Fine-Tuning with LoRA / PEFT

Parameter-Efficient Fine-Tuning for Large Language Models

AI Training Session | Herman Alany | 2026

Overview

LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) enable organizations to fine-tune large language models without retraining entire models. This drastically reduces computational cost, memory usage, and infrastructure requirements while maintaining high performance.

Key Benefits

  • ~90% fewer trainable parameters
  • ≈1:1 performance compared to full fine-tuning
  • Low-rank adapters (r ≈ 8) for efficient updates

What You Will Learn

1. The Problem Space

Why traditional fine-tuning fails for most organizations due to cost and scale.

2. What is LoRA?

Low-rank matrix decomposition and how it reduces training cost.

3. PEFT Architecture

Frozen base model with lightweight LoRA layers.

4. Training Pipeline

Forward pass, loss, backpropagation — only adapters update.

5. HuggingFace PEFT

Using LoRAConfig and get_peft_model for implementation.

6. Live Demo

Transforming generic AI into domain-specific intelligence.

Model Architecture

Base Model

Original pretrained weights remain unchanged.

FROZEN

LoRA Adapters

Small trainable matrices injected into the model.

TRAINABLE

Fine-Tuned Model

Domain-specific output with minimal compute cost.

OUTPUT

Tech Stack

Python PyTorch HuggingFace Transformers PEFT Library GPT / LLaMA