README.md

metadata

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: 4.992

INT8 gpt-neox-japanese-2.7b-int8

Post-training static quantization

PyTorch

This is an INT8 PyTorch model quantized with Intel® Neural Compressor.

The original fp32 model comes from the fine-tuned model 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:

from optimum.intel import INCModelForCausalLM

model_id = "Intel/gpt-neox-japanese-2.7b-int8"
int8_model = INCModelForCausalLM.from_pretrained(model_id)