diff --git a/README.md b/README.md
index aeb0837..802ba14 100644
--- a/README.md
+++ b/README.md
@@ -26,15 +26,22 @@ pip install git+https://gitea.fabelous.app/Machine-Learning/aiuNN.git
 Here's a basic example of how to use `aiuNN` for image upscaling:
 
 ```python src/main.py
-from aiia import AIIABase
+from aiia import AIIABase, AIIAConfig
 from aiunn import aiuNN, aiuNNTrainer
 import pandas as pd
 from torchvision import transforms
 
+# Create a configuration and build a base model.
+config = AIIAConfig()
+ai_config = aiuNNConfig()
+
+base_model = AIIABase(config)
+upscaler = aiuNN(config=ai_config)
+
 # Load your base model and upscaler
 pretrained_model_path = "path/to/aiia/model"
-base_model = AIIABase.load(pretrained_model_path, precision="bf16")
-upscaler = aiuNN(base_model)
+base_model = AIIABase.from_pretrained(pretrained_model_path)
+upscaler.load_base_model(base_model)
 
 # Create trainer with your dataset class
 trainer = aiuNNTrainer(upscaler, dataset_class=UpscaleDataset)
@@ -105,19 +112,19 @@ class UpscaleDataset(Dataset):
             # Open image bytes with Pillow and convert to RGBA first
             low_res_rgba = Image.open(io.BytesIO(low_res_bytes)).convert('RGBA')
             high_res_rgba = Image.open(io.BytesIO(high_res_bytes)).convert('RGBA')
-            
+
             # Create a new RGB image with black background
             low_res_rgb = Image.new("RGB", low_res_rgba.size, (0, 0, 0))
             high_res_rgb = Image.new("RGB", high_res_rgba.size, (0, 0, 0))
-            
+
             # Composite the original image over the black background
             low_res_rgb.paste(low_res_rgba, mask=low_res_rgba.split()[3])
             high_res_rgb.paste(high_res_rgba, mask=high_res_rgba.split()[3])
-            
+
             # Now we have true 3-channel RGB images with transparent areas converted to black
             low_res = low_res_rgb
             high_res = high_res_rgb
-                        
+
             # If a transform is provided (e.g. conversion to Tensor), apply it
             if self.transform:
                 low_res = self.transform(low_res)
@@ -127,4 +134,4 @@ class UpscaleDataset(Dataset):
             print(f"\nError at index {idx}: {str(e)}")
             self.failed_indices.add(idx)
             return self[(idx + 1) % len(self)]
-```
+```
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