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所在平台: Udemy |
课程主页: https://www.udemy.com/course/quantizing-llms-with-pytorch-and-hugging-face/
课程评论：没有评论
课程名称：使用PyTorch和Hugging Face对大语言模型进行量化 课程概述：随着大语言模型（LLMs）在各行业的应用不断增加，高效部署这些计算密集型模型的挑战日益重要。本课程《使用PyTorch和Hugging Face对大语言模型进行量化》将为您提供工具和技术，利用量化这一重要优化方法，减少内存使用并提高推理速度，同时保持模型准确性。 在本实践课程中，您将首先掌握量化的基本概念。通过直观的解释，您将理解线性量化、不同数据类型及其内存需求的概念，并学习如何手动进行值的量化以加深实用理解。 接下来，深入学习高级量化技术，包括对称和非对称量化及其应用。您将获得每通道和每组量化方法的实践经验，并学习如何计算和减轻量化误差。通过实际案例，您将看到这些方法的实际应用及其对模型性能的影响。 最后一部分着眼于前沿主题，如2位和4位量化。您将学习比特打包和解包的实现方式，逐步运行这些技术，并将其应用于真实的Hugging Face模型。课程结束时，您将熟练使用PyTorch和Bits and Bytes等工具，将模型量化到不同的精度，从而优化小规模和企业级的LLM部署。 无论您是机器学习从业者、探索优化技术的数据科学家，还是专注于高效模型部署的系统工程师，本课程都将为您提供全面的量化指南。通过理论与实践编码练习的结合，您将获得在现代AI应用中降低成本和提升计算效率所需的专业知识。
As large language models (LLMs) continue to transform industries, the challenge of deploying these computationally intensive models efficiently has become paramount. This course, Quantizing LLMs with PyTorch and Hugging Face, equips you with the tools and techniques to harness quantization, an essential optimization method, to reduce memory usage and improve inference speed without significant loss of model accuracy.In this hands-on course, you'll start by mastering the fundamentals of quantization. Through intuitive explanations, you will demystify concepts like linear quantization, different data types and their memory requirements, and how to manually quantize values for practical understanding.Next, delve into advanced quantization techniques, including symmetric and asymmetric quantization, and their applications. Gain practical experience with per-channel and per-group quantization methods, and learn how to compute and mitigate quantization errors. Through real-world examples, you'll see these methods come to life and understand their impact on model performance.The final section focuses on cutting-edge topics such as 2-bit and 4-bit quantization. You'll learn how bit packing and unpacking work, implement these techniques step-by-step, and apply them to real Hugging Face models. By the end of the course, you'll be adept at using tools like PyTorch and Bits and Bytes to quantize models to varying precisions, enabling you to optimize both small-scale and enterprise-level LLM deployments.Whether you are a machine learning practitioner, a data scientist exploring optimization techniques, or a systems engineer focused on efficient model deployment, this course provides a comprehensive guide to quantization. With a blend of theory and practical coding exercises, you'll gain the expertise needed to reduce costs and improve computational efficiency in modern AI applications.
