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所在平台: Udemy |
课程主页: https://www.udemy.com/course/train-opensource-large-language-models-from-zero-to-hero/
课程评论：没有评论
课程名称：从零到英雄：训练开源大型语言模型 课程概述： 本课程旨在帮助开发者和数据科学家掌握大型语言模型（LLMs）的训练和优化技术，让学员充分发挥LLMs的潜力。课程内容涵盖从基础到高级的各种主题，帮助开发者理解LLMs的工作原理，同时使数据科学家学习简单与复杂的训练技巧。课程开篇将介绍语言模型的基本原理及变压器架构的变革性影响，指导学员搭建开发环境，并从零开始训练第一个模型。 深度探讨最新的微调方法，如LoRA、QLoRA和DoRA，以提高模型性能，学习如何利用Flash Attention和NEFTune技术提高LLM对噪声数据的鲁棒性，并通过实践编码环节获得实际经验。课程还将探索使LLM与人类偏好对齐的高级方法，如直接偏好优化（DPO）、KTO和ORPO，帮助学员实施这些技术，确保模型不仅具有良好的性能，还符合用户期望和伦理标准。 最后，学员将学习如何利用多GPU设置、模型并行性、完全分片数据并行（FSDP）训练和Unsloth框架加速LLM的训练，以提高速度并减少VRAM使用。通过本课程的学习，学员将对训练、微调和优化开源LLMs有深入理解和实践经验。 如有任何问题或请求，请通过以下邮件与我联系：gal@apriori.ai 祝学习愉快！
Unlock the full potential of Large Language Models (LLMs) with this comprehensive course designed for developers and data scientists eager to master advanced training and optimization techniques.I'll cover everything from A to Z, helping developers understand how LLMs works and data scientists learn simple and advance training techniques. Starting with the fundamentals of language models and the transformative power of the Transformer architecture, you'll set up your development environment and train your first model from scratch.Dive deep into cutting-edge fine-tuning methods like LoRA, QLoRA, and DoRA to enhance model performance efficiently. Learn how to improve LLM robustness against noisy data using techniques like Flash Attention and NEFTune, and gain practical experience through hands-on coding sessions.The course also explores aligning LLMs to human preferences using advanced methods such as Direct Preference Optimization (DPO), KTO, and ORPO. You'll implement these techniques to ensure your models not only perform well but also align with user expectations and ethical standards.Finally, accelerate your LLM training with multi-GPU setups, model parallelism, Fully Sharded Data Parallel (FSDP) training, and the Unsloth framework to boost speed and reduce VRAM usage. By the end of this course, you'll have a good understanding and practical experience to train, fine-tune, and optimize robust open-source LLMs.For any problem or request please use this email to communicate with me: gal@apriori.aiHappy learning!
