Foundations of Large Language Models: From Transformers to Fine-Tuning

Large Language Models are transforming how we build software, but understanding how they actually work under the hood is the key to building truly robust AI applications. This text-based course guides you through the mechanics of transformers, generative AI, and modern model optimization. You will transition from a curious learner to a practitioner capable of understanding, fine-tuning, and deploying modern language models. By studying foundational concepts, analyzing written code snippets, and exploring advanced optimization strategies, you will gain the confidence to work with models of all sizes. What you'll learn: - Understand the foundational architecture of Transformers, including encoders, decoders, and self-attention mechanisms. - Explore modern generative AI models such as GPT, BERT, T5, and Llama, and learn how they process text. - Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA to adapt models with minimal hardware resources. - Implement quantization strategies, including 4-bit and 8-bit precision, to optimize models for efficient deployment. - Configure distributed training workflows using tools like DeepSpeed and Fully Sharded Data Parallel (FSDP). - Master the basics of Retrieval-Augmented Generation (RAG) and vector databases to connect models to external data. The course starts with essential terminology and the evolution of natural language processing before moving into transformer mechanics, fine-tuning strategies, and advanced scaling techniques. Written explanations and clear code walkthroughs ensure you grasp both theory and practical implementation. This course is designed for beginners, software developers, and data enthusiasts looking to enter the world of generative AI. No prior machine learning experience is required to get started. Start reading today to unlock the potential of large language models and build your AI engineering toolkit.