Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)
This course delivers a comprehensive, engineer-friendly blueprint for fine-tuning large language models with an emphasis on performance, scalability, and cost efficiency. Students will move from foundational concepts to advanced, production-ready techniques that minimize GPU memory, bandwidth, and financial overhead while preserving or enhancing model effectiveness. The curriculum blends theory with hands-on labs, visuals, and short quizzes to reinforce concepts such as parameter-efficient fine-tuning (PEFT), quantization, pruning, distributed training, data curation, monitoring, and deployment. Each module is designed to be actionable in real-world settings—from researchers prototyping ideas to ML engineers deploying models in production. Bonus labs use Hugging Face PEFT tooling and QLoRA on representative models (e.g., Llama, Mistral) to ensure practical familiarity with current best practices. By the end, learners will be able to architect, implement, validate, and operate cost-effective fine-tuning pipelines that scale to industry-grade models while maintaining rigorous performance standards.
