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Oct 23, 2025
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docs: fix qat description in README.md #3212

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2 changes: 1 addition & 1 deletion torchao/quantization/qat/README.md
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Quantization-Aware Training (QAT) refers to applying fake quantization during the
training or fine-tuning process, such that the final quantized model will exhibit
higher accuracies and perplexities. Fake quantization refers to rounding the float
higher accuracies and lower perplexities. Fake quantization refers to rounding the float
values to quantized values without actually casting them to dtypes with lower
bit-widths, in contrast to post-training quantization (PTQ), which does cast the
quantized values to lower bit-width dtypes, e.g.:
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