Course Code: finetuninglora
Duration: 14 hours
Prerequisites:
  • 对机器学习概念的基本理解
  • 熟悉 Python 程式设计
  • 使用 TensorFlow 或 PyTorch 等深度学习框架的经验

观众

  • 开发人员
  • AI 从业者
Overview:

Low-Rank Adaptation （LoRA） 是一种尖端技术，通过减少传统方法的计算和记忆体要求来有效地微调大规模模型。本课程提供有关使用 LoRA 为特定任务调整预训练模型的实践指导，使其成为资源受限环境的理想选择。

这种由讲师指导的现场培训（在线或现场）面向希望在不需要大量计算资源的情况下为大型模型实施微调策略的中级开发人员和 AI 从业者。

在本次培训结束时，参与者将能够：

  • 了解低秩适应 （LoRA） 的原理。
  • 实施LoRA以高效微调大型模型。
  • 针对资源受限的环境优化微调。
  • 评估和部署LoRA调优模型以用于实际应用。

课程形式

  • 互动讲座和讨论。
  • 大量的练习和练习。
  • 在即时实验室环境中动手实施。

课程自定义选项

  • 要申请本课程的定制培训，请联系我们进行安排。
Course Outline:

低秩适应 （LoRA） 简介

  • 什么是LoRA？
  • LoRA 在高效微调方面的优势
  • 与传统微调方法的比较

了解微调挑战

  • 传统微调的局限性
  • 计算和记忆体约束
  • 为什么LoRA是有效的替代方案

设置环境

  • 安装 Python 和所需的库
  • 设置 Hugging Face Transformer 和 PyTorch
  • 探索LoRA相容模型

实施LoRA

  • LoRA 方法概述
  • 使用LoRA调整预训练模型
  • 针对特定任务进行微调（例如，文字分类、摘要）

使用LoRA优化微调

  • LoRA 的超参数优化
  • 评估模型性能
  • 最大限度地减少资源消耗

动手实验

  • 使用LoRA微调BERT以进行文字分类
  • 将 LoRA 应用于 T5 以执行摘要任务
  • 探索独特任务的自定义 LoRA 配置

部署LoRA调优模型

  • 汇出和保存LoRA优化模型
  • 将LoRA模型整合到应用程式中
  • 在生产环境中部署模型

LoRA 中的高级技术

  • 将LoRA与其他优化方法相结合
  • 为更大的模型和数据集扩展LoRA
  • 使用LoRA探索多模式应用

挑战和最佳实践

  • 避免使用LoRA进行过拟合
  • 确保实验的可重复性
  • 故障排除和调试策略

高效微调的未来趋势

  • LoRA 和相关方法的新兴创新
  • LoRA 在实际 AI 中的应用
  • 高效微调对 AI 开发的影响

总结和后续步骤

Sites Published:

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