Update README.md

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README.md

@@ -1,3 +1,61 @@
 ---
+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.neural_compressor.quantization import IncQuantizedModelForCausalLM
+
+int8_model = IncQuantizedModelForCausalLM.from_pretrained(
+    "Intel/gpt-neox-japanese-2.7b-int8",
+)
+```