Efficient One-shot Compression via Low-Rank Local Feature Distillation (2025.naacl-long)
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| Challenge: | Existing structured pruning approaches for large language models require calibration data and costly continued pretraining on billions of tokens to recover lost performance. |
| Approach: | They propose a method that locally distills activations with low-rank weights . they compress Mixtral-8x7B on a single GPU and Phi-2 3B by 40% . |
| Outcome: | The proposed method compresses Mixtral-8x7B on a single A100 GPU, removing 10 billion parameters while retaining over 95% of its original performance. |
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| Challenge: | Recent advances in hardware, modeling, and optimization for deep neural networks have led to improvements in memory and inference efficiency. |
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
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| Challenge: | Low-rank decomposition methods suffer from accuracy degradation and expensive calibration procedures. |
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| Challenge: | Low-rank adaption (LoRA) is a low-level pruning method that can be expensive and slow to deploy. |
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