Course Code: finetuninglora
Duration: 14 hours
Prerequisites:
  • 對機器學習概念的基本理解
  • 熟悉 Python 程式設計
  • 使用 TensorFlow 或 PyTorch 等深度學習框架的經驗

觀眾

  • 開發人員
  • AI 從業者
Overview:

Low-Rank Adaptation （LoRA） 是一種尖端技術，通過減少傳統方法的計算和記憶體要求來有效地微調大規模模型。本課程提供有關使用 LoRA 為特定任務調整預訓練模型的實踐指導，使其成為資源受限環境的理想選擇。

這種由講師指導的現場培訓（在線或現場）面向希望在不需要大量計算資源的情況下為大型模型實施微調策略的中級開發人員和 AI 從業者。

在本次培訓結束時，參與者將能夠：

  • 瞭解低秩適應 （LoRA） 的原理。
  • 實施LoRA以高效微調大型模型。
  • 針對資源受限的環境優化微調。
  • 評估和部署LoRA調優模型以用於實際應用。

課程形式

  • 互動講座和討論。
  • 大量的練習和練習。
  • 在即時實驗室環境中動手實施。

課程自定義選項

  • 要申請本課程的定製培訓，請聯繫我們進行安排。
Course Outline:

低秩適應 （LoRA） 簡介

  • 什麼是LoRA？
  • LoRA 在高效微調方面的優勢
  • 與傳統微調方法的比較

瞭解微調挑戰

  • 傳統微調的局限性
  • 計算和記憶體約束
  • 為什麼LoRA是有效的替代方案

設置環境

  • 安裝 Python 和所需的庫
  • 設置 Hugging Face Transformer 和 PyTorch
  • 探索LoRA相容模型

實施LoRA

  • LoRA 方法概述
  • 使用LoRA調整預訓練模型
  • 針對特定任務進行微調（例如，文字分類、摘要）

使用LoRA優化微調

  • LoRA 的超參數優化
  • 評估模型性能
  • 最大限度地減少資源消耗

動手實驗

  • 使用LoRA微調BERT以進行文字分類
  • 將 LoRA 應用於 T5 以執行摘要任務
  • 探索獨特任務的自定義 LoRA 配置

部署LoRA調優模型

  • 匯出和保存LoRA優化模型
  • 將LoRA模型整合到應用程式中
  • 在生產環境中部署模型

LoRA 中的高級技術

  • 將LoRA與其他優化方法相結合
  • 為更大的模型和數據集擴展LoRA
  • 使用LoRA探索多模式應用

挑戰和最佳實踐

  • 避免使用LoRA進行過擬合
  • 確保實驗的可重複性
  • 故障排除和調試策略

高效微調的未來趨勢

  • LoRA 和相關方法的新興創新
  • LoRA 在實際 AI 中的應用
  • 高效微調對 AI 開發的影響

總結和後續步驟

Sites Published:

United Arab Emirates - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Qatar - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Egypt - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Saudi Arabia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

South Africa - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Brasil - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Canada - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

中国 - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

香港 - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

澳門 - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

台灣 - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

USA - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Österreich - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Schweiz - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Deutschland - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Czech Republic - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Denmark - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Estonia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Finland - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Greece - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Italia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Lithuania - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Norway - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Portugal - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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India - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Chile - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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United Kingdom - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

South Korea - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Pakistan - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Bulgaria - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Bolivia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Indonesia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Morocco - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Tunisia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

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Serbia - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Bhutan - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Nepal - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Uzbekistan - Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)