P-Tuning v2 paper overview - Code World

P-Tuning v2 paper overview

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Origin blog.csdn.net/qq128252/article/details/134769856

P-Tuning v2 paper overview

P-tuning paper overview

[Paper Reading Notes 75] P-Tuning v2

Paper Notes P-Tuning v2 Suggestive optimization equal to fine-tuning performance

On the overview of efficient fine-tuning of large models: Adapter Tuning, AdaMix, PET, Prefix-Tuning, Prompt Tuning, P-tuning, P-tuning v2

P-Tuning v2: Prompt optimization equal to fine-tuning performance

[Paper Reading Notes 76] GPT Understands, Too (P-tuning)

[Paper Reading Notes 75] P-Tuning v2

ChatGLM2-6B fine-tuning practice based on P-Tuning v2 | JD Cloud technical team

Popular understanding of major fine-tuning methods for large models: from Prefix Tuning, P-Tuning V1/V2 to LoRA, QLoRA

Uniswap V2 white paper translation

Take you hand in hand to realize the P-Tuning fine-tuning of ChatGLM2-6B

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[Примечания к тезисам 75] P-Tuning v2

Paper: Deformable ConvNets v2 read notes

Large model-DeltaTuning-incremental 03: Prompt-Tuning (P-tuning v1) [Only add a trainable parameter layer to the input layer]

【LLM】Analysis of chatglm's sft+p-tuning v2

К обзору эффективной тонкой настройки больших моделей: Adapter Tuning, AdaMix, PET, Prefix-Tuning, Prompt Tuning, P-tuning, P-tuning v2

[Interpretation of the paper] ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoder

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ChatGLM2 of LLMs: Introduction and usage of ChatGLM-Finetuning (based on DeepSpeed) (four fine-tuning methods (Freeze method/Lora method/P-Tuning method/full parameters) + single-card/multi-card training

Overview of the principles of efficient fine-tuning technology for large model parameters (2) - BitFit, Prefix Tuning, Prompt Tuning

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Популярное понимание основных методов тонкой настройки для больших моделей: от Prefix Tuning, P-Tuning V1/V2 до LoRA, QLoRA

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