add model card.

README.md

@@ -1,3 +1,39 @@
 ---
 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',
+)
+```