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This paper explores the optimal bit width to achieve an optimal trade-off between quantized model size and accuracy. We present ParetoQ, a unified framework that comprehensively compares 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings. We find a learning transfer between 2-bit and 3-bit, and ParetoQ outperforms all previous methods tuned for specific bit widths. The ParetoQ ternary 600M-parameter model outperforms the previous SoTA ternary 3B-parameter model, and the ternary, 2-bit, and 3-bit quantizations exhibit comparable performance in the size-accuracy trade-off, demonstrating the potential for 2-bit quantization to reduce memory and improve speed.
Takeaways, Limitations
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Takeaways:
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We present ParetoQ, a unified framework for comprehensively comparing different bit widths (1 bit, 1.58 bit, 2 bit, 3 bit, and 4 bit).
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Discovered learning transition between 2-bit and 3-bit.
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The ParetoQ ternary 600M-parameter model outperforms the existing SoTA 3B-parameter model.
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Ternary, 2-bit, and 3-bit quantizations show competitive performance in size-accuracy tradeoffs.
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2-bit quantization has the potential to reduce memory and improve speed.
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Limitations:
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There is no direct mention of Limitations in the paper.
