Summary of Search For Efficient Large Language Models, by Xuan Shen et al.
Search for Efficient Large Language Models
by Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, Yanzhi Wang
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposes a novel training-free architecture search framework to identify optimal subnets for Large Language Models (LLMs) that preserve their strengths while achieving inference acceleration. Unlike traditional methods, which struggle with the complexity of LLMs, this approach leverages the redundancy in LLMs and generates subnets that inherit specific weights from the original models. A reformation algorithm is introduced to rectify the inherited weights using a small amount of calibration data. The proposed method demonstrates superior performance on standard benchmarks compared to state-of-the-art training-free structured pruning works. Furthermore, the generated subnets can directly reduce GPU memory usage and achieve inference acceleration. This work sheds light on the potential of exploring optimal architectures for LLMs, which is crucial for their widespread adoption in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where computers can understand language as well as humans do. Researchers have been working on building these machines, called Large Language Models (LLMs), but they take up too much memory and are slow to use. To solve this problem, scientists came up with a way to find the most important parts of an LLM and create smaller versions that can still understand language well. This new approach doesn’t require training like traditional methods do, which makes it faster and more efficient. The results show that these smaller models are better than previous attempts at compressing LLMs, and they use less memory while being just as good at understanding language. |
Keywords
» Artificial intelligence » Inference » Pruning
