Summary of Xmodel-lm Technical Report, by Yichuan Wang et al.
Xmodel-LM Technical Report
by Yichuan Wang, Yang Liu, Yu Yan, Qun Wang, Xucheng Huang, Ling Jiang
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A compact and efficient 1.1B language model, Xmodel-LM, is introduced, pre-trained on approximately 2 trillion tokens. The model is trained on the self-built Xdata dataset, which balances Chinese and English corpora based on downstream task optimization. Despite its smaller size, Xmodel-LM exhibits remarkable performance, surpassing existing open-source language models of similar scale. The model checkpoints and code are publicly accessible on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Xmodel-LM is a new language model that’s really good at understanding text! It was trained on a huge dataset with both Chinese and English texts, which helps it do well on different tasks. Even though it’s smaller than some other models, it can still do lots of things as well or even better than them. You can get the details about this model from GitHub. |
Keywords
» Artificial intelligence » Language model » Optimization
