Fine Tuning Expert

This is what you reach for when you need to fine-tune an LLM without burning through GPU budget or guessing at hyperparameters. It walks you through the full pipeline from dataset validation to deployment, with working examples for LoRA and QLoRA using Hugging Face PEFT. The checkpoint-driven workflow is smart: it forces you to validate your data before training and actually measure metrics before deploying. Includes concrete configs (rank 16, alpha 32, cosine scheduling) and the merge-and-unload pattern for production. The constraint list calling out common mistakes like skipping warmup or merging incompatible adapters shows this comes from someone who's debugged those failures. Covers instruction tuning, RLHF, and DPO if you need them.