Principle:LLMBook zh LLMBook zh github io PEFT LoRA Configuration

Knowledge Sources	LoRA: Low-Rank Adaptation of Large Language Models PEFT Library LLMBook-zh
Domains	Deep_Learning, Parameter_Efficient_Finetuning
Last Updated	2026-02-08 00:00 GMT

Overview

The configuration and injection pattern that wraps a pre-trained model with LoRA adapters using the PEFT library.

Description

PEFT LoRA Configuration is the process of defining LoRA hyperparameters (rank, alpha, dropout, target modules) and applying them to a pre-trained model. The PEFT library automates the injection of LoRA layers into specified modules, freezes the base model weights, and wraps everything into a PeftModel that can be trained with standard HuggingFace Trainer.

Usage

Use this when you want to apply LoRA fine-tuning using the PEFT library instead of implementing LoRA layers from scratch. Configure LoraConfig with task type, rank, alpha, and dropout, then apply via get_peft_model.

Theoretical Basis

LoRA configuration involves:

1. Define hyperparameters: rank (r), scaling factor (lora_alpha), dropout rate, task type.
2. Create LoraConfig: Encapsulates all LoRA settings.
3. Apply to model: get_peft_model() injects LoRA layers into the target modules and freezes base weights.

The scaling factor determines the effective LoRA contribution: output = lora_alpha/r × BA × x.

Related Pages

Implemented By

• Implementation:LLMBook_zh_LLMBook_zh_github_io_LoraConfig_Get_Peft_Model

Uses Heuristic

• Heuristic:LLMBook_zh_LLMBook_zh_github_io_LoRA_Initialization_Strategy