Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models - Code World

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

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Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Fine-tuning: In-depth analysis of the application of P-tuning v2 on large models

Practical Application of Large Models 16-A fine-tuning technique for large pre-trained models: Practical application of the Adapter-Tuning method, detailed introduction to the principle

Practical application of large models 10 - Detailed explanation of large model domain knowledge and parameter efficient fine-tuning (PEFT) technology, and use PEFT to train your own large models

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

Large language model fine-tuning and PEFT efficient fine-tuning

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

[Notas de leitura de papel 75] P-Tuning v2

P-Tuning v2: otimização imediata igual ao desempenho de ajuste fino

Na visão geral do ajuste fino eficiente de modelos grandes: Adaptador Tuning, AdaMix, PET, Prefix-Tuning, Prompt Tuning, P-tuning, P-tuning v2

Summary of fine-tuning methods for large models: LoRA, Adapter, Prefix-tuning, P-tuning, Prompt-tuning

Prática de ajuste fino ChatGLM2-6B baseada em P-Tuning v2 | Equipe técnica JD Cloud

A Survey of Fine-tuning Methods for Large Models

Summary of fine-tuning techniques for large models

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How to reduce model cost? Platypus: Fast, cheap and powerful LLM that beats the competition with only one GPU and 5 hours of LLaMA2 fine-tuning

[Instruction fine-tuning of LLM series] Long story short, "Prompt" for instruction fine-tuning of large models

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

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