GRU

Code2LoRA: Hypernetwork generates repository-specific LoRA adapters for code models with zero token overhead

Code2LoRA is a hypernetwork framework that generates repository-specific LoRA adapters for code language models, eliminating the inference-time token overhead of RAG or long-context injection. It supports both static repository snapshots and evolving codebases via a GRU-backed adapter updated per code diff. The authors introduce RepoPeftBench, a new benchmark of 604 Python repositories with static and evolution tracks, on which Code2LoRA-Static matches per-repository LoRA fine-tuning upper bounds and Code2LoRA-Evo outperforms a shared LoRA by 5.2 percentage points.