Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution
arXiv:2505.24037v3 Announce Type: replace Abstract: Sparse large language models (LLMs) offer an attractive direction toward efficient deployment, but adapting them to downstream tasks remains challenging. The central difficulty is to enable effective task adaptation without sacrificing the efficiency advantages of sparsity. Existing fine-tuning methods are not well-suited to this setting, as they either introduce additional dense parameters or assume a fixed sparse topology, limiting their compatibility with sparse LLMs. In this paper, we propose Sparsity Evolution Fine-Tuning (SEFT), a fine-
The increasing scale of LLMs and the imperative for efficient deployment in diverse applications are driving the need for more effective sparse model adaptation techniques.
Efficient fine-tuning methods for sparse LLMs significantly reduce the computational and energy costs associated with powerful AI models, broadening their accessibility and application across industries.
The proposed Sparsity Evolution Fine-Tuning (SEFT) offers a more effective approach to adapting sparse LLMs, potentially accelerating their practical deployment without sacrificing efficiency benefits.
- · AI developers
- · Cloud providers
- · Edge AI companies
- · Enterprises adopting LLMs
- · Inefficient dense model producers
Wider adoption of computationally efficient large language models across various platforms.
Reduced infrastructure costs for deploying and maintaining advanced AI capabilities.
Democratization of sophisticated AI tools, leading to an increase in AI-powered applications and services.
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Read at arXiv cs.AI