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--- |
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license: apache-2.0 |
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tags: |
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- int8 |
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- Intel® Neural Compressor |
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- neural-compressor |
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- PostTrainingDynamic |
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datasets: |
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- cnn-news |
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metrics: |
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- accuracy |
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--- |
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# INT8 T5 small finetuned on CNN-News |
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### Post-training dynamic quantization |
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This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). |
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The original fp32 model comes from the fine-tuned model [shivaniNK8/t5-small-finetuned-cnn-news](https://huggingface.co/shivaniNK8/t5-small-finetuned-cnn-news). |
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The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104. |
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The linear modules **lm.head**, fall back to fp32 for less than 1% relative accuracy loss. |
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### Evaluation result |
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| |INT8|FP32| |
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|---|:---:|:---:| |
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| **Accuracy (eval-rouge1)** | 38.9981 |39.2142| |
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| **Model size** |154M|242M| |
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### Load with optimum: |
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```python |
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from optimum.intel import INCModelForSeq2SeqLM |
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model_id = "Intel/t5-small-finetuned-cnn-news-int8-dynamic" |
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int8_model = INCModelForSeq2SeqLM.from_pretrained(model_id) |
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``` |
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