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---
language:
- ja
license: mit
tags:
- ja
- japanese
- gpt_neox
- gpt
- text-generation
- lm
- nlp
- int8
- neural-compressor
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- oscar
model-index:
- name: gpt-neox-japanese-2.7b-int8
results:
- task:
name: Text Generation
type: text-generation
dataset:
name: oscar
type: oscar
args: unshuffled_original_ast
metrics:
- name: Acurracy
type: loss
value: 4.9920
---
# INT8 gpt-neox-japanese-2.7b-int8
## Post-training static quantization
### PyTorch
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 [abeja/gpt-neox-japanese-2.7b](https://huggingface.co/abeja/gpt-neox-japanese-2.7b).
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.
#### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-loss)** |4.9920|3.5219|
| **Model size (MB)** |2570|5360|
#### Load with Intel® Neural Compressor:
```python
from optimum.intel import INCModelForCausalLM
model_id = "Intel/gpt-neox-japanese-2.7b-int8"
int8_model = INCModelForCausalLM.from_pretrained(model_id)
```
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