arXiv:2505.22934v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LMs) for individual tasks yields strong performance but is expensive for deployment and storage. Recent works explore model merging to combine multiple task-specific models into a single multi-task model without additional training. However, existing merging methods often fail for models fine-tuned with low-rank adaptation (LoRA), due to significant performance degradation. In this paper, we show that this issue arises from a previously overlooked interplay between model parameters and data distributio
The proliferation of LoRA fine-tuning for large language models has exposed significant challenges in combining these specialized models, leading to a focus on robust merging techniques.
Improved model merging techniques for LoRA will allow for more efficient deployment and management of specialized AI models, reducing computational and storage costs for AI providers and users.
The ability to effectively merge LoRA-tuned models could lead to more versatile and cost-effective multi-task AI systems, potentially accelerating the development of specialized AI agents.
- · AI developers
- · Cloud providers
- · Enterprises adopting AI
- · AI agent developers
- · Inefficient monolithic model deployment strategies
More efficient and scalable deployment of specialized AI models becomes possible.
This efficiency could enable the creation of more sophisticated and specialized AI agent architectures.
Reduced compute and storage costs might democratize access to advanced AI capabilities, fostering broader innovation.
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Read at arXiv cs.LG