Add minimal usage snippets (Kotlin + Python)

README.md

@@ -29,6 +29,43 @@ face[1,3,256,256] (mmpose mean/std) →[GPU: RTMPose-m]→ simcc_x[1,98,512], si
 
 output[0] = simcc_x, output[1] = simcc_y; each landmark = `argmax` over its 1D SimCC (bins = pixels × 2).
 
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
+
+```kotlin
+val model = CompiledModel.create(context.assets, "rtm_face_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(chw)              // [1,3,256,256] mmpose mean/std (0-255 RGB), NCHW
+model.run(inputs, outputs)
+val simccX = outputs[0].readFloat()    // [1,98,512]
+val simccY = outputs[1].readFloat()    // [1,98,512]; keypoint = argmax / 2
+```
+
+**Python (desktop verification)**
+
+```python
+MEAN = np.array([123.675, 116.28, 103.53], np.float32)
+STD  = np.array([58.395, 57.12, 57.375], np.float32)
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+
+img = Image.open("face.jpg").convert("RGB").resize((256, 256))  # centered subject crop
+x = ((np.asarray(img, np.float32) - MEAN) / STD).transpose(2, 0, 1)[None]
+
+it = Interpreter(model_path="rtm_face_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
+od = it.get_output_details()                                     # output 0 = simcc_x, 1 = simcc_y
+sx = it.get_tensor(od[0]["index"])[0]                             # simcc_x [98,512]
+sy = it.get_tensor(od[1]["index"])[0]                             # simcc_y [98,512]
+kx, ky = sx.argmax(-1) / 2.0, sy.argmax(-1) / 2.0                 # 98 keypoints, px in 256x256
+for i, (a, b) in enumerate(zip(kx, ky)):
+    print(f"kp{i}: ({a:.1f}, {b:.1f})")
+```
+
 ## How it converts (litert-torch) — the RTMPose recipe, unchanged
 
 Same model family as the human-pose RTMPose; only the config/checkpoint change to WFLW. The two on-device-only