PHLoRA: data-free Post-hoc Low-Rank Adapter extraction from full-rank checkpoint

Understand

We introduce PHLoRA (Pronounced "flora").

• (Post-hoc LoRA), a simple yet powerful method to extract low-rank adaptation adapters from full-rank fine-tuned models without requiring access to training data or gradients.
• By computing the low-rank decomposition of weight differences between a base model and its fine-tuned counterpart, our method reconstructs adapter modules that can be merged or dynamically routed at inference time via S-LoRA, or served in scalable, industry settings using platforms like NVIDIA NIM.
• This approach amortizes latency overhead across requests and yields substantial cost savings.