Course Code: ftllmqlora
Duration: 14 hours
Prerequisites:
  • 了解机器学习基础知识和神经网路
  • 具备模型微调和迁移学习的经验
  • 熟悉大型语言模型（LLMs）和深度学习框架（例如PyTorch, TensorFlow）

目标受众

  • 机器学习工程师
  • AI开发人员
  • 数据科学家
Overview:

QLoRA 是一种先进的技术，通过利用量化方法来微调大型语言模型（LLMs），提供了一种更高效的方式来微调这些模型，而无需承担巨大的计算成本。本次培训将涵盖使用 QLoRA 微调 LLMs 的理论基础和实际应用。

这是一个由讲师主导的培训（线上或线下），针对中高级别的机器学习工程师、AI 开发者和数据科学家，他们希望学习如何使用 QLoRA 来高效地微调大型模型，以适应特定任务和自定义需求。

在培训结束时，参与者将能够：

  • 理解 QLoRA 和量化技术在 LLMs 中的理论基础。
  • 在微调大型语言模型时，实现 QLoRA 以应用于特定领域。
  • 在有限的计算资源下，使用量化技术优化微调性能。
  • 高效地部署和评估微调后的模型，应用于实际场景。

课程形式

  • 互动式讲座和讨论。
  • 大量的练习和实践。
  • 在即时实验室环境中进行动手操作。

课程定制选项

  • 如需为此课程定制培训，请联系我们安排。
Course Outline:

QLoRA与量化简介

  • 量化概述及其在模型优化中的作用
  • QLoRA框架介绍及其优势
  • QLoRA与传统微调方法的关键差异

Large Language Models (LLMs)基础知识

  • LLM简介及其架构
  • 大规模微调大型模型的挑战
  • 量化如何帮助克服LLM微调中的计算限制

为Fine-Tuning LLM实施QLoRA

  • 设置QLoRA框架和环境
  • 准备用于QLoRA微调的数据集
  • 使用Python和PyTorch/TensorFlow在LLM上实施QLoRA的逐步指南

使用QLoRA优化Fine-Tuning性能

  • 如何平衡模型准确性和量化性能
  • 在微调期间减少计算成本和内存使用的技术
  • 使用最低硬件需求进行微调的策略

评估微调模型

  • 如何评估微调模型的有效性
  • 语言模型的常见评估指标
  • 微调后优化模型性能并解决问题

部署和扩展微调模型

  • 将量化LLM部署到生产环境的最佳实践
  • 扩展部署以处理实时请求
  • 用于模型部署和监控的工具和框架

实际Use Case和案例研究

  • 案例研究：为客户支持和NLP任务微调LLM
  • 在医疗、金融和电子商务等行业中微调LLM的示例
  • 从实际部署QLoRA模型中学到的经验教训

总结与下一步

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