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UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
arXiv – CS AI|Hung-Yueh Chiang, Chi-Chih Chang, Yu-Chen Lu, Chien-Yu Lin, Kai-Chiang Wu, Mohamed S. Abdelfattah, Diana Marculescu||8 views
🤖AI Summary
Researchers introduce UniQL, a unified framework for quantizing and compressing large language models to run efficiently on mobile devices. The system achieves 4x-5.7x memory reduction and 2.7x-3.4x speed improvements while maintaining accuracy within 5% of original models.
Key Takeaways
- →UniQL enables deployment of large language models on mobile devices through unified quantization and low-rank compression.
- →The framework supports diverse model types including Transformers, State Space Models, and hybrid architectures.
- →Memory usage is reduced by 4x-5.7x while token throughput improves by 2.7x-3.4x compared to original models.
- →Models maintain accuracy within 5% degradation at 15% pruning rates across tested architectures.
- →The system processes weight-sorting, fine-tuning, and quantization in a single cloud-based workflow with configurable on-device pruning.
Read Original →via arXiv – CS AI
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