Fine-tune large language models with parameter-efficient methods. Covers full fine-tuning, LoRA, QLoRA, PEFT, adapter-based tuning, prefix tuning, prompt tuning, instruction tuning, RLHF (PPO, DPO), SFT (supervised fine-tuning), HuggingFace TRL and PEFT libraries, Unsloth, Axolotl, dataset preparation (Alpaca, ShareGPT, chat formats), training data quality, evaluation during fine-tuning, hyperparameter selection, memory optimization, multi-GPU fine-tuning, merging adapters, and deploying fine-tuned models. Use when fine-tuning LLMs, creating custom models, or implementing RLHF/DPO alignment.
