Text-to-LoRA: Instant Transformer Adaption

While Foundation Models provide a general tool for rapid content creation, they regularly require task-specific adaptation. Traditionally, this exercise involves careful curation of datasets and repeated fine-tuning of the underlying model. Fine-tuning techniques enable practitioners to adapt foundation models for many new applications but require expensive and lengthy training while being notably sensitive to hyper-parameter choices. To overcome these limitations, we introduce Text-to-LoRA (T2L), a model capable of adapting Large Language Models on the fly solely based on a natural language description of the target task. T2L is a hypernetwork trained to construct LoRAs in a single inexpensive forward pass. After training T2L on a suite of 9 pre-trained LoRA adapters (GSM8K, Arc, etc.), we show that the ad-hoc reconstructed LoRA instances match the performance of task-specific adapters across the corresponding test sets. Furthermore, T2L can compress hundreds of LoRA instances and zero-shot generalize to entirely unseen tasks. This approach provides a significant step towards democratizing the specialization of foundation models and enables language-based adaptation with minimal compute requirements. Our code is available at this https URL

@article{charakorn2025_2506.06105,
  title={ Text-to-LoRA: Instant Transformer Adaption },
  author={ Rujikorn Charakorn and Edoardo Cetin and Yujin Tang and Robert Tjarko Lange },
  journal={arXiv preprint arXiv:2506.06105},
  year={ 2025 }
}