Xmodel-2 Technical Report
This work addresses the need for efficient reasoning models in AI, though it appears incremental as it builds on existing methods like the WSD scheduler from MiniCPM.
The authors tackled the problem of developing efficient large language models for reasoning tasks by introducing Xmodel-2, a 1.2-billion-parameter model that achieves state-of-the-art performance in complex reasoning and agent-based tasks while maintaining low training costs.
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