diff --git a/Gemma/Run_with_Ollama_Python.ipynb b/Gemma/Run_with_Ollama_Python.ipynb
index 48967a6..1294a34 100644
--- a/Gemma/Run_with_Ollama_Python.ipynb
+++ b/Gemma/Run_with_Ollama_Python.ipynb
@@ -1,419 +1,419 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "j4qMNV_533ls"
-   },
-   "source": [
-    "##### Copyright 2024 Google LLC."
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j4qMNV_533ls"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "urf-mQKk348O"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iS5fT77w4ZNj"
+      },
+      "source": [
+        "# Gemma - Run with Ollama Python library\n",
+        "\n",
+        "Author: Sitam Meur\n",
+        "\n",
+        "*   GitHub: [github.com/sitamgithub-MSIT](https://github.com/sitamgithub-MSIT/)\n",
+        "*   X: [@sitammeur](https://x.com/sitammeur)\n",
+        "\n",
+        "Description: This notebook demonstrates how you can run inference on a Gemma model using  [Ollama Python library](https://github.com/ollama/ollama-python). The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/Gemma/Run_with_Ollama_Python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FF6vOV_74aqj"
+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "### Select the Colab runtime\n",
+        "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the Gemma model. In this case, you can use a T4 GPU:\n",
+        "\n",
+        "1. In the upper-right of the Colab window, select **▾ (Additional connection options)**.\n",
+        "2. Select **Change runtime type**.\n",
+        "3. Under **Hardware accelerator**, select **T4 GPU**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4tlnekw44gaq"
+      },
+      "source": [
+        "## Installation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AL7futP_4laS"
+      },
+      "source": [
+        "Install Ollama through the offical installation script."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MuV7cWtcAoSV"
+      },
+      "outputs": [],
+      "source": [
+        "!curl -fsSL https://ollama.com/install.sh | sh"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FpV183Rv6-1P"
+      },
+      "source": [
+        "Install Ollama Python library through the official Python client for Ollama."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0Mrj29SH-3OD"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q ollama"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iNxJFvGIe48_"
+      },
+      "source": [
+        "## Start Ollama\n",
+        "\n",
+        "Start Ollama in background using nohup."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5CX39xKVe9UN"
+      },
+      "outputs": [],
+      "source": [
+        "!nohup ollama serve > ollama.log &"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6YfDqlyo46Rp"
+      },
+      "source": [
+        "## Prerequisites\n",
+        "\n",
+        "*   Ollama should be installed and running. (This was already completed in previous steps.)\n",
+        "*   Pull the gemma2 model to use with the library: `ollama pull gemma2:2b`\n",
+        "    *  See [Ollama.com](https://ollama.com/) for more information on the models available."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oPU5dA1-B5Fn"
+      },
+      "outputs": [],
+      "source": [
+        "import ollama"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NE1AWlucBza_"
+      },
+      "outputs": [],
+      "source": [
+        "ollama.pull('gemma2:2b')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KL5Kc6HaKjmF"
+      },
+      "source": [
+        "## Inference\n",
+        "\n",
+        "Run inference using Ollama Python library."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HN0nrhpmFUUB"
+      },
+      "source": [
+        "### Generate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AC5FsDQuFZfb"
+      },
+      "outputs": [],
+      "source": [
+        "import markdown\n",
+        "from ollama import generate\n",
+        "\n",
+        "# Generate a response to a prompt\n",
+        "response = generate(\"gemma2:2b\", \"Explain the process of photosynthesis.\")\n",
+        "print(response[\"response\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p8v050JkFhuY"
+      },
+      "source": [
+        "#### Streaming Responses\n",
+        "\n",
+        "To enable response streaming, set `stream=True`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jw8UD2v5Fms6"
+      },
+      "outputs": [],
+      "source": [
+        "# Stream the generated response\n",
