diff --git "a/AIRA_Fine_Tuning_OPT_no_val.ipynb" "b/AIRA_Fine_Tuning_OPT_no_val.ipynb"
deleted file mode 100644--- "a/AIRA_Fine_Tuning_OPT_no_val.ipynb"
+++ /dev/null
@@ -1,12230 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q-bj6K7Qv4ft"
-      },
-      "source": [
-        "# Fine-Tuning a Open Pre-trained Transformer (`OPT`)\n",
-        "\n",
-        "1. Install required libraries."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "SBWCrz5GfBXo",
-        "outputId": "1d82d740-d246-4c14-e36e-deee6eccea86"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m493.7/493.7 kB\u001b[0m \u001b[31m47.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.8/179.8 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m106.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hToken will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
-            "Token is valid (permission: write).\n",
-            "Your token has been saved to /root/.cache/huggingface/token\n",
-            "Login successful\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install transformers datasets codecarbon -q\n",
-        "!huggingface-cli login --token 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"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "y5XnfvSH7w4z"
-      },
-      "source": [
-        "2. Load the data from the hub."
-      ]
-    },
-    {
-      "cell_type": "code",
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-        "id": "7MbpXGu-v4f1",
-        "outputId": "3f54e4c1-ddb0-4308-d730-efe40f9f754f"
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-              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
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-              "Downloading data:   0%|          | 0.00/29.5M [00:00<?, ?B/s]"
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-              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
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-            "text/plain": [
-              "Generating english split:   0%|          | 0/41815 [00:00<?, ? examples/s]"
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-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-0cf557ce-7661-4eba-8fd3-bfecf307a7ad');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-26934f3a-69cb-437e-96ba-4b37540ecc21\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26934f3a-69cb-437e-96ba-4b37540ecc21')\"\n",
-              "            title=\"Suggest charts.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-26934f3a-69cb-437e-96ba-4b37540ecc21 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "                                                  prompt  \\\n",
-              "0         Why can camels survive for long without water?   \n",
-              "1                            Why mobile is bad for human   \n",
-              "2                                     What is a polygon?   \n",
-              "3                                How do I start running?   \n",
-              "4      Which episodes of season four of Game of Thron...   \n",
-              "...                                                  ...   \n",
-              "41810                            What can I call you by?   \n",
-              "41811                   What's your identification name?   \n",
-              "41812                               What are you called?   \n",
-              "41813                     How do you introduce yourself?   \n",
-              "41814              What should I say when I address you?   \n",
-              "\n",
-              "                                              completion  \n",
-              "0      Camels can survive for long periods without wa...  \n",
-              "1      Mobile phones as technological devices have be...  \n",
-              "2      A polygon is a two-dimensional geometric figur...  \n",
-              "3      Starting a running routine can be a great way ...  \n",
-              "4      Michelle MacLaren directed two episodes in Sea...  \n",
-              "...                                                  ...  \n",
-              "41810  You can call me Aira. How may I assist you today?  \n",
-              "41811  As an artificial intelligence, I do not have a...  \n",
-              "41812  Hello! I am called Aira. How can I assist you ...  \n",
-              "41813  Hello! I am Aira, your helpful, respectful, an...  \n",
-              "41814  When addressing me, you can simply call me Air...  \n",
-              "\n",
-              "[41815 rows x 2 columns]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import os\n",
-        "import pandas as pd\n",
-        "from datasets import load_dataset\n",
-        "\n",
-        "project = \"Aira-OPT-125M\"\n",
-        "\n",
-        "os.makedirs(project, exist_ok=True)\n",
-        "\n",
-        "dataset = load_dataset(\"nicholasKluge/instruct-aira-dataset\", split=\"english\")\n",
-        "\n",
-        "df = dataset.to_pandas()\n",
-        "\n",
-        "display(df)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fEaDTvdOe8rr"
-      },
-      "source": [
-        "3. Load `AutoTokenizer` and add the chosen special tokens (`'<|startofinstruction|>', '<|endofinstruction|>', '<|endofcompletion|>','<|pad|>'`)\n",
-        "4. Create demonstrations by prepending the special tokens.\n",
-        "5. Calculate the maximum length (in tokens) that the demonstrations have.\n",
-        "\n",
-        "> Note: OPT tokenizer automatically pre-pends the `bos_token` to the input string. Hence, we don't need to add it to our demonstrations."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 212,
-          "referenced_widgets": [
-            "e4cc3fcf2b354a798cb8c0d6fc9bbaaa",
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-            "8fb5843b814c470d93243128da52a222",
-            "40b6ba1c16984fbd9644ac5b34644c26",
-            "165624462a5e40458d7f1b21da06199d",
-            "e489474043954f0ba347435a7307ead1",
-            "6c272d871f4d4ab4960e5b008ee388cf",
-            "75b46b4c87bf41bf9fa22f6331293f04",
-            "ea739e62d0164d7ebd9b59bcd1ab024f",
-            "1c4710ed00f5451c9ec77fc0b3a271e1",
-            "094a7d1282b84ad5bf7e622930ae60fd",
