Summary of Enhancing Stability For Large Language Models Training in Constrained Bandwidth Networks, by Yun Dai et al.
Enhancing Stability for Large Language Models Training in Constrained Bandwidth Networks
by Yun Dai, Tejas Dharamsi, Byron Hsu, Tao Song, Hamed Firooz
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 novel approach to training extremely large language models (LLMs) is presented, which tackles convergence issues in distributed training systems. By modifying the hierarchical partitioning (hpZ) scheme used in ZeRO++ to reduce cross-machine communication, the proposed algorithm achieves reliable convergence on massive models like Falcon Models and Llama-2. This improvement enables robust training of larger models with 98% throughput and model training speed, without compromising quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super-sized! Researchers found that training these huge models can be tricky because of “convergence issues” (think of it like trying to get a big team working together). To fix this problem, they changed the way the information is shared between computers. This new approach makes sure the model trains correctly and fast, without losing any quality. |
Keywords
» Artificial intelligence » Llama