+        "response = generate('gemma2:2b', 'Explain the process of photosynthesis.', stream=True)\n",
+        "\n",
+        "for part in response:\n",
+        "  print(part['response'], end='', flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "utyVRlIYFvdm"
+      },
+      "source": [
+        "#### Async client\n",
+        "\n",
+        "To make asynchronous requests, use the `AsyncClient` class."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JVvKQE0XF2kh"
+      },
+      "outputs": [],
+      "source": [
+        "import asyncio\n",
+        "import nest_asyncio\n",
+        "from ollama import AsyncClient\n",
+        "\n",
+        "nest_asyncio.apply()\n",
+        "\n",
+        "\n",
+        "async def generate():\n",
+        "    \"\"\"\n",
+        "    Asynchronously generates a response to a given prompt using the AsyncClient.\n",
+        "\n",
+        "    This function creates an instance of AsyncClient and sends a request to generate\n",
+        "    a response for the specified prompt. The response is then printed.\n",
+        "    \"\"\"\n",
+        "    # Create an instance of the AsyncClient\n",
+        "    client = AsyncClient()\n",
+        "\n",
+        "    # Send a request to generate a response to the prompt\n",
+        "    response = await client.generate(\n",
+        "        \"gemma2:2b\", \"Explain the process of photosynthesis.\"\n",
+        "    )\n",
+        "    print(response[\"response\"])\n",
+        "\n",
+        "# Run the generate function\n",
+        "asyncio.run(generate())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WFyi_zzWAwe7"
+      },
+      "source": [
+        "### Chat"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UN20MoSv_S76"
+      },
+      "outputs": [],
+      "source": [
+        "from ollama import chat\n",
+        "\n",
+        "# Start a conversation with the model\n",
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": \"user\",\n",
+        "        \"content\": \"What is keras?\",\n",
+        "    },\n",
+        "]\n",
+        "\n",
+        "# Get the model's response to the message\n",
+        "response = chat(\"gemma2:2b\", messages=messages)\n",
+        "print(response[\"message\"][\"content\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HZGsL1FI7kj8"
+      },
+      "source": [
+        "#### Streaming Responses\n",
+        "\n",
+        "To enable response streaming, set `stream=True`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UdxAuS1C7lm_"
+      },
+      "outputs": [],
+      "source": [
+        "# Stream the chat response\n",
+        "stream = chat(\n",
+        "    model=\"gemma2:2b\",\n",
+        "    messages=[{\"role\": \"user\", \"content\": \"What is keras?\"}],\n",
+        "    stream=True,\n",
+        ")\n",
+        "\n",
+        "for chunk in stream:\n",
+        "    print(chunk[\"message\"][\"content\"], end=\"\", flush=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "czWceNYOEizg"
+      },
+      "source": [
+        "#### Async client + Streaming\n",
+        "\n",
+        "To make asynchronous requests, use the `AsyncClient` class, and for streaming, use `stream=True`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YJmd92z1IUVl"
+      },
+      "outputs": [],
+      "source": [
+        "import asyncio\n",
+        "import nest_asyncio\n",
+        "from ollama import AsyncClient\n",
+        "\n",
+        "nest_asyncio.apply()\n",
+        "\n",
+        "\n",
+        "async def chat():\n",
+        "    \"\"\"\n",
+        "    Asynchronously sends a chat message to the specified model and prints the response.\n",
+        "\n",
+        "    This function sends a message with the role \"user\" and the content \"What is keras?\"\n",
+        "    to the model \"gemma2:2b\" using the AsyncClient's chat method. The response is then streamed.\n",
+        "    \"\"\"\n",
+        "    # Define the message to send to the model\n",
+        "    message = {\"role\": \"user\", \"content\": \"What is keras?\"}\n",
+        "\n",
+        "    # Send the message to the model and print the response\n",
+        "    async for part in await AsyncClient().chat(\n",
+        "        model=\"gemma2:2b\", messages=[message], stream=True\n",
+        "    ):\n",
+        "        print(part[\"message\"][\"content\"], end=\"\", flush=True)\n",
+        "\n",
+        "# Run the chat function\n",
+        "asyncio.run(chat())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cDr8VzvGIXFC"
