You won't just read about the KV Cache; you'll build it to accelerate your model's inference.We believe the best way to achieve true mastery is by building. Starting with the foundational concepts that led to the transformer revolution, we will guide you step-by-step through every critical component. Finally, you'll take your custom-built model and learn to deploy it for real-world use with the industry-standard, high-performance vLLM Inference Engine on Runpod.After completing this course, you will have moved from an LLM user to an LLM architect. You will possess the first-principles knowledge that separates the experts from the crowd and empowers you to build, debug, and innovate at the cutting edge of AI.You will learn to build and understand:The Origins of LLMs: The evolution from RNNs to the Attention mechanism that started it all.The Transformer, Demystified: A deep dive into why the Transformer architecture works and the critical differences between training and inference.The Mistral 7B Blueprint: How to architect a complete Large Language Model, replicating the global structure of a state-of-the-art model.Core Mechanics from Scratch:Tokenization: Turning raw text into a format your model can understand.Rotary Positional Encoding (RoPE): Implementing the modern technique for injecting positional awareness.Grouped-Query Attention (GQA): Coding the innovation that makes models like Mistral so efficient.Sliding Window Attention (SWA): Implementing the attention variant that allows for processing much longer sequences.The KV Cache: Building the essential component for lightning-fast text generation during inference.End-to-End Model Construction: Assembling all the pieces-from individual attention heads to full Transformer Blocks-into a functional LLM in PyTorch.Bringing Your Model to Life: Implementing the logic for text generation to see your model create coherent language.Production-Grade Deployment: A practical guide to deploying your custom model using the blazingly fast vLLM engine on the Runpod cloud platform.If you are a developer, ML engineer, or researcher ready to go beyond the API and truly understand the technology that is changing the world, this course was designed for you. We are thrilled to guide you on your journey to becoming a true LLM expert.Let's start building.
