Principle:Axolotl ai cloud Axolotl LoRA Adapter Injection

Knowledge Sources	LoRA: Low-Rank Adaptation QLoRA PEFT Library Axolotl
Domains	Parameter_Efficient_Finetuning, Model_Architecture, Memory_Optimization
Last Updated	2026-02-06 23:00 GMT

Overview

A parameter-efficient fine-tuning technique that injects trainable low-rank decomposition matrices alongside frozen pre-trained model weights.

Description

LoRA (Low-Rank Adaptation) injects small, trainable matrices into specific layers of a pre-trained model while keeping the original weights frozen. Instead of fine-tuning all model parameters (which for a 7B model means 7 billion trainable parameters), LoRA adds pairs of low-rank matrices (A and B) to targeted layers, reducing trainable parameters to typically 0.1-1% of the original model.

The key insight is that weight updates during fine-tuning have a low intrinsic rank. By decomposing the update matrix into two smaller matrices (rank decomposition), LoRA achieves comparable quality to full fine-tuning with dramatically fewer trainable parameters and lower memory requirements.

In Axolotl, LoRA injection is handled by the load_lora function which creates a LoraConfig from the YAML configuration and wraps the model using HuggingFace PEFT's get_peft_model.

Usage

Use LoRA adapter injection when:

• Fine-tuning large models with limited GPU memory
• Using QLoRA (combined with 4-bit quantization)
• Training task-specific adapters that can be swapped at inference
• Requiring multiple specialized models from a single base model

Theoretical Basis

For a pre-trained weight matrix $W_0\in {\mathbb{ℝ}}^{d\times k}$, LoRA adds a low-rank update:

$W=W_0+\mathrm{\Delta }W=W_0+BA$

Where $B\in {\mathbb{ℝ}}^{d\times r}$ and $A\in {\mathbb{ℝ}}^{r\times k}$, with rank $r\ll \min (d,k)$.

Key parameters:

• Rank (r): Controls adapter capacity. Typical values: 8-64
• Alpha (α): Scaling factor applied as $\frac{\alpha }{r}$. Controls update magnitude
• Target modules: Which layers receive LoRA injection (attention, MLP, etc.)
• Dropout: Applied to LoRA layers for regularization

Forward pass:

# Pseudo-code for LoRA forward pass
h = W_0 @ x + (B @ A) @ x * (alpha / r)
# B initialized to zero, A initialized randomly
# During training: W_0 frozen, only A and B updated

Memory savings:

• Full fine-tuning: $d\times k$ trainable parameters per layer
• LoRA: $(d+k)\times r$ trainable parameters per layer
• For d=k=4096, r=16: 98.4% parameter reduction

Related Pages

Implemented By

• Implementation:Axolotl_ai_cloud_Axolotl_Load_Lora