LoRA-Gen: Specializing Large Language Model via Online LoRA Generation

• URL: http://arxiv.org/abs/2506.11638v1
• Date: Fri, 13 Jun 2025 10:11:01 GMT
• Title: LoRA-Gen: Specializing Large Language Model via Online LoRA Generation
• Authors: Yicheng Xiao, Lin Song, Rui Yang, Cheng Cheng, Yixiao Ge, Xiu Li, Ying Shan,
• Abstract summary: We propose the LoRA-Gen framework to generate LoRA parameters for edge-side models based on task descriptions.<n>We merge the LoRA parameters into the edge-side model to achieve flexible specialization.<n>Our method facilitates knowledge transfer between models while significantly improving the inference efficiency of the specialized model.
• Score: 68.01864057372067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances have highlighted the benefits of scaling language models to enhance performance across a wide range of NLP tasks. However, these approaches still face limitations in effectiveness and efficiency when applied to domain-specific tasks, particularly for small edge-side models. We propose the LoRA-Gen framework, which utilizes a large cloud-side model to generate LoRA parameters for edge-side models based on task descriptions. By employing the reparameterization technique, we merge the LoRA parameters into the edge-side model to achieve flexible specialization. Our method facilitates knowledge transfer between models while significantly improving the inference efficiency of the specialized model by reducing the input context length. Without specialized training, LoRA-Gen outperforms conventional LoRA fine-tuning, which achieves competitive accuracy and a 2.1x speedup with TinyLLaMA-1.1B in reasoning tasks. Besides, our method delivers a compression ratio of 10.1x with Gemma-2B on intelligent agent tasks.

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