Summary of Rankadaptor: Hierarchical Rank Allocation For Efficient Fine-tuning Pruned Llms Via Performance Model, by Changhai Zhou et al.
RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model
by Changhai Zhou, Shijie Han, Lining Yang, Yuhua Zhou, Xu Cheng, Yibin Wang, Hongguang Li
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper addresses the challenge of efficiently compressing large language models (LLMs) while recovering their performance in downstream tasks. Current methods employ structural pruning and Low-Rank Adaptation (LoRA), but this approach is suboptimal due to uneven modification of model architecture and fixed configuration allocation across layers. To improve upon this, the authors introduce RankAdaptor, a hierarchical rank allocation method that enables fine-tuning of pruned LLMs based on layerwise specific recovery requirements. The approach uses offline meta-learning and online incremental learning to determine optimal rank values for each layer. Experimental results show that RankAdaptor outperforms state-of-the-art methods across various pruning settings and LLM architectures, with improvements ranging from 0.7% to 5.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a way to make big language models smaller while keeping them good at doing their job. Currently, people are using two main techniques: structural pruning and something called LoRA. But these methods have some problems – they change the model’s architecture in different ways and don’t adjust to each layer’s needs. To solve this, the authors created a new method called RankAdaptor that helps fine-tune the pruned models based on what each layer needs. The new approach uses two kinds of learning: one that happens offline and another that happens online while the model is working. The results show that this new method works better than others in many situations, with improvements ranging from a little bit to quite a lot. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation » Meta learning » Pruning
