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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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.. | ||
merges | ||
__init__.py | ||
adapter.py | ||
chunks.py | ||
convert.py | ||
dist.py | ||
hub.py | ||
import_utils.py | ||
log.py | ||
logits_process.py | ||
peft.py | ||
quantization.py | ||
segments.py | ||
sgmv.py | ||
speculate.py | ||
tokens.py | ||
watermark.py | ||
weights.py |