Fine-Tuning LLMs: LoRA, QLoRA, and PEFT in Practice
Learn to fine-tune large language models efficiently using LoRA, QLoRA, and PEFT. Practical intermediate course covering the full fine-tuning workflow from dataset to deployment.
Course Content
Fine-Tuning LLMs: LoRA, QLoRA, and PEFT in Practice
Fine-tuning used to require massive compute budgets. LoRA and QLoRA changed that — you can now fine-tune a 7B parameter model on a single consumer GPU. This course shows you exactly how.
The Fine-Tuning Revolution
Parameter-efficient fine-tuning (PEFT) techniques like LoRA and QLoRA have democratized LLM customization. Instead of updating all billions of parameters, these methods update a tiny fraction — achieving comparable results at a fraction of the compute cost. This course teaches you to use them professionally.
What You’ll Build
- Instruction-following assistant: Fine-tuned on a custom dataset to follow domain-specific instructions
- Code generation specialist: A coding assistant specialized to your team’s codebase patterns
- Production inference service: Quantized, merged model served with vLLM for low-latency inference
Hardware Requirements
Most lessons work on Google Colab (free tier). QLoRA lessons require a T4 GPU (free on Colab). The capstone project recommages an A100 (available via Colab Pro or AWS).
📋 Prerequisites
- Python programming (intermediate level)
- Basic understanding of neural networks and transformers
- Access to a GPU (Google Colab free tier works for most lessons)
🎯 What You'll Learn
- Choose between fine-tuning, RAG, and prompt engineering for any use case
- Prepare high-quality datasets for instruction fine-tuning
- Apply LoRA and QLoRA to fine-tune 7B+ models efficiently
- Evaluate fine-tuned models with rigorous benchmarks
- Deploy fine-tuned models to production inference APIs