LoRA Fine-Tuning — Configure & Run Your First Training Job

I will configure and run a LoRA fine-tuning job for your model and dataset — covering Hugging Face PEFT library setup, LoRA configuration (rank, alpha, dropout, target modules), training script implementation, training monitoring, and delivery of the trained LoRA adapter weights. LoRA fine-tuning run without proper target module selection (which attention and MLP layers to adapt), rank calibration for your task complexity, and learning rate scheduling for LoRA's lower effective learning rate produces adapters that either underfit (rank too low, learning rate too conservative) or overfit (rank too high, learning rate too aggressive) without delivering the quality improvement the approach is capable of.

The setup covers PEFT library configuration, LoRA hyperparameter configuration, training script with validation monitoring, training curve analysis, adapter weight delivery, and a training report with loss curves and evaluation metrics.