Fine-Tuning and Customizing Open-Source LLMs: LoRA, QLoRA and Self-Hosting

A premium, complete and advanced course on fine-tuning and customizing open-source large language models, updated for 2026. You will learn when to fine-tune versus RAG versus prompting, the anatomy of modern open models (Llama, Mistral, Qwen, DeepSeek, Gemma 4) and their licenses, how to build high-quality and legally sound datasets, parameter-efficient fine-tuning with LoRA and QLoRA (rank, alpha, target modules, quantization with bitsandbytes), the full toolchain (Hugging Face transformers, PEFT, TRL, Unsloth, Axolotl), instruction tuning, chat templates and preference optimization (DPO), evaluation, inference quantization (GGUF, GPTQ, AWQ), and self-hosting with Ollama, vLLM and TGI. Includes cost and hardware planning, production deployment, real-world case studies, a strong focus on legal and ethical fine-tuning data, and a comprehensive final assessment.