+      },
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "Congratulations! You have successfully run inference on a Gemma model using the Ollama Python library. You can now integrate this into your Python projects."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "Run_with_Ollama_Python.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "id": "urf-mQKk348O"
-   },
-   "outputs": [],
-   "source": [
-    "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#\n",
-    "# https://www.apache.org/licenses/LICENSE-2.0\n",
-    "#\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "iS5fT77w4ZNj"
-   },
-   "source": [
-    "# Gemma - Run with Ollama Python library\n",
-    "\n",
-    "Author: Sitam Meur\n",
-    "\n",
-    "*   GitHub: [github.com/sitamgithub-MSIT](https://github.com/sitamgithub-MSIT/)\n",
-    "*   X: [@sitammeur](https://x.com/sitammeur)\n",
-    "\n",
-    "Description: This notebook demonstrates how you can run inference on a Gemma model using  [Ollama Python library](https://github.com/ollama/ollama-python). The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.\n",
-    "\n",
-    "<table align=\"left\">\n",
-    "  <td>\n",
-    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/Gemma/Run_with_Ollama_Python.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
-    "  </td>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FF6vOV_74aqj"
-   },
-   "source": [
-    "## Setup\n",
-    "\n",
-    "### Select the Colab runtime\n",
-    "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the Gemma model. In this case, you can use a T4 GPU:\n",
-    "\n",
-    "1. In the upper-right of the Colab window, select **▾ (Additional connection options)**.\n",
-    "2. Select **Change runtime type**.\n",
-    "3. Under **Hardware accelerator**, select **T4 GPU**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "4tlnekw44gaq"
-   },
-   "source": [
-    "## Installation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AL7futP_4laS"
-   },
-   "source": [
-    "Install Ollama through the offical installation script."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MuV7cWtcAoSV"
-   },
-   "outputs": [],
-   "source": [
-    "!curl -fsSL https://ollama.com/install.sh | sh"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FpV183Rv6-1P"
-   },
-   "source": [
-    "Install Ollama Python library through the official Python client for Ollama."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0Mrj29SH-3OD"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install -q ollama"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "iNxJFvGIe48_"
-   },
-   "source": [
-    "## Start Ollama\n",
-    "\n",
-    "Start Ollama in background using nohup."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5CX39xKVe9UN"
-   },
-   "outputs": [],
-   "source": [
-    "!nohup ollama serve > ollama.log &"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6YfDqlyo46Rp"
-   },
-   "source": [
-    "## Prerequisites\n",
-    "\n",
-    "*   Ollama should be installed and running. (This was already completed in previous steps.)\n",
-    "*   Pull the gemma2 model to use with the library: `ollama pull gemma2:2b`\n",
-    "    *  See [Ollama.com](https://ollama.com/) for more information on the models available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "oPU5dA1-B5Fn"
-   },
-   "outputs": [],
-   "source": [
-    "import ollama"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NE1AWlucBza_"
-   },
-   "outputs": [],
-   "source": [
-    "ollama.pull('gemma2:2b')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KL5Kc6HaKjmF"
-   },
-   "source": [
-    "## Inference\n",
-    "\n",
-    "Run inference using Ollama Python library."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HN0nrhpmFUUB"
-   },
-   "source": [
-    "### Generate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "AC5FsDQuFZfb"
-   },
-   "outputs": [],
-   "source": [
-    "import markdown\n",
-    "from ollama import generate\n",
-    "\n",
-    "# Generate a response to a prompt\n",
-    "response = generate(\"gemma2:2b\", \"Explain the process of photosynthesis.\")\n",
-    "print(response[\"response\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "p8v050JkFhuY"
-   },
-   "source": [
-    "#### Streaming Responses\n",
-    "\n",
-    "To enable response streaming, set `stream=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jw8UD2v5Fms6"
-   },
-   "outputs": [],
-   "source": [
