Fine-Tuning Mistral 7B with PEFT, QLoRA, and MLflow

Customizing large language models for specialized tasks is one of the most critical skills in modern artificial intelligence engineering. This text-based guide teaches you how to adapt the Mistral 7B architecture efficiently using modern parameter-efficient techniques and manage the entire lifecycle with standard MLOps tooling. You will understand foundational concepts before moving into structured text walkthroughs and runnable code examples. What you'll learn: Understand core concepts of large language model architectures and fine-tuning fundamentals; Apply Parameter-Efficient Fine-Tuning (PEFT) and LoRA techniques to reduce compute requirements; Configure 4-bit quantization with QLoRA for resource-efficient training on consumer-grade hardware; Track training parameters, metrics, and model artifacts systematically using MLflow; Evaluate fine-tuned model outputs against task-specific benchmarks; Deploy and manage fine-tuned model versions across practical MLOps workflows. The course begins with essential terminology, dataset preparation, and hardware optimization principles before guiding you through step-by-step written exercises and configuration setups. This course is designed for programmers, data enthusiasts, and machine learning beginners ready to expand their skills into modern LLM operations. Start reading today to build and track your own fine-tuned language models.