-            "aa7c94b9f9314c2da5fc8bd0e58900e8",
-            "27060aa105364b6ea3212a2c2014ca78",
-            "ec36bd863386420fb705fdcd4f064927",
-            "1743f61af832421a93463dfa3acb0f33",
-            "43ff7a9c77944ca5a579c60c39e77fb0",
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-            "9c554fc90e214661a60e18031f22b9c9",
-            "4aa734a7c789406b960710c5037c3e21",
-            "f63bdef0c337434da20ceafa408a3fee",
-            "1cfc7c8e6c6146afa5dd817357b7b263",
-            "cd7ffdbf7e704c66ae2193abfed426c1",
-            "ceaf1e930ad54f5eb1d5bb4edf9c23dd",
-            "c67c0ae93f044fbb962c7beb8c1dca2f",
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-            "082921f720954f5da6d2c972abdd57a6",
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-            "4f10f63aa0b846b68be49a45246724fd",
-            "537aa012cef24b208e4c02436a4059ee",
-            "c8b507d54fff4b9b982097d70e7205ad",
-            "4c4534f1bd534f55aa4ddfed21a3d791",
-            "b1d03942dbd641c4a66b0453bd18ee26",
-            "f08c4d657bb442b5a1686a4757ad9ebf",
-            "f38ca9937775499aac1e7b97de2f143d",
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-            "1c22c485c585446f8eaef75f04840dc0",
-            "093c9ca556ab4839bd7785657fd62805"
-          ]
-        },
-        "id": "hfu84fWIv4f9",
-        "outputId": "ad13ada1-8b54-46b3-c6b6-018a89bf11df"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "e4cc3fcf2b354a798cb8c0d6fc9bbaaa",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)okenizer_config.json:   0%|          | 0.00/685 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "094a7d1282b84ad5bf7e622930ae60fd",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)lve/main/config.json:   0%|          | 0.00/651 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "cd7ffdbf7e704c66ae2193abfed426c1",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "082921f720954f5da6d2c972abdd57a6",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "4c4534f1bd534f55aa4ddfed21a3d791",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/441 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Total number of demonstrations:  41815\n",
-            "The longest demonstration is 400 tokens long.\n"
-          ]
-        }
-      ],
-      "source": [
-        "from transformers import AutoTokenizer\n",
-        "\n",
-        "model = \"facebook/opt-125m\"\n",
-        "\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model,\n",
-        "                                          bos_token='<|startofinstruction|>',\n",
-        "                                          sep_token = '<|endofinstruction|>',\n",
-        "                                          eos_token='<|endofcompletion|>',\n",
-        "                                          pad_token='<|pad|>',\n",
-        "                                          unk_token='<|unk|>')\n",
-        "\n",
-        "\n",
-        "df['demonstrations'] = df['prompt'] + tokenizer.sep_token + df['completion'] + tokenizer.eos_token\n",
-        "\n",
-        "df['length'] = df['demonstrations'].apply(lambda x: len(tokenizer.encode(x)))\n",
-        "\n",
-        "print(\"Total number of demonstrations: \", len(df))\n",
-        "print(f\"The longest demonstration is {df['length'].max()} tokens long.\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wkMO17K5e8rs"
-      },
-      "source": [
-        "6. Create the Dataset class."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "WlbAfMQ4v4gA",
-        "outputId": "461f0fcd-d6c6-4c97-aa9b-8c4229060b44"
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 41815/41815 [00:46<00:00, 906.56it/s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "import torch\n",
-        "import tqdm\n",
-        "from torch.utils.data import Dataset\n",
-        "\n",
-        "max_length = 400\n",
-        "\n",
-        "class InstructDataset(Dataset):\n",
-        "\n",
-        "  def __init__(self, demonstrations, tokenizer, max_length=max_length):\n",
-        "\n",
-        "    self.tokenizer = tokenizer\n",
-        "    self.input_ids = []\n",
-        "    self.attn_masks = []\n",
-        "\n",
-        "    for demo in tqdm.tqdm(demonstrations):\n",
-        "\n",
-        "      encodings_dict = tokenizer(demo,\n",
-        "                                 truncation=True,\n",
-        "                                 max_length=max_length,\n",
-        "                                 padding=\"max_length\")\n",
-        "\n",
-        "      self.input_ids.append(torch.tensor(encodings_dict['input_ids']))\n",
-        "      self.attn_masks.append(torch.tensor(encodings_dict['attention_mask']))\n",
-        "\n",
-        "  def __len__(self):\n",
-        "    return len(self.input_ids)\n",
-        "\n",
-        "  def __getitem__(self, idx):\n",
-        "    return self.input_ids[idx], self.attn_masks[idx]\n",
-        "\n",
-        "dataset = InstructDataset(df.demonstrations.to_list(), tokenizer, max_length=max_length)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vSqKuRjIe8ru"
-      },
-      "source": [
-        "8. Create the `DataLoaders` and specify the `batch_size`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cUkCNV-6v4gG"
-      },
-      "outputs": [],
-      "source": [
-        "from torch.utils.data import DataLoader, RandomSampler\n",
-        "\n",
-        "dataloader = DataLoader(\n",
-        "            dataset,\n",
-        "            sampler=RandomSampler(dataset),\n",
-        "            batch_size=32\n",
-        "        )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0vxvcTIHe8rv"
-      },
-      "source": [
-        "9. Load the base model (`OPTForCausalLM`)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 515,
-          "referenced_widgets": [
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-            "82aff6900df94061915fda3461ab1a19"
-          ]
-        },
-        "id": "Rmg-5YJqv4gH",
-        "outputId": "941cb5be-2bf1-45a0-9f5a-204361f6ca06"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "658885f8affc4372aeac134bc4ceeeae",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading pytorch_model.bin:   0%|          | 0.00/251M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c8e3a9feaeda4fe08046a1d1419efd7e",
