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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "copyright"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2023 Google LLC\n",
+        "#\n",
+        "# 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": "title:generic,gcp"
+      },
+      "source": [
+        "# Vertex AI LLM and streaming prediction\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/llm_streaming_prediction.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "        <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official//prediction/llm_streaming_prediction.ipynb\">\n",
+        "        <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "        </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official//prediction/llm_streaming_prediction.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      Open in Vertex AI Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>\n",
+        "<br/><br/><br/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "overview:mlops"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "\n",
+        "This tutorial demonstrates how to use Vertex AI LLM for making streaming predictions on large language models.\n",
+        "\n",
+        "Learn more about [Vertex AI Language Models](https://cloud.google.com/python/docs/reference/aiplatform/latest/vertexai.language_models.TextGenerationModel#vertexai_language_models_TextGenerationModel_predict_streaming)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "objective:mlops,stage2,get_started_automl_training"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to use Vertex AI LLM to download pretrained LLM model, make predictions and finetuning the model.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI LLM`\n",
+        "- `Vertex AI Prediction`\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Load a pretrained text generation model.\n",
+        "- Make a non-streaming prediction\n",
+        "- Load a pretrained text generation model, which supports streaming.\n",
+        "- Make a streaming prediction\n",
+        "- Load a pretrained chat model.\n",
+        "- Do a local interactive chat session.\n",
+        "- Do a batch prediction with a text generation model.\n",
+        "- Do a batch prediction with a text embedding model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:flowers,icn"
+      },
+      "source": [
+        "### Model\n",
+        "\n",
+        "The pre-trained models used for this tutorial are from Vertex AI LLM repository. The models used are:\n",
+        "\n",
+        "- text-bison\n",
+        "- chat-bison\n",
+        "- text-embedding-gecko"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fb3451ce8e47"
+      },
+      "source": [
+        "### Costs\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "- Vertex AI\n",
+        "- Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "install_mlops"
+      },
+      "source": [
+        "## Installations\n",
+        "\n",
+        "Install the packages required for executing this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NQ6SdFxji8ks"
+      },
+      "outputs": [],
+      "source": [
+        "! pip3 install --upgrade --quiet google-cloud-aiplatform"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "restart"
+      },
+      "source": [
+        "### Colab only: Uncomment the following cell to restart the kernel"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D-ZBOjErv5mM"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
+        "\n",
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yfEglUHQk9S3"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, try the following:\n",
+        "* Run `gcloud config list`.\n",
+        "* Run `gcloud projects list`.\n",
+        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "set_project_id"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the project id\n",
+        "! gcloud config set project {PROJECT_ID} --quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "region"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gcp_authenticate"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
+        "\n",
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated.\n",
+        "\n",
+        "**2. Local JupyterLab instance, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ce6043da7b33"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0367eac06a10"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "21ad4dbb4a61"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c13224697bfb"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bucket:mbsdk"
+      },
+      "source": [
+        "### Create a Cloud Storage bucket\n",
+        "\n",
+        "Create a storage bucket to store intermediate artifacts such as datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bucket"
+      },
+      "outputs": [],
+      "source": [
+        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "autoset_bucket"
+      },
+      "source": [
+        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "91c46850b49b"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "setup_vars"
+      },
+      "source": [
+        "### Set up variables\n",
+        "\n",
+        "Next, set up some variables used throughout the tutorial.\n",
+        "### Import libraries and define constants"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8696271b314f"
+      },
+      "outputs": [],
+      "source": [
