Persian-Phi: 通过课程学习高效适应紧凑大语言模型
Persian-Phi: Efficient Cross-Lingual Adaptation of Compact LLMs via Curriculum Learning
AI总结：
Persian-Phi通过课程学习高效适应紧凑大语言模型，实现波斯语多语言能力的扩展。
摘要：
人工智能的普及目前受到训练大型语言模型（LLMs）以支持低资源语言所需的巨大计算成本的阻碍。本文提出了Persian-Phi，一个38亿参数的模型，挑战了认为稳健的多语言能力需要大规模模型或多语言基线的假设。我们展示了如何通过一种新颖且资源高效的课程学习流程，将微软Phi-3 Mini（原本是一个单语英语模型）有效地适应到波斯语。我们的方法采用一种独特的
英文摘要：
The democratization of AI is currently hindered by the immense computational costs required to train Large Language Models (LLMs) for low-resource languages. This paper presents Persian-Phi, a 3.8B parameter model that challenges the assumption that robust multilingual capabilities require massive model sizes or multilingual baselines. We demonstrate how Microsoft Phi-3 Mini -- originally a monolingual English model -- can be effectively adapted to Persian through a novel, resource-efficient curriculum learning pipeline. Our approach employs a unique "warm-up" stage using bilingual narratives (Tiny Stories) to align embeddings prior to heavy training, followed by continual pretraining and instruction tuning via Parameter-Efficient Fine-Tuning (PEFT). Despite its compact size, Persian-Phi achieves competitive results on Open Persian LLM Leaderboard in HuggingFace. Our findings provide a validated, scalable framework for extending the reach of state-of-the-art LLMs to underrepresented languages with minimal hardware resources. The Persian-Phi model is publicly available at https://huggingface.co/amirakhlaghiqqq/PersianPhi.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
