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Due to the substantial scale of Large Language Models (LLMs), the directapplication of conventional compression methodologies proves impractical. Thecomputational demands associated with even minimal gradient updates presentchallenges, particularly on consumer-grade hardware. This paper introduces aninnovative approach for the parametric and practical compression of LLMs basedon reduced order modelling, which entails low-rank decomposition within thefeature space and re-parameterization in the weight space. Notably, thiscompression technique operates in a layer-wise manner, obviating the need for aGPU device and enabling the compression of billion-scale models withinstringent constraints of both memory and time. Our method represents asignificant advancement in model compression by leveraging matrixdecomposition, demonstrating superior efficacy compared to the prevailingstate-of-the-art structured pruning method.

Rethinking Compression: Reduced Order Modelling of Latent Features in Large Language Models