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https://blog.philip-huang.tech/?page=peft-overview
語言模型(LM)技術已經實現一些重大突破,使得模型的規模更加龐大。然而,對大部份的人說,要微調如此巨大的模型所需的門檻太高。Parameter-efficient fine-tuning(PEFT)提供了一種新的訓練方法,即通過訓練一小組參數,使微調門檻降低,並且讓模型能夠適應和執行新的任務。
LM fine-tuning 演進
Full fine-tuning Transformer 架構模型剛推出時(BERT,GPT, etc.),普遍模型大小落在500M~700M左右,這時候高端的消費級顯卡可以負擔微調所需的硬體門檻。
In-Context learni
The text was updated successfully, but these errors were encountered:
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https://blog.philip-huang.tech/?page=peft-overview
- tags: peft overview LLM fine-tune LoRA Adapter - date: 2023/12/15語言模型(LM)技術已經實現一些重大突破,使得模型的規模更加龐大。然而,對大部份的人說,要微調如此巨大的模型所需的門檻太高。Parameter-efficient fine-tuning(PEFT)提供了一種新的訓練方法,即通過訓練一小組參數,使微調門檻降低,並且讓模型能夠適應和執行新的任務。
Full fine-tuning
Transformer 架構模型剛推出時(BERT,GPT, etc.),普遍模型大小落在500M~700M左右,這時候高端的消費級顯卡可以負擔微調所需的硬體門檻。
In-Context learni
The text was updated successfully, but these errors were encountered: