Initial ParetoQ commit#1876
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should there be a test of some sort? Otherwise it's likely this will break soon without anyone knowing. |
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jerryzh168
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Apr 9, 2025
This project contains the training code of ParetoQ introduced in: "ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization" (https://arxiv.org/abs/2502.02631). All code is written by @liuzechun and @zxdmike and migrated from https://github.com/facebookresearch/ParetoQ. ParetoQ is the first unified framework that facilitates rigorous comparisons across 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings. By optimizing training schemes and refining quantization functions, ParetoQ surpasses all previous methods tailored to specific bit widths. Specifically, the 1.58-bit ParetoQ LLaMA-3 8B model reduces the performance gap to full precision by relatively 37.8% compared to the 1-bit Era’s 1.58-bit LLaMA-3 8B model, while using only 30% of the training tokens.
liangel-02
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This project contains the training code of ParetoQ introduced in: "ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization" (https://arxiv.org/abs/2502.02631). All code is written by @liuzechun and @zxdmike and migrated from https://github.com/facebookresearch/ParetoQ. ParetoQ is the first unified framework that facilitates rigorous comparisons across 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings. By optimizing training schemes and refining quantization functions, ParetoQ surpasses all previous methods tailored to specific bit widths. Specifically, the 1.58-bit ParetoQ LLaMA-3 8B model reduces the performance gap to full precision by relatively 37.8% compared to the 1-bit Era’s 1.58-bit LLaMA-3 8B model, while using only 30% of the training tokens.
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This project contains the training code of ParetoQ introduced in: "ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization" (https://arxiv.org/abs/2502.02631). All code is written by @liuzechun and @zxdmike and migrated from
https://github.com/facebookresearch/ParetoQ.
ParetoQ is the first unified framework that facilitates rigorous comparisons across 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings. By optimizing training schemes and refining quantization functions, ParetoQ surpasses all previous methods tailored to specific bit widths. Specifically, the 1.58-bit ParetoQ LLaMA-3 8B model reduces the performance gap to full precision by relatively 37.8% compared to the 1-bit Era’s 1.58-bit LLaMA-3 8B model, while using only 30% of the training tokens.