Update README.md

README.md

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 # INT8 DistilBart finetuned on CNN DailyMail
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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 [sysresearch101/t5-large-finetuned-xsum-cnn](https://huggingface.co/sysresearch101/t5-large-finetuned-xsum-cnn).
 
 Below linear modules are fallbacked to fp32 for less than 1% relative accuracy loss:
@@ -21,11 +24,13 @@ Below linear modules are fallbacked to fp32 for less than 1% relative accuracy l
 **'model.decoder.layers.2.fc2'**, **'model.encoder.layers.11.fc2'**, **'model.decoder.layers.1.fc2'**, **'model.decoder.layers.0.fc2'**, **'model.decoder.layers.4.fc1'**, **'model.decoder.layers.3.fc2'**, **'model.encoder.layers.8.fc2'**, **'model.decoder.layers.3.fc1'**, **'model.encoder.layers.11.fc1'**, **'model.encoder.layers.0.fc2'**, **'model.encoder.layers.3.fc1'**, **'model.encoder.layers.10.fc2'**, **'model.decoder.layers.5.fc1'**, **'model.encoder.layers.1.fc2'**, **'model.encoder.layers.3.fc2'**, **'lm_head'**, **'model.encoder.layers.7.fc2'**, **'model.decoder.layers.0.fc1'**, **'model.encoder.layers.4.fc1'**, **'model.encoder.layers.10.fc1'**, **'model.encoder.layers.6.fc1'**
 
 ### Evaluation result
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 |   |INT8|FP32|
 |---|:---:|:---:|
 | **Accuracy (eval-rougeLsum)** | 41.4707 | 41.8117 |
 | **Model size**  |722M|1249M|
 ### Load with optimum:
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 ```python
 from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM
 int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained(