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This model is a fork of https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english , quantized using static Post-Training Quantization (PTQ) with ONNX Runtime and 🤗 Optimum library.

It achieves 0.896 accuracy on the validation set.

This model uses the ONNX Runtime static quantization configurations qdq_add_pair_to_weight=True and qdq_dedicated_pair=True, so that weights are stored in fp32, and full Quantize + Dequantize nodes are inserted for the weights, compared to the default where weights are stored in int8 and only a Dequantize node is inserted for weights. Moreover, here QDQ pairs have a single output. For more reference, see the documentation: https://github.com/microsoft/onnxruntime/blob/ade0d291749144e1962884a9cfa736d4e1e80ff8/onnxruntime/python/tools/quantization/quantize.py#L432-L441

This is useful to later load a static quantized model in TensorRT.

To load this model:

from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained("fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static-dedicated-qdq-everywhere")

Weights stored as int8, only DequantizeLinear nodes (model here: https://huggingface.co/fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static)

DQ only

Weights stored as fp32, only QuantizeLinear + DequantizeLinear nodes (this model)


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This model can be loaded on the Inference API on-demand.

Datasets used to train fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static-dedicated-qdq-everywhere