What is recorded here?
Used approximate second-order information for one-shot post-training weight quantization of large pretrained language models.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Year follows the 2022 preprint; conference publication was 2023. Compression and accuracy results depend on the model, calibration data, bit width and kernels.
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People and systems
Elias Frantar; Saleh Ashkboos; Torsten Hoefler; Dan Alistarh
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
Frantar and colleagues: GPTQ post-training weight quantization
Used approximate second-order information for one-shot post-training weight quantization of large pretrained language models.
Scientific scope
Year follows the 2022 preprint; conference publication was 2023. Compression and accuracy results depend on the model, calibration data, bit width and kernels.
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
Elias Frantar; Saleh Ashkboos; Torsten Hoefler; Dan Alistarh
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.