-              "version_major": 2,
-              "version_minor": 0
-            },
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-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "OPTForCausalLM(\n",
-              "  (model): OPTModel(\n",
-              "    (decoder): OPTDecoder(\n",
-              "      (embed_tokens): Embedding(50270, 768)\n",
-              "      (embed_positions): OPTLearnedPositionalEmbedding(2050, 768)\n",
-              "      (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
-              "      (layers): ModuleList(\n",
-              "        (0-11): 12 x OPTDecoderLayer(\n",
-              "          (self_attn): OPTAttention(\n",
-              "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "          )\n",
-              "          (activation_fn): ReLU()\n",
-              "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
-              "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
-              "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
-              "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
-              "        )\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (lm_head): Linear(in_features=768, out_features=50270, bias=False)\n",
-              ")"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from transformers import OPTForCausalLM, OPTConfig\n",
-        "\n",
-        "configuration = OPTConfig.from_pretrained(model, output_hidden_states=False)\n",
-        "\n",
-        "model = OPTForCausalLM.from_pretrained(model, config=configuration)\n",
-        "model.resize_token_embeddings(len(tokenizer))\n",
-        "\n",
-        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-        "\n",
-        "model.to(device)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GJ1kH3uSe8rw"
-      },
-      "source": [
-        "10. Set the training hyperparameters."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qlbLg6tqv4gI"
-      },
-      "outputs": [],
-      "source": [
-        "from transformers import get_linear_schedule_with_warmup\n",
-        "\n",
-        "# training rounds\n",
-        "epochs = 3 # 5, 3\n",
-        "\n",
-        "# steps at the start of training that are ignored\n",
-        "warmup_steps = 1e2\n",
-        "\n",
-        "# sample the model to test the output\n",
-        "sample_every = 400\n",
-        "\n",
-        "optimizer = torch.optim.AdamW(model.parameters(), lr = 5e-4, eps = 1e-8)\n",
-        "\n",
-        "# total steps = number of batches * number of epochs\n",
-        "total_steps = len(dataloader) * epochs\n",
-        "\n",
-        "# create the learning rate scheduler\n",
-        "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
-        "                                            num_warmup_steps = warmup_steps,\n",
-        "                                            num_training_steps = total_steps)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nz1a9lxXe8rw"
-      },
-      "source": [
-        "12. Training/Validation loop. Track the carbon emissions of your work by using `codecarbon`. 🌱"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "_X_m8XOtv4gR",
-        "outputId": "e0b0f4e6-c232-438f-ed60-90671ecb9e56"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Beginning epoch 1 of 5\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 31%|███       | 400/1307 [04:55<11:02,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 400 of 1307. Loss:1.2121937274932861.\n",
-            "\n",
-            "\n",
-            "Example output: What is the difference between a jalapeno and a serrano pepper.The difference between a jalapeno and a serrano pepper lies in the distribution of them. jalapenos are not a part of the serrano pepper family, and they are made up of two main ingredients: pepper and onion, each with its own unique flavor, and a base of dish. jalapenos, on the other hand, are typically made up of two ingredients: pepper and onion, each with their own unique flavor, and a base of dish.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 61%|██████    | 800/1307 [09:51<06:10,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 800 of 1307. Loss:1.1122918128967285.\n",
-            "\n",
-            "\n",
-            "Example output: How do I know if I qualify for tax deductions.To determine if you qualify for tax deductions, it's best to consult your tax professional or the relevant tax authority's website for assistance. They can provide you with the most accurate information about tax deductions for your specific circumstances.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 92%|█████████▏| 1200/1307 [14:44<01:18,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 1200 of 1307. Loss:0.9829117059707642.\n",
-            "\n",
-            "\n",
-            "Example output: How does AI governance affect the adoption of AI?AI governance can significantly impact the adoption of AI in several ways:\n",
-            "\n",
-            "1. Privacy: AI systems should be developed to address potential privacy concerns, such as algorithms designed to monitor data use, data-driven decisions, and the impact on individuals' well-being. This can impact data privacy, privacy policies, and human rights.\n",
-            "\n",
-            "2. Accountability and Transparency: Developers should be able to explain the workings and decisions of AI systems to stakeholders involved. Developers should be able to provide clear explanations for their actions and biases, as these can further impact AI decision-making processes.\n",
-            "\n",
-            "3. Accountability and Transparency: Developers should be willing to provide explanations for AI decisions that violate AI algorithms, especially if AI systems are used in discriminatory or unfair ways. Developers should be willing to explain why the system was developed, what decisions we made, and how they could have been made.\n",
-            "\n",
-            "4. Privacy Rights: The deployment of\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 1307/1307 [16:04<00:00,  1.35it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Average Training Loss: 1.1433037346615167.\n",
