readme: ---
  license: apache-2.0
  tags:
  - int8
  - Intel® Neural Compressor
  - neural-compressor
  - PostTrainingDynamic
  datasets: 
  - cnn_dailymail
  metrics:
  - rougeLsum
  ---
  
  # INT8 DistilBart finetuned on CNN DailyMail
  
  ### Post-training dynamic quantization
  
  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). 
  
  The original fp32 model comes from the fine-tuned model [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6).
  
  Below linear modules (21/133) are fallbacked to fp32 for less than 1% relative accuracy loss:
  
  **'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
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-rougeLsum)** | 41.4707 | 41.8117 |
  | **Model size**  |722M|1249M|
  ### Load with optimum:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM
  int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained(
      'Intel/distilbart-cnn-12-6-int8-dynamic',
  )
  ```