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arXiv · 2412.04180

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization

Runsheng Bai·Bo Liu·Qiang Liu
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Abstract

Large Language Models (LLMs) exhibit impressive performance across various tasks, but deploying them for inference poses challenges. Their high resource demands often necessitate complex, costly multi-GPU pipelines, or the use of smaller, less capable models. While quantization offers a promising solution utilizing lower precision for model storage, existing methods frequently experience significant performance drops at lower precision levels. Additionally, they typically provide only a limited set of solutions at specific bit levels, many of which are extensively manually tuned. To address these challenges, we propose a new method called SKIM: Scaled K-means clustering wIth Mixed precision. Our approach introduces two novel techniques: 1. A greedy algorithm to solve approximately optimal bit allocation across weight channels, and 2. A trainable scaling vector for non-differentiable K-means clustering. These techniques substantially improve performance and can be adapted to any given bit. Notably, in terms of model perplexity, our method narrows the gap between 3-bit quantized LLaMA models and their full precision counterparts by 16.3% on average.

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Runsheng Bai, Bo Liu, Qiang Liu. 2024-12-05. SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization. https://arxiv.org/abs/2412.04180

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