Course Code: ftllmqlora
Duration: 14 hours
Prerequisites:
  • 了解機器學習基礎知識和神經網路
  • 具備模型微調和遷移學習的經驗
  • 熟悉大型語言模型（LLMs）和深度學習框架（例如PyTorch, TensorFlow）

目標受眾

  • 機器學習工程師
  • AI開發人員
  • 數據科學家
Overview:

QLoRA 是一種先進的技術，通過利用量化方法來微調大型語言模型（LLMs），提供了一種更高效的方式來微調這些模型，而無需承擔巨大的計算成本。本次培訓將涵蓋使用 QLoRA 微調 LLMs 的理論基礎和實際應用。

這是一個由講師主導的培訓（線上或線下），針對中高級別的機器學習工程師、AI 開發者和數據科學家，他們希望學習如何使用 QLoRA 來高效地微調大型模型，以適應特定任務和自定義需求。

在培訓結束時，參與者將能夠：

  • 理解 QLoRA 和量化技術在 LLMs 中的理論基礎。
  • 在微調大型語言模型時，實現 QLoRA 以應用於特定領域。
  • 在有限的計算資源下，使用量化技術優化微調性能。
  • 高效地部署和評估微調後的模型，應用於實際場景。

課程形式

  • 互動式講座和討論。
  • 大量的練習和實踐。
  • 在即時實驗室環境中進行動手操作。

課程定制選項

  • 如需為此課程定制培訓，請聯繫我們安排。
Course Outline:

QLoRA與量化簡介

  • 量化概述及其在模型優化中的作用
  • QLoRA框架介紹及其優勢
  • QLoRA與傳統微調方法的關鍵差異

Large Language Models (LLMs)基礎知識

  • LLM簡介及其架構
  • 大規模微調大型模型的挑戰
  • 量化如何幫助克服LLM微調中的計算限制

為Fine-Tuning LLM實施QLoRA

  • 設置QLoRA框架和環境
  • 準備用於QLoRA微調的數據集
  • 使用Python和PyTorch/TensorFlow在LLM上實施QLoRA的逐步指南

使用QLoRA優化Fine-Tuning性能

  • 如何平衡模型準確性和量化性能
  • 在微調期間減少計算成本和內存使用的技術
  • 使用最低硬件需求進行微調的策略

評估微調模型

  • 如何評估微調模型的有效性
  • 語言模型的常見評估指標
  • 微調後優化模型性能並解決問題

部署和擴展微調模型

  • 將量化LLM部署到生產環境的最佳實踐
  • 擴展部署以處理實時請求
  • 用於模型部署和監控的工具和框架

實際Use Case和案例研究

  • 案例研究：為客戶支持和NLP任務微調LLM
  • 在醫療、金融和電子商務等行業中微調LLM的示例
  • 從實際部署QLoRA模型中學到的經驗教訓

總結與下一步

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