Summary of Xmodel-2 Technical Report, by Wang Qun et al.
Xmodel-2 Technical Report
by Wang Qun, Liu Yang, Lin Qingquan, Qu Zhijiu, Jiang Ling
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Xmodel-2 is a large language model designed for reasoning tasks, boasting 1.2 billion parameters. Its architecture enables smaller models to share hyperparameters, facilitating experimentation and seamless transfer of optimal configurations to larger models. To optimize training efficiency and stability, the WSD learning rate scheduler from MiniCPM is employed. 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new large language model called Xmodel-2, which is specifically designed for reasoning tasks. It has many parameters and allows smaller models to share settings with larger ones, making it easier to experiment and improve performance. The model was trained on a huge amount of text data from different sources and does well at complex tasks like reasoning and problem-solving. |
Keywords
» Artificial intelligence » Large language model
