When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models

• URL: http://arxiv.org/abs/2502.15443v1
• Date: Fri, 21 Feb 2025 13:11:22 GMT
• Title: When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models
• Authors: Weilan Wang, Yu Mao, Dongdong Tang, Hongchao Du, Nan Guan, Chun Jason Xue,
• Abstract summary: This paper introduces a framework to compress large language models (LLMs) after quantization.<n>A compression-aware quantization is first proposed to enhance model weight compressibility by re-scaling the model parameters before quantization, followed by a pruning method to improve further.<n>Experiments show inference with the compressed model can achieve a 40% reduction in memory size with negligible loss in accuracy and inference speed.
• Score: 12.687035979970194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) exhibit excellent performance in various tasks. However, the memory requirements of LLMs present a great challenge when deploying on memory-limited devices, even for quantized LLMs. This paper introduces a framework to compress LLM after quantization further, achieving about 2.2x compression ratio. A compression-aware quantization is first proposed to enhance model weight compressibility by re-scaling the model parameters before quantization, followed by a pruning method to improve further. Upon this, we notice that decompression can be a bottleneck during practical scenarios. We then give a detailed analysis of the trade-off between memory usage and latency brought by the proposed method. A speed-adaptive method is proposed to overcome it. The experimental results show inference with the compressed model can achieve a 40% reduction in memory size with negligible loss in accuracy and inference speed.

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