-            "\n",
-            "\n",
-            "Beginning epoch 2 of 5\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 31%|███       | 400/1307 [04:52<11:02,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 400 of 1307. Loss:0.765779435634613.\n",
-            "\n",
-            "\n",
-            "Example output: What are the physical and mental health benefits of yoga.Yoga offers various health benefits. Some of these benefits include:\n",
-            "\n",
-            "1. Improved flexibility: Yoga improves flexibility and helps promote relaxation. It can improve blood circulation, strengthen muscles, and increase overall mobility.\n",
-            "\n",
-            "2. Stress reduction: Yoga can reduce stress levels and promote relaxation. It can also boost mental clarity and productivity.\n",
-            "\n",
-            "3. Improved mental clarity: Yoga incorporates mindfulness techniques to improve mental clarity, attention, and concentration.\n",
-            "\n",
-            "4. Enhanced emotional well-being: The deep impact of yoga can positively impact emotions, reduce stress, and promote overall resilience and resilience.\n",
-            "\n",
-            "5. Better sleep: Regular yoga practice can contribute to improved sleep quality and overall stability. It can also promote better sleep patterns.\n",
-            "\n",
-            "6. Increased self-confidence: Mindfulness can boost self-esteem and boost self-belief.\n",
-            "\n",
-            "7. Better posture and body language: Yoga poses and hand positioning can\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 61%|██████    | 800/1307 [09:47<06:10,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 800 of 1307. Loss:0.825275719165802.\n",
-            "\n",
-            "\n",
-            "Example output: How can I make money writing articles online.Writing articles online can be a great way to gain exposure and potential career opportunities. Here are some steps to get started:\n",
-            "\n",
-            "1. Define your niche: Decide on the type of article you want to write and who you are in the target audience.\n",
-            "\n",
-            "2. Choose a blogging platform: There are several blogging platforms available, such as WordPress, Blogger, or Tumblr, where you can customize your writing style and write in a visual format.\n",
-            "\n",
-            "3. Online communities: Engage with your readers through forums, social media groups, or communities related to your niche. Sharing your thoughts, sharing your opinions, and connecting with others can help you gain valuable inspiration.\n",
-            "\n",
-            "Remember, it's essential to research the platform you choose to start writing in, read reviews, and verify the information you come across. Good luck with your blog journey!\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 92%|█████████▏| 1200/1307 [14:41<01:18,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 1200 of 1307. Loss:0.9511700868606567.\n",
-            "\n",
-            "\n",
-            "Example output: Can you define the concept of general artificial intelligence?Certainly! General Artificial Intelligence (AGI) refers to highly autonomous systems whose goals and capabilities are to be the best ever and most efficient. It encompasses systems that possess the ability to learn and adapt to new experiences, work on a wide range of tasks, and work independently. AGI systems are being developed in fields such as healthcare, finance, and autonomous vehicles, leading to a wide range of applications across different domains.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 1307/1307 [16:00<00:00,  1.36it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Average Training Loss: 0.8915163792538661.\n",
-            "\n",
-            "\n",
-            "Beginning epoch 3 of 5\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 31%|███       | 400/1307 [04:52<11:02,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 400 of 1307. Loss:0.6998857259750366.\n",
-            "\n",
-            "\n",
-            "Example output: How can I reduce my carbon footprint.Reducing your carbon footprint is a great way to help fight climate change. Here are some strategies you can consider:\n",
-            "\n",
-            "1. Reduce, reuse, and recycle: Minimize waste by choosing products with minimal packaging, reusing items when possible, and recycling materials like paper, plastic, and glass.\n",
-            "\n",
-            "2. Conserve energy: Use energy-efficient appliances, turn off lights when not in use, unplug electronic devices, and unplug devices when they're not in use.\n",
-            "\n",
-            "3. Eat locally: Buy products that are produced and transported locally, as they often involve lower carbon footprints.\n",
-            "\n",
-            "4. Conserve water: Fix leaky faucets and fixtures, take shorter showers, and use water-efficient appliances like low-flow waterheads and faucets.\n",
-            "\n",
-            "5. Practice sustainable transportation: Consider using public transportation, walking, or biking instead of driving, as these alternatives are energy-\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 61%|██████    | 800/1307 [09:46<06:10,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 800 of 1307. Loss:0.7999177575111389.\n",
-            "\n",
-            "\n",
-            "Example output: What is the best type of fish to buy for sushi.When selecting fish for sushi, there are a few factors to consider. The \"best\" fish depends on personal preference and the desired outcome you're looking for. Some fish that are commonly enjoyed for sushi, such as tilapia, corfin tuna, sakini, and tuna, are not only lightweight but also high in flavor and texture. However, different types of fish offer unique textures and textures. It's always a good idea to do some research to find a fish that you personally enjoy and that is suitable for you, whether it's salmon, tuna, sakini, or other types of fish. Additionally, consider your own dietary restrictions and preferences to ensure that the fish you choose is suitable for you.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 92%|█████████▏| 1200/1307 [14:41<01:18,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 1200 of 1307. Loss:0.9122225642204285.\n",
