An efficient strategy for fine-tuning large language models

Abstract

These findings support a practical process for resource-constrained domain adaptation: use DSS to efficiently construct datasets, then select the fine-tuning modality based on available compute (full-precision when feasible; LoRA or QLoRA when memory-limited). The proposed workflow offers a general guide for efficiently fine-tuning LLMs for domain-specific tasks with limited data availability.

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