Training and Fine-Tuning LLMs Course

Learn the fundamentals of training and fine-tuning large language models through this comprehensive course covering essential concepts from data preparation to deployment. Begin with foundational LLM knowledge and explore the NeurIPS LLM Efficiency Challenge to understand current industry standards. Master LLM evaluation techniques including benchmarks, metrics, and assessment challenges that researchers face when measuring model performance. Dive deep into data requirements for LLMs, examining data scaling laws, dataset construction methodologies, and efficient data storage solutions. Explore the critical differences between LLM pretraining and fine-tuning approaches to understand when and how to apply each technique. Gain practical insights into hardware requirements, including GPU memory calculations for training different model sizes. Study tokenization processes, optimization strategies, and hyperparameter tuning techniques essential for successful model training. Learn about quantization methods to reduce model size while maintaining performance, and understand scaling laws that govern LLM behavior across different model sizes and datasets. Benefit from expert insights and real-world examples throughout the curriculum to bridge the gap between theoretical knowledge and practical implementation.