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docs/en/api/peft/MAIN_CLASSES/PEFT_TYPE.md

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# PEFT (Prompt Engineering with Fine-Tuning)
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MindNLP's PEFT (Prompt Engineering with Fine-Tuning) is a methodology for fine-tuning natural language models using prompts. It allows users to tailor their models for specific tasks or domains by providing customized prompts, enabling better performance on downstream tasks.
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## Introduction
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PEFT leverages the power of prompts, which are short natural language descriptions of the task, to guide the model's understanding and behavior. By fine-tuning with prompts, users can steer the model's attention and reasoning towards relevant information, improving its performance on targeted tasks.
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## Supported PEFT Algorithms
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| Algorithm | Description |
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|------------------|--------------------------------------------------------------|
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| [AdaLoRA](./tuners/adalora.md) | Adaptable Prompting with Learned Rationales (AdaLoRA) |
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| [Adaption_Prompt](./tuners/adaption_prompt.md) | Adaptation Prompting |
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| [IA3](./tuners/ia3.md) | Iterative Alignments for Adaptable Alignment and Prompting (IA3) |
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| [LoKr](./tuners/lokr.md) | Large-scale k-shot Knowledge Representation (LoKr) |
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| [LoRA](./tuners/lora.md) | Learnable Redirection of Attention (LoRA) |
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| [Prompt Tuning](./tuners/prompt_tuning.md) | Prompt Tuning fine-tunes models by optimizing the prompts used during fine-tuning. |
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Each algorithm offers unique approaches to prompt engineering and fine-tuning, allowing users to adapt models to diverse tasks and domains effectively.
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:::mindnlp.peft.tuners.adalora.config.AdaLoraConfig
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::: mindnlp.peft.tuners.adalora.model.AdaLoraModel
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:::mindnlp.peft.tuners.adalora.model.AdaLoraModel
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:::mindnlp.peft.tuners.adaption_prompt.config.AdaptionPromptConfig
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::: mindnlp.peft.tuners.adaption_prompt.model.AdaptionPromptModel
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:::mindnlp.peft.tuners.adaption_prompt.model.AdaptionPromptModel
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:::mindnlp.peft.tuners.ia3.config
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:::mindnlp.peft.tuners.ia3.model

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