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arXiv · 2505.22296

360-LLaMA-Factory: Plug & Play Sequence Parallelism for Long Post-Training

Haosheng Zou·Xiaowei Lv·Shousheng Jia·Lin Li·Xiaochun Gong·Xiangzheng Zhang
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Abstract

Adding sequence parallelism into LLaMA-Factory, we open-sourced 360-LLaMA-Factory at https://github.com/Qihoo360/360-LLaMA-Factory. 360-LLaMA-Factory has received wide recognition and used in models such as Light-R1 arXiv:2503.10460, TinyR1 arXiv:2503.04872, Kaggle AIMO math models and also in large companies' training frameworks. This technical report delves deeper into the different sequence parallel modes behind 360-LLaMA-Factory and discusses our implementation insights.

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Haosheng Zou, Xiaowei Lv, Shousheng Jia, Lin Li, Xiaochun Gong, Xiangzheng Zhang. 2025-05-28. 360-LLaMA-Factory: Plug & Play Sequence Parallelism for Long Post-Training. https://arxiv.org/abs/2505.22296

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