Summary of Skywork-moe: a Deep Dive Into Training Techniques For Mixture-of-experts Language Models, by Tianwen Wei et al.
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
by Tianwen Wei, Bo Zhu, Liang Zhao, Cheng Cheng, Biye Li, Weiwei Lü, Peng Cheng, Jianhao Zhang, Xiaoyu Zhang, Liang Zeng, Xiaokun Wang, Yutuan Ma, Rui Hu, Shuicheng Yan, Han Fang, Yahui Zhou
First submitted to arxiv on: 3 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 This technical report introduces the Skywork-MoE, a high-performance mixture-of-experts (MoE) large language model with 146 billion parameters and 16 experts. The training methodologies used to develop Skywork-MoE include initializing it from pre-existing dense checkpoints of our Skywork-13B model. The paper compares upcycling versus training from scratch initializations, finding that the choice between these approaches depends on both performance and MoE training budget. Two innovative techniques are highlighted: gating logit normalization for expert diversification and adaptive auxiliary loss coefficients for layer-specific adjustment. Experimental results validate the effectiveness of these methods. By leveraging these techniques and insights, our upcycled Skywork-MoE was trained on a condensed subset of the SkyPile corpus, achieving strong performance across various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This report introduces a new language model called Skywork-MoE. It’s a special type of AI that can understand and generate human-like text. The model has 146 billion tiny adjustments, or “parameters,” and uses a technique called mixture-of-experts to work well on different tasks. The researchers tested two ways to start training the model: using an existing, already-trained version as a starting point, or creating one from scratch. They found that both methods have their advantages and disadvantages. The report also introduces two new techniques to help the model learn better: gating logit normalization and adaptive auxiliary loss coefficients. These innovations helped the model perform well on a variety of tasks. |
Keywords
» Artificial intelligence » Language model » Large language model » Mixture of experts
