Course Code: ftdeepseekllm
Duration: 21 hours
Prerequisites:
  • 机器学习和深度学习框架的经验
  • 对变压器和大型语言模型(LLM)的熟悉
  • 了解数据预处理和模型训练技术

观众

  • 探索LLM微调的AI研究人员
  • 开发定制AI模型的机器学习工程师
  • 实施AI驱动解决方案的高级开发人员
Overview:

DeepSeek LLM，包括像DeepSeek-R1和DeepSeek-V3这样的模型，为构建AI应用程序提供了强大的基础。对这些模型进行领域特定数据集的微调使得能够创建针对业务需求量身定制的专门AI解决方案。

这个由教师主导的现场培训（在线或现场）旨在针对高级AI研究人员、机器学习工程师和希望微调DeepSeek LLM模型以创建针对特定行业、领域或业务需求的专门AI应用程序的开发人员。

参加者在培训结束时将能够：

  • 了解DeepSeek模型的架构和能力，包括DeepSeek-R1和DeepSeek-V3。
  • 准备数据集并对数据进行预处理，以便进行微调。
  • 对特定领域的应用微调DeepSeek LLM。
  • 高效地优化和部署经过微调的模型。

课程格式

  • 互动讲座和讨论。
  • 大量的练习和实践。
  • 在现场实验室环境中进行实动。

课程定制选项

  • 要请求此课程的定制培训，请联系我们安排。
Course Outline:

介绍DeepSeek LLMFine-Tuning

  • DeepSeek模型的概述，例如DeepSeek-R1和DeepSeek-V3
  • 了解微调LLM的必要性
  • 微调与提示工程的比较

为Fine-Tuning准备数据集

  • 策划特定领域的数据集
  • 数据预处理和清理技术
  • 针对DeepSeek LLM进行令牌化和数据集格式化

设置Fine-Tuning环境

  • 配置GPU和TPU加速
  • 使用DeepSeek LLM设置Hugging Face Transformers
  • 了解微调的超参数

Fine-Tuning DeepSeek LLM的监督微调

  • 实施监督微调
  • 使用LoRA（低秩适配）和PEFT（参数效率Fine-Tuning）
  • 针对大规模数据集运行分布式微调

评估和优化微调模型

  • 通过评估指标评估模型性能
  • 处理过拟合和欠拟合
  • 优化推理速度和模型效率

部署微调后的DeepSeek模型

  • 对模型进行打包以便API部署
  • 将微调后的模型集成到应用程序中
  • 通过云计算和边缘计算来扩展部署

实际的Use Case和应用

  • 金融、医疗和客户支持的微调LLM
  • 行业应用的案例研究
  • 领域特定AI模型的伦理考虑

总结和结论

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