Course Code: ftdeepseekllm
Duration: 21 hours
Prerequisites:
  • 機器學習和深度學習框架的經驗
  • 對變壓器和大型語言模型(LLM)的熟悉
  • 了解數據預處理和模型訓練技術

觀眾

  • 探索LLM微調的AI研究人員
  • 開發定制AI模型的機器學習工程師
  • 實施AI驅動解決方案的高級開發人員
Overview:

DeepSeek LLM，包括像DeepSeek-R1和DeepSeek-V3這樣的模型，為構建AI應用程序提供了強大的基礎。對這些模型進行領域特定數據集的微調使得能夠創建針對業務需求量身定制的專門AI解決方案。

這個由教師主導的現場培訓（在線或現場）旨在針對高級AI研究人員、機器學習工程師和希望微調DeepSeek LLM模型以創建針對特定行業、領域或業務需求的專門AI應用程序的開發人員。

參加者在培訓結束時將能夠：

  • 了解DeepSeek模型的架構和能力，包括DeepSeek-R1和DeepSeek-V3。
  • 準備數據集並對數據進行預處理，以便進行微調。
  • 對特定領域的應用微調DeepSeek LLM。
  • 高效地優化和部署經過微調的模型。

課程格式

  • 互動講座和討論。
  • 大量的練習和實踐。
  • 在現場實驗室環境中進行實動。

課程定制選項

  • 要請求此課程的定制培訓，請聯繫我們安排。
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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