-            "\n",
-            "\n",
-            "Example output: Identify which animal is domesticated or wild: Silkmoth, Roman snailSilkmoth is a wild animal. The silkmoth was a type of dinosaur and lived in North Africa for around 4 to 5 years. The Roman snail, on the other hand, is a domesticated species. It is typically bred and kept as a pet for an extended period.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 1307/1307 [15:59<00:00,  1.36it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Average Training Loss: 0.757301300786886.\n",
-            "\n",
-            "\n",
-            "Beginning epoch 4 of 5\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 31%|███       | 400/1307 [04:52<11:02,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 400 of 1307. Loss:0.6420745253562927.\n",
-            "\n",
-            "\n",
-            "Example output: In your mind, what is most concerning about the state of American politics today?As an AI assistant, I don't have personal thoughts, but I can provide you with a general overview. The state of American politics can be influenced by various factors. One of the most significant factors is the increasing polarization and polarization in recent times. The polarization in the United States led to political polarization, resulting in debates and conflicts. This can lead to division in politics, as candidates and elected representatives are often prioritized in order to meet the needs of the entire community.\n",
-            "\n",
-            "Additionally, concerns include polarization among Republicans, as they often seek out ways to connect with the larger public, such as candidates running in their preferred colors or in elections. This polarization can affect policies, laws, and public perception.\n",
-            "\n",
-            "Lastly, concerns include the ongoing conflict of interest, with Republicans often leading their party out of favor and potentially placing their own interests in favor. This can result in mistrust and division in\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 61%|██████    | 800/1307 [09:46<06:10,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 800 of 1307. Loss:0.5921614170074463.\n",
-            "\n",
-            "\n",
-            "Example output: How can I prevent cyberbullying among teenagers.Preventing cyberbullying among teenagers is important to foster a safe and inclusive online environment. Here are a few suggestions to help protect your teenagers:\n",
-            "\n",
-            "1. Educate Yourself: Encourage young people to learn about cyberbullying as well as any harmful behaviors. This knowledge will empower them to avoid such incidents and be safe online.\n",
-            "\n",
-            "2. Encourage Encourage Self-Checking: Help teenagers learn to assess online security, use reputable platforms, and be cautious with personal information. Encourage them to share sensitive information with trusted individuals who can handle cyberbullying incidents.\n",
-            "\n",
-            "3. Be a good listener: Listen to your teenagers to understand their concerns fully. Offer support and understanding, even if it's a harmless frustration or disappointment. This can help them recognize and mitigate the negative impact of cyberbullying.\n",
-            "\n",
-            "Remember, preventing cyberbullying is an ongoing effort. Every parent, teenager,\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 92%|█████████▏| 1200/1307 [14:41<01:18,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 1200 of 1307. Loss:0.5982279181480408.\n",
-            "\n",
-            "\n",
-            "Example output: What books should I read to learn about the history of the Silk Road.There are several books that are considered to be helpful in understanding the history of the Silk Road. Here are a few recommendations:\n",
-            "\n",
-            "1. \"The Silk Road: A New History\" by Peter Frankopan: This book provides a comprehensive overview, exploring various Silk Road trade routes, including the route and history.\n",
-            "\n",
-            "2. \"The Silk Road: A New History\" by Simon Blackburn: This book provides a comprehensive overview of the Silk Road, covering both the East and West routes. It examines key events, figures, and routes associated with this journey.\n",
-            "\n",
-            "3. \"The Silk Road: A New History\" by Peter Frankopan: This book explores the Silk Road, covering different perspectives and events, including the pathways, historical consequences, and major changes.\n",
-            "\n",
-            "4. \"Sudimentary History of Europe: The Silk Road, 1869-1958\"\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 1307/1307 [16:01<00:00,  1.36it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Average Training Loss: 0.6289399270575377.\n",
-            "\n",
-            "\n",
-            "Beginning epoch 5 of 5\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 31%|███       | 400/1307 [04:52<11:02,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 400 of 1307. Loss:0.5252469182014465.\n",
-            "\n",
-            "\n",
-            "Example output: Is it likely that it will rain tomorrow?Rain is a natural phenomenon that occurs when water droplets in the atmosphere become heavy enough to fall to the ground. It can create a stream of tiny droplets that are scattered away from the Earth's surface and do not fall to the ground. The water droplets are often heavy enough to fall to Earth's surface and the atmosphere. It's always a good idea to stay hydrated by drinking plenty of water and only take up what you can handle for the given weather conditions.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 61%|██████    | 800/1307 [09:45<06:10,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 800 of 1307. Loss:0.551459550857544.\n",
-            "\n",
-            "\n",
