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Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
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ModuLoRA: モジュール式量子化器との統合による消費者GPUの2ビットLLMの微調整
Junjie Yin1, Jiahao Dong2, Yingheng Wang3
1Department of Computer Science, Johns Hopkins University.
まとめ
単一のGPUで2〜4ビット精度で大型言語モデル (LLM) の微調整のためのメモリ効率の良いアルゴリズムであるModuLoRAを紹介します. この方法は,高度な低精度の微調整を可能にし,メモリ使用量を減らすことで競争力のある性能を達成します.
科学分野:
- 人工知能
- 機械学習
- 自然言語処理
背景:
- 大型言語モデル (LLM) は,微調整のためにかなりの計算リソースを必要とします.
- 既存の微調整方法はしばしば高級ハードウェアを必要とし,アクセシビリティを制限します.
- 低精度量子化はメモリフットプリントを 減らすための道を示しますが 微調整は困難です
研究 の 目的:
- LLM のためのメモリ効率のよい微調整アルゴリズムを開発する.
- 消費者向けGPUで65Bパラメータを持つLLMの微調整を可能にします.
- 柔軟な微調整のための低ランク適応を任意の重量定量器に統合する.
主な方法:
- 提案されたModuLoRA (モジュラー・ローランク・アダプテーション) は,新しい微調整アプローチです.
- 低精度重量物質化のための量子化アグノスティックバックパスを実装しました.
- 最先端の2ビットQuIP#と3ビットOPTQの量子化方法
主要な成果:
- 単一の24GBGPUで2/3/4ビットの精度を使用して65BのパラメータでLLMをうまく調整しました.
- テキストの分類,自然言語の推論,および指示に従うタスクの競争力のあるパフォーマンスを達成しました.
- 最先端のROUGEのスコアを上回り,既存の4ビットと8ビットメソッドを上回った.
結論:
- ModuLoRAは,LLMのフィニッシュチューニングに必要なメモリを大幅に削減します.
- この方法は,高精度のLLM (2ビット,3ビット) の微調整を初めて可能にします.
- より広範なアクセシビリティのために,LLMToolsライブラリを通じてModuLoRAと低精度モデルをリリースしました.
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