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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-fine-tuning-with-llama-huggingface-transformers-2024/
课程评论：没有评论
课程名称：终极指南：使用HuggingFace的LLama GPT进行精细调优 课程概述：深入了解人工智能，本课程专为希望掌握指令调优或监督精细调优（SFT）的爱好者而设计，使用强大的Tiny Llama 1.1B模型。该课程适合数据科学家、机器学习工程师及任何对精细调优大语言模型的实际应用感兴趣的人士。课程内容特别旨在提供使用Tiny Llama 1.1B模型的实践经验，该模型基于3000亿个标记训练，并与Databricks开发的Dolly 15k记录数据集配对。通过本课程，您将不仅学习SFT的基础知识，还能有效使用HuggingFace Transformers库准备数据集、设置训练以及计算准确性，所有操作均在Google Colab的便捷环境中进行。 您将学习到的内容： 1. **监督精细调优（SFT）介绍**：深入理解SFT的原理及其在特定任务中调整先进语言模型的重要性。 2. **Tiny Llama 1.1B概述**：探讨Tiny Llama 1.1B模型的架构和特点，了解其在人工智能生态系统中的作用。 3. **使用Dolly 15k进行数据集准备**：学习如何准备和预处理Dolly 15k记录数据集，以确保精细调优过程的顺利高效。 4. **HuggingFace Transformers库**：获得使用HuggingFace库的实践经验，学习如何将模型加载到GPU并准备训练环境。 5. **训练设置与执行**：逐步了解如何在Google Colab上设置和执行精细调优过程，重点关注实际实施。 6. **性能评估**：学习如何计算准确性并评估模型的表现，使用指标确保您的SFT工作有效。 7. **实际应用**：将新技能转化为实际问题，理解如何将精细调优的模型适应于各种领域。 模型使用：TinyLlama-1.1B-intermediate-step-1431k-3T 数据集使用：databricks-dolly-15k 适合人群： - 寻求在NLP和指令调优领域专业化的数据科学家和机器学习工程师。 - 希望实施和扩展行业应用中精细调优语言模型的AI从业者。 - 渴望增强应用程序语言理解能力的软件开发者。 - 希望获得最新AI精细调优技术实践经验的学生和学术人士。 先决条件： - 精通Python，并熟悉机器学习和NLP概念。 - 具备神经网络框架的经验，优选使用HuggingFace Transformers库的PyTorch。 - 拥有Google账号以便访问Google Colab进行实践练习。
Dive into the world of AI with our comprehensive Udemy course, designed for enthusiasts eager to master instruction tuning, or supervised fine-tuning (SFT), using the incredibly powerful Tiny Llama 1.1B model. This course is perfect for data scientists, ML engineers, and anyone interested in the practical applications of fine-tuning large language models.Our curriculum is specially crafted to provide you with hands-on experience using the Tiny Llama 1.1B model, trained on a vast 3 trillion tokens and paired with the Databricks-crafted Dolly 15k record dataset. Through this course, you'll not only learn the basics of SFT but also how to effectively use the HuggingFace Transformers library to prepare your dataset, set up training, and compute accuracy-all within the accessible environment of Google Colab.What You Will LearnIntroduction to Supervised Fine-Tuning (SFT): Gain a solid understanding of the principles behind SFT and its importance in tailoring state-of-the-art language models to specific tasks.Exploring Tiny Llama 1.1B: Delve into the architecture and features of the Tiny Llama 1.1B model and discover its role in the AI ecosystem.Dataset Preparation with Dolly 15k: Learn how to prepare and preprocess the Dolly 15k record dataset to ensure your fine-tuning process is seamless and efficient.HuggingFace Transformers Library: Get hands-on experience with the HuggingFace library, learning how to load models onto a GPU and prepare your training environment.Training Setup and Execution: Walk through the steps to set up and execute the fine-tuning process using Google Colab with a focus on practical implementation.Performance Evaluation: Learn how to compute accuracy and evaluate your model's performance, using metrics to ensure your SFT efforts are effective.Real-World Application: Translate your new skills to real-world problems, understanding how to adapt your fine-tuned model to various domains.Model Used: TinyLlama-1.1B-intermediate-step-1431k-3TDataset Used: databricks-dolly-15kWho This Course is ForData Scientists and Machine Learning Engineers seeking to specialize in NLP and instruction tuning.AI Practitioners looking to implement and scale fine-tuned language models for industry applications.Software Developers eager to enhance applications with sophisticated language understanding capabilities.Students and Academics desiring hands-on experience with state-of-the-art AI fine-tuning techniques.PrerequisitesProficiency in Python and familiarity with machine learning and NLP concepts.Experience with neural network frameworks, preferably PyTorch, as used by the HuggingFace Transformers library.A Google account to access Google Colab for hands-on exercises.
