metadata
license: apache-2.0
tags:
- int8
- Intel® Neural Compressor
- neural-compressor
- PostTrainingDynamic
datasets:
- cnn-news
metrics:
- accuracy
INT8 T5 small finetuned on CNN-News
Post-training dynamic quantization
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model shivaniNK8/t5-small-finetuned-cnn-news.
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) | 38.9981 | 39.2142 |
| Model size | 154M | 242M |
Load with optimum:
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM
int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained(
'Intel/t5-small-finetuned-cnn-news-int8-dynamic',
)