Use symmetric quantization in the quantize subcommand
#2120
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drbh
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Packing of asymmetric quantization is broken, all (q)zeros values of `0` get reset to `1`, resulting in a loss of accuracy. So instead use symmetric quantization. To be able to distinguish models with symmetric and asymmetric quantization, a new config tensor `gptq_sym` is added. If this tensor is not present, we assume `sym=False`.
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ErikKaum
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Packing of asymmetric quantization is broken, all (q)zeros values of `0` get reset to `1`, resulting in a loss of accuracy. So instead use symmetric quantization. To be able to distinguish models with symmetric and asymmetric quantization, a new config tensor `gptq_sym` is added. If this tensor is not present, we assume `sym=False`.
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…2120) Packing of asymmetric quantization is broken, all (q)zeros values of `0` get reset to `1`, resulting in a loss of accuracy. So instead use symmetric quantization. To be able to distinguish models with symmetric and asymmetric quantization, a new config tensor `gptq_sym` is added. If this tensor is not present, we assume `sym=False`.
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What does this PR do?
Packing of asymmetric quantization has an issue where all (q)zeros values of
0get reset to1, resulting in a loss of accuracy. So instead use symmetric quantization. To be able to distinguish models with symmetric and asymmetric quantization, a new config tensorgptq_symis added. If this tensor is not present, we assumesym=False.Before submitting
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