Summary of Openelm: An Efficient Language Model Family with Open Training and Inference Framework, by Sachin Mehta and Mohammad Hossein Sekhavat and Qingqing Cao and Maxwell Horton and Yanzi Jin and Chenfan Sun and Iman Mirzadeh and Mahyar Najibi and Dmitry Belenko and Peter Zatloukal and Mohammad Rastegari
OpenELM: An Efficient Language Model Family with Open Training and Inference Framework
by Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces OpenELM, an open-source large language model that prioritizes reproducibility and transparency. The researchers develop a layer-wise scaling strategy to optimize the transformer model’s performance, leading to enhanced accuracy. For instance, when limited to approximately one billion parameters, OpenELM outperforms OLMo by 2.36% while requiring fewer pre-training tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OpenELM is an open language model designed to ensure reproducibility and transparency in large-scale research. The paper shows how a special scaling strategy helps the model work better. This means that OpenELM can do things other models can’t, like getting more accurate results with less data. |
Keywords
» Artificial intelligence » Language model » Large language model » Transformer
