Summary of Hlat: High-quality Large Language Model Pre-trained on Aws Trainium, by Haozheng Fan et al.
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
by Haozheng Fan, Hao Zhou, Guangtai Huang, Parameswaran Raman, Xinwei Fu, Gaurav Gupta, Dhananjay Ram, Yida Wang, Jun Huan
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 AWS Trainium, the second-generation machine learning accelerator, is designed for training large deep learning models. However, pre-training large language models (LLMs) on AWS Trainium is challenging due to its relatively nascent software ecosystem. In this paper, we introduce HLAT, a family of 7B and 70B decoder-only LLMs pre-trained using 4096 AWS Trainium accelerators over 1.8 trillion tokens. The performance of HLAT is benchmarked against popular open-source models, including LLaMA and OpenLLaMA, trained on NVIDIA GPUs and Google TPUs, respectively. Our results show that HLAT achieves model quality comparable to the baselines of similar model size on various evaluation tasks. We also share our training scripts and configurations, as well as best practices for using NeuronX Distributed Training (NxDT), a customized distributed training library for AWS Trainium. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful computer that can help train artificial intelligence models really quickly. This is exactly what the researchers did in this paper. They used a special machine called AWS Trainium to train large language models, which are like super smart computers that can understand and generate human-like text. The goal was to make these models work better on specific tasks, like answering questions or generating text. To do this, they trained their model using 4096 of these special machines over a huge amount of data – 1.8 trillion tokens! They compared their results with other top-performing models and found that theirs worked just as well. The researchers also shared how they did it, so others can learn from them. |
Keywords
» Artificial intelligence » Decoder » Deep learning » Llama » Machine learning
