A hands-on guide to instruction-tuning Llama 2 using QLoRA and the TRL SFTTrainer. Covers defining a use case and prompt template (Alpaca format), creating an instruction dataset using the Databricks Dolly-15k dataset, fine-tuning the base Llama 2 model with 4-bit quantization and LoRA adapters, and running inference with the trained model. Also covers Flash Attention integration, which reduced training time by 1.5x and cost by 30% on an AWS g5.2xlarge instance. The end goal is generating synthetic instruction datasets to personalize LLMs for tasks like email writing.
