Principle:Axolotl ai cloud Axolotl LoRA Merging

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

Overview

A post-training operation that merges LoRA adapter weights back into the base model to produce a standalone model without adapter overhead.

Description

LoRA Merging combines the trained low-rank adapter weights with the frozen base model weights to produce a single, merged model. During training, the forward pass computes $W_{0} x + B A x$ where $W_{0}$ is frozen and $B A$ is the trained adapter. Merging computes $W_{m e r g e d} = W_{0} + B A$ once, eliminating the runtime overhead of the separate adapter computation.

This is essential for deployment: a merged model loads and runs like any standard model without requiring the PEFT library. It also enables further quantization (GGUF, GPTQ, AWQ) for optimized inference.

Usage

Use LoRA merging when:

Deploying a fine-tuned model to production without PEFT dependency
Converting to optimized inference formats (GGUF, GPTQ, AWQ)
Sharing a standalone model on HuggingFace Hub
No longer needing the ability to swap adapters

Theoretical Basis

Merging is a simple linear algebra operation:

$W_{m e r g e d} = W_{0} + \frac{α}{r} \cdot B \cdot A$

Where $\frac{α}{r}$ is the LoRA scaling factor.

Properties:

Lossless: The merged model produces identical outputs to the adapter model
Irreversible: After merging, individual adapter weights cannot be recovered
One-time: Merging is done once post-training, not during inference

Related Pages

Implemented By

Implementation:Axolotl_ai_cloud_Axolotl_Do_Merge_Lora

Page Connections

Double-click a node to navigate. Hold to expand connections.

Principle

Implementation

Heuristic

Environment