-    "# Stream the generated response\n",
-    "response = generate('gemma2:2b', 'Explain the process of photosynthesis.', stream=True)\n",
-    "\n",
-    "for part in response:\n",
-    "  print(part['response'], end='', flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "utyVRlIYFvdm"
-   },
-   "source": [
-    "#### Async client\n",
-    "\n",
-    "To make asynchronous requests, use the `AsyncClient` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JVvKQE0XF2kh"
-   },
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "import nest_asyncio\n",
-    "from ollama import AsyncClient\n",
-    "\n",
-    "nest_asyncio.apply()\n",
-    "\n",
-    "\n",
-    "async def generate():\n",
-    "    \"\"\"\n",
-    "    Asynchronously generates a response to a given prompt using the AsyncClient.\n",
-    "\n",
-    "    This function creates an instance of AsyncClient and sends a request to generate\n",
-    "    a response for the specified prompt. The response is then printed.\n",
-    "    \"\"\"\n",
-    "    # Create an instance of the AsyncClient\n",
-    "    client = AsyncClient()\n",
-    "\n",
-    "    # Send a request to generate a response to the prompt\n",
-    "    response = await client.generate(\n",
-    "        \"gemma2:2b\", \"Explain the process of photosynthesis.\"\n",
-    "    )\n",
-    "    print(response[\"response\"])\n",
-    "\n",
-    "# Run the generate function\n",
-    "asyncio.run(generate())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WFyi_zzWAwe7"
-   },
-   "source": [
-    "### Chat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UN20MoSv_S76"
-   },
-   "outputs": [],
-   "source": [
-    "from ollama import chat\n",
-    "\n",
-    "# Start a conversation with the model\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": \"What is keras?\",\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "# Get the model's response to the message\n",
-    "response = chat(\"gemma2:2b\", messages=messages)\n",
-    "print(response[\"message\"][\"content\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HZGsL1FI7kj8"
-   },
-   "source": [
-    "#### Streaming Responses\n",
-    "\n",
-    "To enable response streaming, set `stream=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UdxAuS1C7lm_"
-   },
-   "outputs": [],
-   "source": [
-    "# Stream the chat response\n",
-    "stream = chat(\n",
-    "    model=\"gemma2:2b\",\n",
-    "    messages=[{\"role\": \"user\", \"content\": \"What is keras?\"}],\n",
-    "    stream=True,\n",
-    ")\n",
-    "\n",
-    "for chunk in stream:\n",
-    "    print(chunk[\"message\"][\"content\"], end=\"\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "czWceNYOEizg"
-   },
-   "source": [
-    "#### Async client + Streaming\n",
-    "\n",
-    "To make asynchronous requests, use the `AsyncClient` class, and for streaming, use `stream=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "YJmd92z1IUVl"
-   },
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "import nest_asyncio\n",
-    "from ollama import AsyncClient\n",
-    "\n",
-    "nest_asyncio.apply()\n",
-    "\n",
-    "\n",
-    "async def chat():\n",
-    "    \"\"\"\n",
-    "    Asynchronously sends a chat message to the specified model and prints the response.\n",
-    "\n",
-    "    This function sends a message with the role \"user\" and the content \"What is keras?\"\n",
-    "    to the model \"gemma2:2b\" using the AsyncClient's chat method. The response is then streamed.\n",
-    "    \"\"\"\n",
-    "    # Define the message to send to the model\n",
-    "    message = {\"role\": \"user\", \"content\": \"What is keras?\"}\n",
-    "\n",
-    "    # Send the message to the model and print the response\n",
-    "    async for part in await AsyncClient().chat(\n",
-    "        model=\"gemma2:2b\", messages=[message], stream=True\n",
-    "    ):\n",
-    "        print(part[\"message\"][\"content\"], end=\"\", flush=True)\n",
-    "\n",
-    "# Run the chat function\n",
-    "asyncio.run(chat())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cDr8VzvGIXFC"
-   },
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "Congratulations! You have successfully run inference on a Gemma model using the Ollama Python library. You can now integrate this into your Python projects."
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "name": "Run_with_Ollama_Python.ipynb",
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
+  "nbformat": 4,
+  "nbformat_minor": 0
 }