Summary of Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization, by Mohammad Samragh et al.
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
by Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid, Fartash Faghri, Minsik Cho, Moin Nabi, Devang Naik, Mehrdad Farajtabar
First submitted to arxiv on: 19 Sep 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 The paper proposes an innovative method to initialize large language models using smaller pre-trained models, which can reduce training time and improve accuracy. The authors introduce HyperCloning, a technique that expands the parameters of a pre-trained model to those of a larger model with increased hidden dimensions, allowing the larger model to retain the functionality of the smaller one. This approach enables the larger model to inherit the predictive power and accuracy of the smaller model before training begins, resulting in significant savings in GPU hours required for pre-training large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper wants to make it possible to use small language models to help bigger ones learn. Right now, big models take a long time and lots of computer power to train. Small models are faster and cheaper to train, but they often don’t work as well. The authors ask if we can find a way to start training big models using the knowledge already in smaller models. They come up with a method called HyperCloning that lets us do just that. This helps big models learn faster and more accurately. |
