Initial ParetoQ commit #1876
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Initial ParetoQ commit #1876
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1876
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New FailureAs of commit 77b1bcc with merge base 8c81863 ( NEW FAILURE - The following job has failed:
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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.