readme: ---
  license: apache-2.0
  tags:
  - int8
  - Intel® Neural Compressor
  - neural-compressor
  - PostTrainingDynamic
  datasets: 
  - mnli
  metrics:
  - accuracy
  ---
  
  # INT8 T5 small finetuned on XSum
  
  ### 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 [adasnew/t5-small-xsum](https://huggingface.co/adasnew/t5-small-xsum).
  
  The linear modules **lm.head**, fall back to fp32 for less than 1% relative accuracy loss.
  
  ### Evaluation result
  
  |   |INT8|FP32|
  |---|:---:|:---:|
  | **Accuracy (eval-rouge1)** | 29.9008 |29.9592|
  | **Model size**  |154M|242M|
  
  ### Load with optimum:
  
  ```python
  from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM
  int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained(
      'Intel/t5-small-xsum-int8-dynamic',
  )
  ```
  