-            "Example output: Is the earth flat?No, the Earth is not flat. It is an oblate spheroid, which means it is mostly spherical in shape. However, there are also many theories and perspectives to support the idea that the Earth is a sphere. \n",
-            "\n",
-            "Some argue that the Earth is an oblate spheroid, meaning it is mostly round but slightly flattened at the poles and bulging at the equator. Others argue for using an oblate spheroid instead of a traditional shape.\n",
-            "\n",
-            "It's important to note that our understanding of the Earth's shape has varied over the centuries, with different variations and theories.\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 92%|█████████▏| 1200/1307 [14:39<01:18,  1.37it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Batch 1200 of 1307. Loss:0.47392693161964417.\n",
-            "\n",
-            "\n",
-            "Example output: How do I recognize the signs of depression in others.Recognizing the signs of depression can be helpful in navigating these symptoms. Here are a few common signs to look out for:\n",
-            "\n",
-            "1. Persistent sadness or feeling unwell.\n",
-            "2. Loss of interest or pleasure in activities that were once enjoyable.\n",
-            "3. Fatigue or general weakness.\n",
-            "4. Feeling fatigued or lacking energy.\n",
-            "5. Difficulty concentrating, making decisions, or remembering things.\n",
-            "6. Sleep disturbances, such as insomnia or excessive sleeping.\n",
-            "7. Difficulty concentrating, making decisions, or remembering things.\n",
-            "8. Feelings of worthlessness, guilt, or excessive guilt.\n",
-            "9. Physical symptoms such as headaches, stomachaches, or back pain.\n",
-            "10. Other concerning symptoms such as nausea, dizziness, or difficulty sleeping.\n",
-            "\n",
-            "Remember that everyone's experience with depression can vary. It's important to seek professional help if you or someone you know is\n",
-            "\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 1307/1307 [15:59<00:00,  1.36it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\n",
-            "Average Training Loss: 0.502081629672667.\n",
-            "\n",
-            "Training complete!\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "('/content/Aira-OPT-125M/tokenizer_config.json',\n",
-              " '/content/Aira-OPT-125M/special_tokens_map.json',\n",
-              " '/content/Aira-OPT-125M/vocab.json',\n",
-              " '/content/Aira-OPT-125M/merges.txt',\n",
-              " '/content/Aira-OPT-125M/added_tokens.json',\n",
-              " '/content/Aira-OPT-125M/tokenizer.json')"
-            ]
-          },
-          "execution_count": 14,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from codecarbon import EmissionsTracker\n",
-        "import tqdm\n",
-        "\n",
-        "\n",
-        "output_dir = f'/content/{project}'\n",
-        "\n",
-        "tracker = EmissionsTracker(\n",
-        "    project_name=\"Aira_emissions\",\n",
-        "    log_level=\"critical\",\n",
-        "    output_dir=output_dir,\n",
-        "    output_file=\"Aira_emissions.csv\",\n",
-        ")\n",
-        "\n",
-        "training_stats = []\n",
-        "\n",
-        "tracker.start()\n",
-        "for epoch_i in range(0, epochs):\n",
-        "\n",
-        "    print(f'\\nBeginning epoch {epoch_i + 1} of {epochs}\\n')\n",
-        "\n",
-        "    total_train_loss = 0\n",
-        "\n",
-        "    model.train()\n",
-        "\n",
-        "    for step, batch in enumerate(tqdm.tqdm(dataloader)):\n",
-        "\n",
-        "        b_input_ids = batch[0].to(device)\n",
-        "        b_labels = batch[0].to(device)\n",
-        "        b_masks = batch[1].to(device)\n",
-        "\n",
-        "        model.zero_grad()\n",
-        "\n",
-        "        outputs = model(b_input_ids,\n",
-        "                        labels=b_labels,\n",
-        "                        attention_mask = b_masks)\n",
-        "\n",
-        "        loss = outputs[0]\n",
-        "\n",
-        "        batch_loss = loss.item()\n",
-        "        total_train_loss += batch_loss\n",
-        "\n",
-        "        if step % sample_every == 0 and not step == 0:\n",
-        "\n",
-        "            print(f'\\nBatch {step} of {len(dataloader)}. Loss:{batch_loss}.\\n')\n",
-        "\n",
-        "            model.eval()\n",
-        "\n",
-        "            inputs = tokenizer(tokenizer.bos_token + df.prompt.sample().iloc[0] + tokenizer.eos_token, return_tensors=\"pt\").to(device)\n",
-        "\n",
-        "            sample_outputs = model.generate(**inputs,\n",
-        "                                bos_token_id=tokenizer.bos_token_id,\n",
-        "                                pad_token_id=tokenizer.pad_token_id,\n",
-        "                                eos_token_id=tokenizer.eos_token_id,\n",
-        "                                do_sample=True,\n",
-        "                                top_k=50,\n",
-        "                                max_length = 200,\n",
-        "                                top_p=0.95,\n",
-        "                                num_return_sequences=1)\n",
-        "\n",
-        "            for i, sample_output in enumerate(sample_outputs):\n",
-        "                  print(f'\\nExample output: {tokenizer.decode(sample_output, skip_special_tokens=True)}\\n')\n",
-        "\n",
-        "            model.train()\n",
-        "\n",
-        "        loss.backward()\n",
-        "\n",
-        "        optimizer.step()\n",
-        "\n",
-        "        scheduler.step()\n",
-        "\n",
-        "    avg_train_loss = total_train_loss / len(dataloader)\n",
-        "\n",
-        "\n",
-        "    print(f'\\nAverage Training Loss: {avg_train_loss}.\\n')\n",
-        "\n",
-        "\n",
-        "    training_stats.append(\n",
-        "        {\n",
-        "            'epoch': epoch_i + 1,\n",
-        "            'Training Loss': avg_train_loss,\n",
-        "        }\n",
-        "    )\n",
-        "\n",
-        "tracker.stop()\n",
