Format LiteRT export guide with black and sync notebook

Author: pctablet505

guides/ipynb/litert_export.ipynb

@@ -95,9 +95,7 @@
     "preprocessor = keras_hub.models.Gemma3CausalLMPreprocessor.from_preset(\n",
     "    preset, sequence_length=32\n",
     ")\n",
-    "model = keras_hub.models.Gemma3CausalLM.from_preset(\n",
-    "    preset, preprocessor=preprocessor\n",
-    ")\n",
+    "model = keras_hub.models.Gemma3CausalLM.from_preset(preset, preprocessor=preprocessor)\n",
     "\n",
     "print(f\"Loaded {preset}\")"
    ]
@@ -268,12 +266,14 @@
     "    symmetric=True,\n",
     ")\n",
     "\n",
+    "\n",
     "# Calibrate with a few representative samples\n",
     "# (In production, use real prompts from your validation set)\n",
     "def calibration_data():\n",
     "    for _ in range(10):\n",
     "        yield [np.random.randint(0, 256000, size=(1, 32)).astype(np.int32)]\n",
     "\n",
+    "\n",
     "qt.quantize(calibration_data(), \"gemma3_270m_aieq.tflite\")\n",
     "\n",
     "print(\"Exported ai-edge-quantizer model\")"

guides/litert_export.py

@@ -61,9 +61,7 @@
 preprocessor = keras_hub.models.Gemma3CausalLMPreprocessor.from_preset(
     preset, sequence_length=32
 )
-model = keras_hub.models.Gemma3CausalLM.from_preset(
-    preset, preprocessor=preprocessor
-)
+model = keras_hub.models.Gemma3CausalLM.from_preset(preset, preprocessor=preprocessor)
 
 print(f"Loaded {preset}")
 
@@ -164,12 +162,14 @@
     symmetric=True,
 )
 
+
 # Calibrate with a few representative samples
 # (In production, use real prompts from your validation set)
 def calibration_data():
     for _ in range(10):
         yield [np.random.randint(0, 256000, size=(1, 32)).astype(np.int32)]
 
+
 qt.quantize(calibration_data(), "gemma3_270m_aieq.tflite")
 
 print("Exported ai-edge-quantizer model")