+        "import vertexai\n",
+        "from vertexai.preview.language_models import TextGenerationModel"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oiraqRwdooE0"
+      },
+      "outputs": [],
+      "source": [
+        "vertexai.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YCriCYpOjZv0"
+      },
+      "source": [
+        "## Text generation\n",
+        "\n",
+        "First, you load from Vertex AI LLM, the text-bison model. Once loaded, you make a prediction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fkAkKn-ijJR5"
+      },
+      "outputs": [],
+      "source": [
+        "model = TextGenerationModel.from_pretrained(\"google/text-bison@001\")\n",
+        "\n",
+        "print(\n",
+        "    model.predict(\n",
+        "        \"What is the best recipe for banana bread? Recipe:\",\n",
+        "        # \"Brainstorm some ideas combining VR and fitness:\",\n",
+        "        # Optional:\n",
+        "        # max_output_tokens=128,\n",
+        "        # temperature=0,\n",
+        "        # top_p=1,\n",
+        "        # top_k=5,\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vltcd5jb5WGe"
+      },
+      "source": [
+        "## Streaming prediction\n",
+        "\n",
+        "Next, you load from Vertex AI LLM, a version of the text-bison model that support streaming prediction. Once loaded, you make a prediction. Note, how each iteration that model returns the next successive output in the prediction response."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Z72f5GTm1weU"
+      },
+      "outputs": [],
+      "source": [
+        "import datetime\n",
+        "\n",
+        "text_generation_model = TextGenerationModel.from_pretrained(\"text-bison\")\n",
+        "\n",
+        "print(\"Start: \", datetime.datetime.now())\n",
+        "for response in text_generation_model.predict_streaming(\n",
+        "    prompt=\"Count to 100\", max_output_tokens=1000\n",
+        "):\n",
+        "    print(datetime.datetime.now())\n",
+        "    print(response)\n",
+        "print(\"End: \", datetime.datetime.now())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wl2AZceWjXoy"
+      },
+      "source": [
+        "## Chat\n",
+        "\n",
+        "Next, you load from Vertex AI LLM, a version of the chat-bison model  Once loaded, you do an interactive chat session."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lco7I7X_Ch2C"
+      },
+      "outputs": [],
+      "source": [
+        "from vertexai.language_models import ChatModel, InputOutputTextPair\n",
+        "\n",
+        "chat_model2 = ChatModel.from_pretrained(\"google/chat-bison@001\")\n",
+        "\n",
+        "chat2 = chat_model2.start_chat(\n",
+        "    # Optional:\n",
+        "    context=\"My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.\",\n",
+        "    examples=[\n",
+        "        InputOutputTextPair(\n",
+        "            input_text=\"Who do you work for?\",\n",
+        "            output_text=\"I work for Ned.\",\n",
+        "        ),\n",
+        "        InputOutputTextPair(\n",
+        "            input_text=\"What do I like?\",\n",
+        "            output_text=\"Ned likes watching movies.\",\n",
+        "        ),\n",
+        "    ],\n",
+        ")\n",
+        "\n",
+        "print(chat2.send_message(\"Are my favorite movies based on a book series?\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "G64BrDoxC-K3"
+      },
+      "outputs": [],
+      "source": [
+        "print(chat2.send_message(\"When where these books published?\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tmWzgmkUzgzU"
+      },
+      "source": [
+        "## Text embedding\n",
+        "\n",
+        "Next, you load from Vertex AI LLM, a version of the text-embedding-gecko model, and then make an embedding request."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Eara9dykkJTA"
+      },
+      "outputs": [],
+      "source": [
+        "from vertexai.language_models import TextEmbeddingModel\n",
+        "\n",
+        "model = TextEmbeddingModel.from_pretrained(\"google/textembedding-gecko@001\")\n",
+        "embeddings = model.get_embeddings([\"What is life?\"])\n",
+        "for embedding in embeddings:\n",
+        "    vector = embedding.values\n",
+        "    print(len(vector))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7gaFptssn03c"
+      },
+      "source": [
+        "## Batch prediction\n",
+        "\n",
+        "### text-bison model\n",
+        "\n",
+        "Now, you do a batch prediction job with a pretrained text-bison model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xHl1XlTpqIFY"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = \"gs://cloud-samples-data/vertex-ai/prediction/llm/test_table.jsonl\"\n",
+        "destination_uri_prefix = f\"{BUCKET_URI}/text-bison@001_\"\n",
+        "! gsutil cp -r gs://cloud-samples-data/vertex-ai/prediction/llm/text-bison@001_/ {destination_uri_prefix}\n",
+        "\n",
+        "\n",
+        "from vertexai.language_models import TextGenerationModel\n",
+        "\n",
+        "text_generation_model = TextGenerationModel.from_pretrained(\"text-bison\")\n",
+        "batch_job_1 = text_generation_model.batch_predict(\n",
+        "    dataset=dataset,\n",
+        "    destination_uri_prefix=destination_uri_prefix,\n",
+        "    model_parameters={},\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3d8c7fc4a4a7"
+      },
+      "source": [
