metadata
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.
The original fp32 model comes from the fine-tuned model 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:
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/roberta-base-squad2-int8-static',
)