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This paper presents the first systematic study of quantization for efficient deployment of diffusion large-scale language models (dLLMs). Specifically, we highlight that activation outliers pose challenges to low-bit quantization, and evaluate the quantization behavior of dLLMs across various task types, model variants, quantization methods, and bit widths.
Takeaways, Limitations
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Takeaways:
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The first systematic study of quantization of dLLMs.
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We confirm that activation outliers are a major problem in low-bit quantization.
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Provides practical insights into the quantization behavior of dLLMs under different settings (bitwidth, quantization method, task category, model type).
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Provides a foundation for future research on efficient dLLM deployment.
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Code released on GitHub.
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Limitations:
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The paper does not provide details on specific quantization methods or performance results (e.g., performance comparisons of specific quantization methods, accuracy degradation with bit width, etc.).
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Further research is needed to determine the generalizability of the methodology presented in the paper.
