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Quant Experts: Token-aware Adaptive Error Reconstruction with Mixture of Experts for Large Vision-Language Models Quantization
arXiv – CS AI|Chenwei Jia, Baoting Li, Xuchong Zhang, Mingzhuo Wei, Bochen Lin, Hongbin Sun||9 views
🤖AI Summary
Researchers introduce Quant Experts (QE), a new post-training quantization technique for Vision-Language Models that uses adaptive error compensation with mixture-of-experts architecture. The method addresses computational and memory overhead issues by intelligently handling token-dependent and token-independent channels, maintaining performance comparable to full-precision models across 2B to 70B parameter scales.
Key Takeaways
- →Quant Experts (QE) introduces token-aware adaptive error compensation for Vision-Language Model quantization without requiring full model retraining.
- →The method divides important channels into token-independent groups (using shared experts) and token-dependent groups (using routed experts).
- →QE addresses the limitation of existing PTQ methods that overlook distributional differences of important channels across different inputs.
- →Extensive testing shows consistent accuracy improvements across various quantization settings from 2B to 70B parameter models.
- →The technique maintains performance comparable to full-precision models while significantly reducing computational and memory requirements.
#quantization#vision-language-models#post-training-quantization#mixture-of-experts#ai-optimization#computational-efficiency#model-compression#arxiv-research
Read Original →via arXiv – CS AI
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