Xmodel-2 Technical Report
Authors: name: Wang Qun, name: Liu Yang, name: Lin Qingquan, name: Qu Zhijiu, name: Jiang Ling

Xmodel-2 is a 1.2-billion-parameter large language model designed
specifically for reasoning tasks. Its architecture enables different model
scales to share a unified set of hyperparameters, allowing for extensive
experimentation on smaller models and seamless transfer of optimal
configurations to larger models. To maximize training efficiency and stability,
Xmodel-2 employs the WSD learning rate scheduler from MiniCPM. Pretrained on
1.5 trillion tokens from diverse sources, Xmodel-2 achieves state-of-the-art
performance in complex reasoning and agent-based tasks, while maintaining low
training costs. These results highlight the potential of efficient model design
and training strategies in advancing reasoning capabilities. Model checkpoints
and code are publicly available on GitHub at
https://github.com/XiaoduoAILab/Xmodel-2