Summary of Reassessing Layer Pruning in Llms: New Insights and Methods, by Yao Lu et al.
Reassessing Layer Pruning in LLMs: New Insights and Methods
by Yao Lu, Hao Cheng, Yujie Fang, Zeyu Wang, Jiaheng Wei, Dongwei Xu, Qi Xuan, Xiaoniu Yang, Zhaowei Zhu
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 investigates the best practices for pruning large language models (LLMs) to make them more deployable in resource-constrained environments. The authors explore various layer selection metrics and fine-tuning methods, including LoRA (Low-Rank Approximation), to determine their effectiveness in reducing computational overhead while preserving model performance. They find that a simple approach involving pruning the final 25% of layers and fine-tuning specific components yields strong results, even surpassing popular LLMs of similar size. The authors share their optimized model weights on Huggingface and provide the code on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make big language models smaller and faster for places with limited computers. It tries different ways to pick which parts of the model to remove and how to adjust the remaining parts to keep the model working well. The results show that a simple method works best, where you take away the last part of the model and fine-tune some specific parts. This can make the model as good or even better than similar-sized models. You can get the best version from Huggingface and look at the code on GitHub. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Pruning
