Generative AI Series

Fine Tuning LLM: Parameter Efficient Fine Tuning (PEFT) — LoRA & QLoRA — Part 1

Parameter Efficient Fine Tuning — LoRA, QLoRA — Concepts

A B Vijay Kumar
8 min readAug 10, 2023


In this blog, we will understand the idea behind Parameter Efficient Fine Tuning (PEFT), and explore LoRA and QLoRA, Two of the most important PEFT methods. We will understnad how PEFT can be used to fine tune the model for domain specific tasks, at the lowest cost and minimal infrastrcuture.


In the ever-evolving world of AI and Natural Language Processing (NLP), Large Language Models and Generative AI have become powerful tools for various applications. Achieving the desired results from these models involves different approaches that can be broadly classified into three categories: Prompt Engineering, Fine-Tuning, and Creating a new model. As we progress from one level to another, the requirements in terms of resources and costs increase significantly.

In this blog post, we’ll explore these approaches and focus on an efficient technique known as Parameter Efficient Fine-Tuning (PEFT) that allows us to fine-tune models with minimal infrastrcture while maintaining high performance.

Prompt Engineering with Existing Models

At the basic level, achieving expected outcomes from Large Language Models involves careful prompt engineering. This process involves crafting suitable prompts and inputs to elicit the desired responses from the model. Prompt Engineering is an essential technique for various use cases, especially when general responses suffice.

Creating a New Model

At the highest level, Creating a new model involves training a model from scratch, specifically tailored for a particular task or domain. This approach provides the highest level of customization, but it demands substantial computational power, extensive data, and time.

Fine Tuning Existing Models

When dealing with domain-specific use cases that require model adaptations, Fine Tuning becomes essential. Fine-Tuning allows us to leverage existing pre-trained foundation models and adapt them to specific tasks or domains. By training the model on domain-specific data, we can tailor it to perform well on targeted tasks.



A B Vijay Kumar

IBM Fellow, Master Inventor, Mobile, RPi & Cloud Architect & Full-Stack Programmer