Summary of Prompt-prompted Adaptive Structured Pruning For Efficient Llm Generation, by Harry Dong et al.
Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation
by Harry Dong, Beidi Chen, Yuejie Chi
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 A novel method for efficiently generating transformer-based large language models (LLMs) is introduced, addressing the computational cost and memory requirements of deploying these models. GRIFFIN selects unique feedforward (FF) experts at the sequence level, leveraging the naturally structured FF activation patterns found in many trained LLMs. This simple yet effective approach maintains the original model’s performance with little to no degradation on various classification and generation tasks while achieving significant speed-ups. The method is training-free and calibration-free, making it a practical solution for deploying transformer-based LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GRIFFIN is a new way to make language models run faster and use less memory. It works by finding the best parts of the model that need to do all the work, instead of using the whole model. This helps language models like transformers be more efficient and easier to use. |
Keywords
» Artificial intelligence » Classification » Transformer
