Fine-Tune Your Own LLM: LoRA, QLoRA & PEFT

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This intermediate-level course guides you through fine-tuning open-source large language models on consumer GPUs using parameter-efficient techniques, all anchored around building a production-style SaaS customer support agent. You’ll first map out the full LLM training pipeline—pretraining, supervised fine-tuning, and alignment—and see exactly where PEFT methods fit when you’re constrained by memory and cost. You’ll compare full fine-tuning with approaches such as LoRA, QLoRA, adapters, prefix and prompt tuning, and then dive into the mathematical intuition behind Low-Rank Adaptation, understanding how rank, alpha, and target module choice influence parameter count and memory usage. You’ll get hands-on with preparing real-world customer support data (tickets, chats, FAQs), turning it into high-quality instruction–completion pairs and formatting them using common schemas like Alpaca, ChatML, and ShareGPT-style formats, with programmatic validation for training readiness. Using Hugging Face Transformers and PEFT, you will implement LoRA and QLoRA to fine-tune 7B–8B parameter open models on a single 24GB GPU, configuring 4-bit NF4 quantization, tracking memory usage, and managing training stability. You’ll learn to debug and iterate using logs, loss curves, and failure case analysis (hallucinations, refusals, overfitting), and evaluate your models with automated metrics (perplexity, ROUGE, BERTScore) while recognizing their limitations for dialogue quality. Finally, you’ll design preference datasets that encode brand tone, helpfulness, and safety, and apply alignment methods such as DPO and ORPO to steer the behavior of your support agent. You’ll explore advanced PEFT variants like DoRA, rsLoRA, and adapter composition to study quality–efficiency trade-offs. The course closes with practical packaging and deployment: merging adapters when appropriate, quantizing for efficient inference, and deploying your agent using modern inference stacks like vLLM and Hugging Face Inference Endpoints, with basic monitoring to track latency and response quality in a SaaS environment. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

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