Update README.md

README.md

@@ -8,9 +8,6 @@ base_model:
 ---
 # bge-base-en-v1.5-int8-ov
 
-> [!WARNING]
-> **Disclaimer**: This model is provided for evaluation purposes only. Performance, accuracy, and stability may vary. Use at your own discretion.
-
  * Model creator: [BAAI](https://huggingface.co/BAAI)
  * Original model: [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
 
@@ -22,56 +19,19 @@ This is [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) model c
 
 ## Quantization Parameters
 
-The quantization was performed using the next code:
-
-```
-from functools import partial
-
-from transformers import AutoTokenizer
-
-from optimum.intel import OVConfig, OVModelForFeatureExtraction, OVQuantizationConfig, OVQuantizer
-
-
-MODEL_ID = "OpenVINO/bge-base-en-v1.5-fp16-ov"
-base_model_path = "bge-base-en-v1.5"
-int8_ptq_model_path = "bge-base-en-v1.5-int8"
-
-model = OVModelForFeatureExtraction.from_pretrained(MODEL_ID)
-model.save_pretrained(base_model_path)
+Weight compression was performed using `nncf.compress_weights` with the following parameters:
 
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-tokenizer.save_pretrained(base_model_path)
-
-
-quantizer = OVQuantizer.from_pretrained(model)
-
-def preprocess_function(examples, tokenizer):
-    return tokenizer(examples["sentence"], padding="max_length", max_length=384, truncation=True)
-
-
-calibration_dataset = quantizer.get_calibration_dataset(
-    "glue",
-    dataset_config_name="sst2",
-    preprocess_function=partial(preprocess_function, tokenizer=tokenizer),
-    num_samples=300,
-    dataset_split="train",
-)
-
-ov_config = OVConfig(quantization_config=OVQuantizationConfig())
-
-quantizer.quantize(ov_config=ov_config, calibration_dataset=calibration_dataset, save_directory=int8_ptq_model_path)
-tokenizer.save_pretrained(int8_ptq_model_path)
-```
+* mode: **INT8_ASYM**
 
-For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/quantizing-models-post-training.html).
+For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).
 
 
 ## Compatibility
 
 The provided OpenVINO™ IR model is compatible with:
 
-* OpenVINO version 2025.1.0 and higher
-* Optimum Intel 1.24.0 and higher
+* OpenVINO version 2025.3.0 and higher
+* Optimum Intel 1.25.2 and higher
 
 
 ## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)