Summary of Efficiently Distilling Llms For Edge Applications, by Achintya Kundu et al.
Efficiently Distilling LLMs for Edge Applications
by Achintya Kundu, Fabian Lim, Aaron Chew, Laura Wynter, Penny Chong, Rhui Dih Lee
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed Multistage Low-rank Fine-tuning of Super-transformers (MLFS) method enables parameter-efficient supernet training for Large Language Models (LLMs). By leveraging this approach, industrial applications can produce a variety of smaller models at constant cost, regardless of the number or size of the models. This is particularly useful in edge computing scenarios where computational resources are limited. The authors demonstrate that high-quality encoder models suitable for commercial use can be obtained using MLFS, and show that decoder-only models exhibit similar resistance to compression. Furthermore, decoders can be effectively sliced to significantly reduce training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Supernet training is important because it lets companies make lots of smaller models without spending more money or resources. This helps with things like self-driving cars or smart home devices. The researchers created a new way to train these supermodels called Multistage Low-rank Fine-tuning of Super-transformers (MLFS). They found that this method can create really good models that are perfect for everyday use, and it also works well when you need to make many different models. |
Keywords
» Artificial intelligence » Decoder » Encoder » Fine tuning » Parameter efficient
