| 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 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 optimum: | |
| ```python | |
| from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM | |
| int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained( | |
| 'Intel/t5-small-xsum-int8-dynamic', | |
| ) | |
| ``` | |