LoRA Rank Optimisation — Find the Minimum Rank for Quality

I will run a systematic LoRA rank optimisation study — training your model at multiple rank values, evaluating quality at each rank, and identifying the minimum rank that meets your quality target, minimising adapter size and inference overhead without sacrificing the performance you need. LoRA rank selection without systematic study defaults to arbitrary values (r=8 or r=16 are common defaults) that may be significantly over-parameterised for simple tasks or under-parameterised for complex ones; a rank study on your specific task and dataset identifies the quality-efficiency optimum empirically.

The study covers training runs at ranks 4, 8, 16, 32, and 64, quality evaluation at each rank, rank-quality trade-off curve analysis, minimum sufficient rank identification, adapter size comparison, and a recommendation with supporting data.