Xmodel-2 Technical Report

• URL: http://arxiv.org/abs/2412.19638v1
• Date: Fri, 27 Dec 2024 13:32:10 GMT
• Title: Xmodel-2 Technical Report
• Authors: Wang Qun, Liu Yang, Lin Qingquan, Qu Zhijiu, Jiang Ling,
• Abstract summary: Xmodel-2 is a large language model designed specifically for reasoning tasks.<n>It employs the WSD learning rate scheduler from MiniCPM to maximize training efficiency and stability.<n>Xmodel-2 achieves state-of-the-art performance in complex reasoning and agent-based tasks, while maintaining low training costs.
• Score: 4.0069773933776665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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