Fine-tuning Text Models with PEFT

The Fine-tuning Text Models with PEFT course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course introduces learners to parameter-efficient fine-tuning methods that enable large language model adaptation on limited hardware. Learners start with foundational concepts of PEFT and Low-Rank Adaptation (LoRA), understanding their advantages over full fine-tuning in terms of memory, cost, and flexibility. The course then dives into implementing QLoRA, combining quantization with LoRA for high-performance fine-tuning on consumer GPUs. Learners practice setting up training environments, preparing datasets, optimizing hyperparameters, and managing checkpoints. The final module emphasizes evaluation, using metrics such as perplexity, BLEU, ROUGE, and BERTScore to measure improvements. By the end, learners will have implemented a fine-tuning pipeline and produced a domain-adapted LLM with performance documentation.