Recent advancements in Large Language Models (LLMs) have led to models containing billions or even trillions of parameters, achieving remarkable performance. However, their size poses challenges in practical deployment due to hardware requirements. The proposed ShortGPT approach from Baichuan Inc. and the Chinese Information Processing Laboratory Institute of Software aims to remove redundant layers based on BI scores, demonstrating significant performance improvements.
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Practical AI Solutions for Middle Managers
Advancements in Large Language Models (LLMs)
Recent advancements in Large Language Models (LLMs) have led to models containing billions or even trillions of parameters, achieving remarkable performance across domains. However, their massive size poses challenges in practical deployment due to stringent hardware requirements. Research has focused on scaling models to enhance performance, guided by established scaling laws. This escalation underscores the need to address hardware limitations to facilitate the widespread utilization of these powerful LLMs.
Addressing Deployment Challenges
Prior works have addressed the challenge of deploying massive trained models by focusing on model compression techniques. These techniques, including quantization and pruning, aim to reduce inference costs. Recent advancements in pruning techniques have shown promise in simplifying model compression for large language models, highlighting the importance of exploring efficient pruning approaches tailored for such models.
ShortGPT: A Unique Pruning Approach
The researchers from Baichuan Inc. and the Chinese Information Processing Laboratory Institute of Software, Chinese Academy of Sciences, present a unique approach, ShortGPT, to analyze layer-wise redundancy in LLMs using Block Influence (BI), measuring hidden state transformations. Their method significantly outperforms previous complex pruning techniques by identifying and removing redundant layers based on BI scores. This method, orthogonal to quantization, reduces parameters and computation while maintaining high performance, paving the way for more efficient LLM training.
Impact and Performance
The proposed method’s comparative experiments against benchmarks and baseline techniques show that the model pruned using the proposed approach consistently outperforms baseline methods across multiple natural language benchmarks. Results demonstrate significant layer-wise redundancy in LLMs, enabling the removal of minimally contributing layers without compromising performance.
Conclusion
In conclusion, the proposed strategy maintains up to 95% of model performance while reducing parameter count and computational requirements by around 25%, surpassing previous pruning methods. This approach, simple yet effective, suggests depth-based redundancy in LLMs and offers compatibility with other compression techniques for versatile model size reduction.
For more details, please check out the paper.
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