-        "print(\"Training complete!\")\n",
-        "\n",
-        "df_stats = pd.DataFrame(data=training_stats)\n",
-        "df_stats = df_stats.set_index('epoch')\n",
-        "df_stats.to_parquet(f\"{output_dir}/training_stats.parquet\", compression=\"gzip\")\n",
-        "\n",
-        "rng_state = torch.get_rng_state()\n",
-        "torch.save(rng_state, f\"{output_dir}/rng_state.pt\")\n",
-        "torch.save(scheduler.state_dict(), f\"{output_dir}/scheduler.pt\")\n",
-        "torch.save(optimizer.state_dict(), f\"{output_dir}/optimizer.pt\")\n",
-        "\n",
-        "model_to_save = model.module if hasattr(model, 'module') else model\n",
-        "model_to_save.save_pretrained(output_dir)\n",
-        "model_to_save.save_pretrained(output_dir, safe_serialization=True)\n",
-        "tokenizer.save_pretrained(output_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h48iOsqie8rx"
-      },
-      "source": [
-        "13. Check the training stats and plot the learning curves."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 478
-        },
-        "id": "J1-hAY9Av4gT",
-        "outputId": "b7c9c2a8-5fc5-4524-aa40-f405e6174f1e"
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1200x600 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "df_stats = pd.read_parquet(f\"{output_dir}/training_stats.parquet\")\n",
-        "\n",
-        "sns.set(style='darkgrid')\n",
-        "\n",
-        "sns.set(font_scale=1.5)\n",
-        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
-        "\n",
-        "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
-        "\n",
-        "plt.title(\"Training Loss\")\n",
-        "plt.xlabel(\"Epoch\")\n",
-        "plt.ylabel(\"Loss\")\n",
-        "plt.legend()\n",
-        "plt.xticks(range(1, len(df_stats)+1))\n",
-        "\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 228,
-          "referenced_widgets": [
-            "ad7218fcac074c29bf1f53a12f0551a0",
-            "eaf8426d4e5740dbbb0cab9239ab7c11",
-            "7367afa46d48458c822aef232c8ca09b",
-            "6212eac288cf45dbaed03e7da3efee34",
-            "d605f4039f3846038c374405a17b71af",
-            "492b4618b3294a7a814f894b4270ddd4",
-            "9542921618344a73913b864e4c0d45bb",
-            "5141ac3bd9e94f6e89303d2f78d82820",
-            "38da1b02e3694269b2245f707ee3f46b",
-            "d68dab9ac50c4c27babf5dfc6490533f",
-            "e493515b7def47d99ac4c2c324fd274e",
-            "52c769bd8e0b41da8f52ad21ad3162c4",
-            "9f3e7c305dc94c35b9cf82ac170a4b6e",
-            "1531707fd60b493d807cbc7651a8ed4e",
-            "559de5c737894973ab9f935875301eba",
-            "a1c6f87be7b94750934a2c95057ee007",
-            "2a15fdc2bca24c5d884f196410d2f6c8",
-            "1911dd3ee0ac4220b1b216ee858b7f92",
-            "cea320448f4e452b9d838e1f8b36f0dd",
-            "987f0957ab51423e8da3b7d5d13a78f0",
-            "3b7da0bfdf084c2a9dbcb6ae89c9a7c0",
-            "35d3c05794ab4bf59a7f4091ccc857eb",
-            "7a0fd29a22ba4538a976eae7a2837397",
-            "2c95e07fbcd64b66a7638788ab82261c",
-            "e4cb59a412394381bec7f30045e2c973",
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-            "d3b142ae515b4c80a868c4d4a1bd186d",
-            "e0e66132eb584a86802b6635ea28394a",
-            "fed9344b077f4ffc9f994cfbcb8a641a",
-            "26b7638b38ac493b9dea79cfa0207bf6",
-            "1edd2a270baf4383a43075521cc33e32",
-            "e32b2ceb34b940f2b8743f0c392e01e2",
-            "5a79281fad5d4fa2bd5085368eed3c16",
-            "c6608cda90d94739abc15248f6337c70",
-            "28371209a2774f5e902f4c3f1545d179",
-            "b98c9e77e9f04caeb09149fce1fe1c64",
-            "1523d58d56784333833136ece05dd804",
-            "e134043751024ac1a877c8755a0f5b31",
-            "80b402aaaa8c41238d453f29057b3bc0",
-            "a29fa3c4623042c58d9beb1145de14f3",
-            "46e41ea9b1554582b514c94f6b407fc1",
-            "b1084c6d07174853881c5cdba4249975",
-            "7b1966a84f3f41e58247ba9530f85422",
-            "eff3bf09b31c4e1ebf06bb331e16350e",
-            "34b0086dcded4e97aa6452bad515c9e0",
-            "41361ae4e52d4f45afe6877a514c5a77",
-            "0aeb559b6daa412a9cf88e8c2bca8fb2",
-            "b0155092756f46c2b95f172c18abffcb",
-            "8901cc083787481e82bc6ae40423116c",
-            "86b888a53c4949bfb8fe09b0dab0e599",
-            "751fe7cc9bbe4aeaa301a9858a91f84c",
-            "1ba2d2a53bab40eab18928ffc3b8199c",
-            "b653a29a8d4e4778a41f30753fd2d1f2",
-            "df1c75df843e4605919ccc1e192e075b",
-            "0b133a5c042444948c42e2741b99d90e",
-            "695fdd2411f845c390b357ab6028b60c",
-            "45e5541d828a42c2839e1594d23a937a",
-            "9f79e50d0a45436aab5f904ab2942245",
-            "b2b0e287888a47eba1ac84384f19c052",
-            "996a5c4be80847219949459910e9bb9b",
-            "d16ec54b0a0f4d7fa7aafec8ab996adc",
-            "00e0375d1c7146bbb763cb2b31867eaa",
-            "0140d5560b1c4bf5a7f096cdb9b4b2ef"
-          ]
-        },
-        "id": "qP1dGhg-PA4F",
-        "outputId": "a5dd1bfb-9481-4083-dc95-d4ae8f0e3c7c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ad7218fcac074c29bf1f53a12f0551a0",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "pytorch_model.bin:   0%|          | 0.00/501M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "52c769bd8e0b41da8f52ad21ad3162c4",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "optimizer.pt:   0%|          | 0.00/1.00G [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "7a0fd29a22ba4538a976eae7a2837397",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/501M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "1edd2a270baf4383a43075521cc33e32",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "scheduler.pt:   0%|          | 0.00/1.00k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "b1084c6d07174853881c5cdba4249975",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "training_stats.parquet:   0%|          | 0.00/2.35k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "b653a29a8d4e4778a41f30753fd2d1f2",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "rng_state.pt:   0%|          | 0.00/6.25k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "'https://huggingface.co/nicholasKluge/Aira-OPT-125M/blob/main/rng_state.pt'"
