Principle:Microsoft LoRA Package Installation
| Knowledge Sources | |
|---|---|
| Domains | Setup, Package_Management |
| Last Updated | 2026-02-10 05:00 GMT |
Overview
Principle of installing and configuring low-rank adaptation libraries as dependencies for parameter-efficient fine-tuning workflows.
Description
Before applying LoRA to any model, the loralib package must be installed into the Python environment. This package provides drop-in replacement layers (Linear, Embedding, MergedLinear, Conv2d) that augment standard PyTorch modules with low-rank trainable parameters. The installation step ensures all LoRA primitives are available for model modification.
Usage
Use this principle at the very start of any LoRA workflow. The loralib package is lightweight (3 source files, ~364 lines) and has only PyTorch as a runtime dependency. Install it before modifying any model architecture.
Theoretical Basis
LoRA operates on the principle that weight updates during fine-tuning have a low intrinsic rank. Instead of updating the full weight matrix W directly, LoRA decomposes the update as:
where B and A are low-rank matrices with rank r much smaller than the dimensions of W. The loralib package provides the layer implementations that realize this decomposition.