Fine-Tuning Transformers and LLMs: From BERT to LLaMA

Modern natural language processing is driven by Transformer models, but understanding how to adapt these massive models to your own custom data can feel overwhelming. This text-based course demystifies the architecture and practical application of Large Language Models (LLMs) without requiring a background in advanced machine learning. You will transition from understanding basic Transformer concepts to confidently fine-tuning and optimizing models like BERT, Phi-2, and LLaMA. Through clear written explanations and comprehensive code walkthroughs, you will learn how to prepare custom datasets, run training pipelines, and compress models for real-world deployment. What you'll learn: - Understand the foundational architecture of Transformers, including self-attention, encoders, and decoders. - Configure and load pre-trained models and datasets using the Hugging Face library. - Fine-tune BERT variants for custom text classification tasks using structured code walkthroughs. - Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA to adapt large models with minimal compute. - Implement knowledge distillation to compress larger models into lightweight, fast alternatives like DistilBERT. - Evaluate model performance and text generation quality using standard modern NLP metrics. The course begins with essential terminology, architectural foundations, and Hugging Face basics. You will then progress through structured text lessons that guide you through practical fine-tuning workflows, optimization strategies, and model compression techniques. This course is designed for aspiring NLP developers, software engineers, and tech enthusiasts who want a solid, beginner-friendly introduction to LLM customization. No prior deep learning experience is required, though basic Python familiarity is helpful. Start reading today to unlock the potential of custom language models for your projects.