Add minimal usage snippets (Kotlin + Python)

README.md

@@ -28,6 +28,39 @@ udnie), each a **3.5 MB** fp16 graph.
 image[1,3,256,256] (RGB 0-255) →[GPU: TransformerNet]→ stylized[1,3,256,256] (RGB 0-255)
 ```
 
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
+
+```kotlin
+val model = CompiledModel.create(context.assets, "style_candy_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(chw)            // [1,3,256,256] RGB 0-255, NCHW
+model.run(inputs, outputs)
+val stylized = outputs[0].readFloat()    // [1,3,256,256] RGB 0-255
+```
+
+**Python (desktop verification)**
+
+```python
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+
+img = Image.open("photo.jpg").convert("RGB")
+w, h = img.size; s = min(w, h)
+img = img.crop(((w-s)//2, (h-s)//2, (w+s)//2, (h+s)//2)).resize((256, 256))
+x = np.asarray(img, np.float32).transpose(2, 0, 1)[None]     # 0-255, no normalization
+
+# candy / mosaic / rain_princess / udnie
+it = Interpreter(model_path="style_candy_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
+y = it.get_tensor(it.get_output_details()[0]["index"])[0]    # [3,256,256] RGB 0-255
+Image.fromarray(y.transpose(1, 2, 0).clip(0, 255).astype(np.uint8)).save("stylized.png")
+```
+
 ## How it converts (litert-torch) — three numerically-exact re-authorings
 
 1. **`ReflectionPad2d` → zero-pad** (`GATHER_ND` → `PAD`; border-only difference).