LLM Fine-tuning: Techniques for Adapting Language Models

Part 13 of a full LLMOps crash course covering LLM fine-tuning techniques. Topics include parameter-efficient training methods like LoRA and QLoRA, and alignment techniques such as RLHF, DPO, and GRPO, with hands-on code examples. The broader course context explains why LLMOps differs from traditional MLOps, covering cost structures, reliability, monitoring for hallucinations, and prompt brittleness in production LLM systems.