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---
license: apache-2.0
tags:
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- mnli
metrics:
- accuracy
---
# INT8 T5 small finetuned on XSum
### Post-training dynamic quantization
This is an INT8 PyTorch model quantized with [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 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.
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 Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/roberta-base-squad2-int8-static',
)
```
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