+        "### text-embedding-gecko model\n",
+        "\n",
+        "Next, you do a batch prediction job with a pretrained text-embedding-gecko model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DfdSZT4SoPxf"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = \"gs://cloud-samples-data/vertex-ai/prediction/llm/embedding_input.jsonl\"\n",
+        "\n",
+        "destination_uri_prefix = f\"{BUCKET_URI}/textembedding-gecko@001_\"\n",
+        "\n",
+        "from vertexai.preview.language_models import TextEmbeddingModel\n",
+        "\n",
+        "text_embedding_model = TextEmbeddingModel.from_pretrained(\"textembedding-gecko@001\")\n",
+        "batch_job_2 = text_embedding_model.batch_predict(\n",
+        "    dataset=dataset,\n",
+        "    destination_uri_prefix=destination_uri_prefix,\n",
+        "    # Optional:\n",
+        "    model_parameters={},\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Dt6Cyr9B0ZOT"
+      },
+      "source": [
+        "## Tuning\n",
+        "\n",
+        "Now, you fine tune with a pretrained text-bison model, and make a prediction with the fine tuned model. Your input data is in JSONL format and stored in a Cloud Storage location."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WxmUoYfC0aJI"
+      },
+      "outputs": [],
+      "source": [
+        "model3 = TextGenerationModel.from_pretrained(\"google/text-bison@001\")\n",
+        "\n",
+        "model3.list_tuned_model_names()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Bb4HhsLc0ha4"
+      },
+      "outputs": [],
+      "source": [
+        "tuning_job = model3.tune_model(\n",
+        "    training_data=\"gs://cloud-samples-data/vertex-ai/prediction/llm/q_a_train_with_context.jsonl\",\n",
+        "    # Optional:\n",
+        "    train_steps=1,\n",
+        "    tuning_job_location=\"europe-west4\",\n",
+        "    tuned_model_location=\"us-central1\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Xf7tWnMU03ZY"
+      },
+      "outputs": [],
+      "source": [
+        "tuned_model = tuning_job.get_tuned_model()\n",
+        "\n",
+        "print(tuned_model.predict(\"Tell me some ideas combining VR and fitness:\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7UqoxmAd28QZ"
+      },
+      "outputs": [],
+      "source": [
+        "model3.list_tuned_model_names()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uB7RC4Sd2-vK"
+      },
+      "outputs": [],
+      "source": [
+        "tuned_model4 = model3.get_tuned_model(\n",
+        "    tuned_model_name=model3.list_tuned_model_names()[0]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "asNjLVy47_Ox"
+      },
+      "outputs": [],
+      "source": [
+        "print(tuned_model4.predict(\"Brainstorm some ideas combining VR and fitness:\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wai6L3fQowJ_"
+      },
+      "source": [
+        "## Tuning from pandas DataFrame\n",
+        "\n",
+        "Now, you fine tune with a pretrained text-bison model, and make a prediction with the fine tuned model. Your input data is an in-memory pandas DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lUsZKDS3nIcJ"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas\n",
+        "\n",
+        "training_data = pandas.DataFrame(\n",
+        "    data=[\n",
+        "        {\"input_text\": \"Input 1\", \"output_text\": \"Output 1\"},\n",
+        "        {\"input_text\": \"Input 2\", \"output_text\": \"Output 2\"},\n",
+        "        {\"input_text\": \"Input 3\", \"output_text\": \"Output 3\"},\n",
+        "        {\"input_text\": \"Input 4\", \"output_text\": \"Output 4\"},\n",
+        "        {\"input_text\": \"Input 5\", \"output_text\": \"Output 5\"},\n",
+        "        {\"input_text\": \"Input 6\", \"output_text\": \"Output 6\"},\n",
+        "        {\"input_text\": \"Input 7\", \"output_text\": \"Output 7\"},\n",
+        "        {\"input_text\": \"Input 8\", \"output_text\": \"Output 8\"},\n",
+        "        {\"input_text\": \"Input 9\", \"output_text\": \"Output 9\"},\n",
+        "        {\"input_text\": \"Input 10\", \"output_text\": \"Output 10\"},\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "training_data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RMReD76mnAOC"
+      },
+      "outputs": [],
+      "source": [
+        "model4 = TextGenerationModel.from_pretrained(\"google/text-bison@001\")\n",
+        "\n",
+        "tuning_job = model4.tune_model(\n",
+        "    training_data=training_data,\n",
+        "    # Optional:\n",
+        "    train_steps=10,\n",
+        "    tuning_job_location=\"europe-west4\",\n",
+        "    tuned_model_location=\"us-central1\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KWCaCpTDnL1h"
+      },
+      "outputs": [],
+      "source": [
+        "tuned_model = tuning_job.get_tuned_model()\n",
+        "\n",
+        "print(tuned_model.predict(\"Tell me some ideas combining VR and fitness:\"))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b1bb76638fb5"
+      },
+      "source": [
+        "## Cleanup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c41c153770e6"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "batch_job_1.delete()\n",
+        "batch_job_2.delete()\n",
+        "\n",
+        "delete_bucket = False\n",
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil rm -rf {BUCKET_URI}"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "llm_streaming_prediction.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}