We present Sailor, a family of open language models ranging from 0.5B to 7B
parameters, tailored for South-East Asian (SEA) languages. These models are
continually pre-trained from Qwen1.5, a great language model for multilingual
use cases. From Qwen1.5, Sailor models accept 200B to 400B tokens, primarily
covering the languages of English, Chinese, Vietnamese, Thai, Indonesian,
Malay, and Lao. The training leverages several techniques, including BPE
dropout for improving the model robustness, aggressive data cleaning and
deduplication, and small proxy models to optimize data mixture. Experimental
results on four typical tasks indicate that Sailor models demonstrate strong
performance across different benchmarks, including commonsense reasoning,
question answering, reading comprehension and examination. Embracing the
open-source spirit, we share our insights through this report to spark a wider
interest in developing large language models for multilingual use cases.

Sailor 是一个面向东南亚语言的一系列开放语言模型，包括从 0.5B 到 7B 参数的模型。通过从 Qwen1.5 继续预训练，Sailor 模型接受 200B 到 400B 个 token，主要涵盖英语、中文、越南语、泰语、印度尼西亚语、马来语和老挝语。实验结果显示，Sailor 模型在常识推理、问答、阅读理解和考试等多个基准任务上表现出强的性能，鼓励开发更多多语种大语言模型的兴趣。