What is recorded here?
Applied equivalent offline rescaling to shift activation outliers into easier-to-quantize weights, enabling eight-bit weight and activation inference.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Year follows the 2022 preprint; ICML publication was 2023. Accuracy and acceleration are empirical for evaluated models and implementations, not a universal lossless guarantee.
Read the original work
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
- MIT Han Lab: SmoothQuant implementation
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People and systems
Guangxuan Xiao; Ji Lin; Mickael Seznec; Hao Wu; Julien Demouth; Song Han
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Xiao and colleagues: SmoothQuant activation smoothing
Applied equivalent offline rescaling to shift activation outliers into easier-to-quantize weights, enabling eight-bit weight and activation inference.
Scientific scope
Year follows the 2022 preprint; ICML publication was 2023. Accuracy and acceleration are empirical for evaluated models and implementations, not a universal lossless guarantee.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Guangxuan Xiao; Ji Lin; Mickael Seznec; Hao Wu; Julien Demouth; Song Han
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.