diff --git a/site/en/gemma/docs/codegemma/keras_quickstart.ipynb b/site/en/gemma/docs/codegemma/keras_quickstart.ipynb
index e220465a3..286e41ea4 100644
--- a/site/en/gemma/docs/codegemma/keras_quickstart.ipynb
+++ b/site/en/gemma/docs/codegemma/keras_quickstart.ipynb
@@ -21,7 +21,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "cellView": "form",
         "id": "tuOe1ymfHZPu"
@@ -88,7 +88,7 @@
         "* A 7B instruction-tuned code model\n",
         "* A 2B model, trained specifically for code infilling and open-ended generation.\n",
         "\n",
-        "This guide walks you through using the CodeGemma 2B model with KerasNLP for a code completion task.\n"
+        "This guide walks you through using the CodeGemma 2B model with KerasHub for a code completion task.\n"
       ]
     },
     {
@@ -161,7 +161,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "id": "DrBoa_Urw9Vx"
       },
@@ -185,13 +185,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
         "id": "KWOQ2sJocj-w"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m792.1/792.1 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m53.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
       "source": [
-        "!pip install -q -U keras-nlp"
+        "!pip install -q -U keras-hub\n",
+        "!pip install -q -U keras"
       ]
     },
     {
@@ -216,7 +227,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "id": "ww83zI9ToPso"
       },
@@ -233,18 +244,18 @@
       "source": [
         "### Import packages\n",
         "\n",
-        "Import Keras and KerasNLP."
+        "Import Keras and KerasHub."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "id": "oQkqsyE1a2YD"
       },
       "outputs": [],
       "source": [
-        "import keras_nlp\n",
+        "import keras_hub\n",
         "import keras\n",
         "\n",
         "# Run at half precision.\n",
@@ -259,30 +270,72 @@
       "source": [
         "### Load Model\n",
         "\n",
-        "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/){:.external}. In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
+        "KerasHub provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/){:.external}. In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
         "\n",
         "Create the model using the `from_preset` method:"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "id": "yygIK9DEIldp"
       },
       "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/config.json...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 785/785 [00:00<00:00, 1.64MB/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/model.weights.h5...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 4.67G/4.67G [00:50<00:00, 99.2MB/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/tokenizer.json...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 591/591 [00:00<00:00, 946kB/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/assets/tokenizer/vocabulary.spm...\n"
+          ]
+        },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/config.json...\n",
-            "100%|██████████| 554/554 [00:00<00:00, 1.41MB/s]\n",
-            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/model.weights.h5...\n",
-            "100%|██████████| 4.67G/4.67G [05:06<00:00, 16.4MB/s]\n",
-            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/tokenizer.json...\n",
-            "100%|██████████| 401/401 [00:00<00:00, 382kB/s]\n",
-            "Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/assets/tokenizer/vocabulary.spm...\n",
-            "100%|██████████| 4.04M/4.04M [00:01<00:00, 2.41MB/s]\n"
+            "100%|██████████| 4.04M/4.04M [00:00<00:00, 43.1MB/s]\n"
           ]
         },
         {
@@ -301,19 +354,19 @@
         {
           "data": {
             "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-              "┃<span style=\"font-weight: bold\"> Tokenizer (type)                                   </span>┃<span style=\"font-weight: bold\">                                             Vocab # </span>┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                   │                                             <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
-              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                                                  </span>┃<span style=\"font-weight: bold\">                                   Config </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>)                              │                      Vocab size: <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
+              "└───────────────────────────────────────────────────────────────┴──────────────────────────────────────────┘\n",
               "</pre>\n"
             ],
             "text/plain": [
-              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-              "┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type)                                  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                            Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                   │                                             \u001b[38;5;34m256,000\u001b[0m │\n",
-              "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                                                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                                  Config\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m)                              │                      Vocab size: \u001b[38;5;34m256,000\u001b[0m │\n",
+              "└───────────────────────────────────────────────────────────────┴──────────────────────────────────────────┘\n"
             ]
           },
           "metadata": {},
@@ -410,7 +463,7 @@
         }
       ],
       "source": [
-        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"code_gemma_2b_en\")\n",
+        "gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(\"code_gemma_2b_en\")\n",
         "gemma_lm.summary()"
       ]
     },
@@ -448,7 +501,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "id": "tGby-fi8n-Hv"
       },
@@ -471,7 +524,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "id": "k1ousdBnr2j8"
       },
@@ -507,7 +560,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "metadata": {
         "id": "N7UlgjSt5QnF"
       },
@@ -542,7 +595,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "metadata": {
         "id": "aae5GHrdpj2_"
       },
@@ -556,7 +609,7 @@
               "'<|fim_prefix|>import <|fim_suffix|>if __name__ == \"__main__\":\\n    sys.exit(0)<|fim_middle|>sys\\n<|file_separator|>'"
             ]
           },
-          "execution_count": 12,
+          "execution_count": 11,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -582,7 +635,7 @@
       "source": [
         "## Summary\n",
         "\n",
-        "This tutorial walked you through using CodeGemma to infill code based on the surrounding context. Next, check out the [AI Assisted Programming with CodeGemma and KerasNLP notebook](https://ai.google.dev/gemma/docs/codegemma/code_assist_keras) for more examples on how you can use CodeGemma.\n",
+        "This tutorial walked you through using CodeGemma to infill code based on the surrounding context. Next, check out the [AI Assisted Programming with CodeGemma and KerasHub notebook](https://ai.google.dev/gemma/docs/codegemma/code_assist_keras) for more examples on how you can use CodeGemma.\n",
         "\n",
         "Also refer to The [CodeGemma model card](https://ai.google.dev/gemma/docs/codegemma/model_card) for the technical specs of the CodeGemma models.\n"
       ]