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INT8 DistilBERT base cased finetuned on Squad

Post-training static quantization

This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model distilbert-base-cased-distilled-squad.

The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.

The linear module distilbert.transformer.layer.1.ffn.lin2 falls back to fp32 to meet the 1% relative accuracy loss.

Test result

INT8 FP32
Accuracy (eval-f1) 86.0005 86.8373
Model size (MB) 71.2 249

Load with optimum:

from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering
int8_model = IncQuantizedModelForQuestionAnswering(
    'Intel/distilbert-base-cased-distilled-squad-int8-static',
)
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This model can be loaded on the Inference API on-demand.

Dataset used to train Intel/distilbert-base-cased-distilled-squad-int8-static