LoRA Consultation — Parameter-Efficient Fine-Tuning Strategy

I will assess your fine-tuning requirement and deliver a written LoRA strategy consultation — covering whether LoRA is appropriate for your use case versus full fine-tuning, optimal rank and alpha configuration for your task type, which model layers to target for adaptation, and a recommended training configuration. LoRA's efficiency advantage — fine-tuning only a small fraction of model parameters while preserving most of the base model — makes it the default choice for most practical fine-tuning scenarios, but the rank and target layer choices materially affect quality; a consultation grounded in your task characteristics produces a configuration that delivers the quality needed without unnecessary rank overhead.

The consultation covers your task type assessment, LoRA vs. full fine-tuning recommendation, rank and alpha configuration for your task, target layer selection, training hyperparameter recommendations, and a training cost estimate.