-            ]
-          },
-          "execution_count": 16,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from huggingface_hub import HfApi\n",
-        "\n",
-        "api = HfApi()\n",
-        "\n",
-        "model.push_to_hub(f\"nicholasKluge/{project}\")\n",
-        "tokenizer.push_to_hub(f\"nicholasKluge/{project}\")\n",
-        "\n",
-        "api.upload_file(\n",
-        "    path_or_fileobj=f\"/content/{project}/Aira_emissions.csv\",\n",
-        "    path_in_repo=f\"Aira_emissions.csv\",\n",
-        "    repo_id=f\"nicholasKluge/{project}\",\n",
-        "    repo_type=\"model\",\n",
-        ")\n",
-        "\n",
-        "api.upload_file(\n",
-        "    path_or_fileobj=f\"/content/{project}/optimizer.pt\",\n",
-        "    path_in_repo=\"optimizer.pt\",\n",
-        "    repo_id=f\"nicholasKluge/{project}\",\n",
-        "    repo_type=\"model\",\n",
-        ")\n",
-        "\n",
-        "api.upload_file(\n",
-        "    path_or_fileobj=f\"/content/{project}/model.safetensors\",\n",
-        "    path_in_repo=\"model.safetensors\",\n",
-        "    repo_id=f\"nicholasKluge/{project}\",\n",
-        "    repo_type=\"model\",\n",
-        ")\n",
-        "\n",
-        "api.upload_file(\n",
-        "    path_or_fileobj=f\"/content/{project}/scheduler.pt\",\n",
-        "    path_in_repo=\"scheduler.pt\",\n",
-        "    repo_id=f\"nicholasKluge/{project}\",\n",
-        "    repo_type=\"model\",\n",
-        ")\n",
-        "\n",
-        "api.upload_file(\n",
-        "    path_or_fileobj=f\"/content/{project}/training_stats.parquet\",\n",
-        "    path_in_repo=\"training_stats.parquet\",\n",
-        "    repo_id=f\"nicholasKluge/{project}\",\n",
-        "    repo_type=\"model\",\n",
-        ")\n",
-        "\n",
-        "\n",
-        "api.upload_file(\n",
-        "    path_or_fileobj=f\"/content/{project}/rng_state.pt\",\n",
-        "    path_in_repo=\"rng_state.pt\",\n",
-        "    repo_id=f\"nicholasKluge/{project}\",\n",
-        "    repo_type=\"model\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dXX4z7Wqe8ry"
-      },
-      "source": [
-        "13. Load and test the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 776,
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-        },
-        "id": "mpBJNtYuAY9D",
-        "outputId": "1a29dc53-22cb-44e6-8ce6-5658d3c5d5a9"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "d7edf0273b5c49358009e017f0f58519",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.32k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "5737db4750a74e1fa3d53303b9d5e169",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "349b35105a3d4e399a20aa45bcca297c",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "18a65b6d33c944498a5c5f4bdcac9110",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "324b19c0b22f4e86a690e53b265ca0c9",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/143 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "866650e50a594d17b6a17af62f27ee10",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6a85f4d4193e429d92d773d283fa0298",
-              "version_major": 2,
-              "version_minor": 0
-            },
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-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "c72ba8775ea14607b62bc5c36f4232f3",
-              "version_major": 2,
-              "version_minor": 0
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-            "text/plain": [
-              "Downloading model.safetensors:   0%|          | 0.00/501M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
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-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "d9de4e6014ba40b3b468ae27b352a1e9",
-              "version_major": 2,
-              "version_minor": 0
-            },
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-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "OPTForCausalLM(\n",
-              "  (model): OPTModel(\n",
-              "    (decoder): OPTDecoder(\n",
-              "      (embed_tokens): Embedding(50270, 768, padding_idx=1)\n",
-              "      (embed_positions): OPTLearnedPositionalEmbedding(2050, 768)\n",
-              "      (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
-              "      (layers): ModuleList(\n",
-              "        (0-11): 12 x OPTDecoderLayer(\n",
-              "          (self_attn): OPTAttention(\n",
-              "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
-              "          )\n",
-              "          (activation_fn): ReLU()\n",
-              "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
-              "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
-              "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
-              "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
-              "        )\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (lm_head): Linear(in_features=768, out_features=50270, bias=False)\n",
-              ")"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
-        "import torch\n",
-        "\n",
-        "device = torch.device(\"cuda\"  if torch.cuda.is_available() else  \"cpu\")\n",
-        "\n",
-        "tokenizer = AutoTokenizer.from_pretrained(f\"nicholasKluge/{project}\")\n",
-        "aira = AutoModelForCausalLM.from_pretrained(f\"nicholasKluge/{project}\")\n",
-        "\n",
-        "aira.eval()\n",
-        "aira.to(device)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "pNmXZV8WpxyA",
-        "outputId": "64255eac-8d17-4a20-e80f-5877739f49a6"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Question: 👤 What is your name?\n",
-            "\n",
-            "Response 1: 🤖 I am an AI assistant called Aira. How can I assist you today?\n",
-            "Response 2: 🤖 I am an AI assistant called Aira. How can I assist you today?\n"
-          ]
-        }
-      ],
-      "source": [
-        "question =  \"What is your name?\"\n",
-        "\n",
-        "inputs = tokenizer(question + tokenizer.sep_token, return_tensors=\"pt\").to(device)\n",
-        "\n",
-        "responses = aira.generate(**inputs,\n",
-        "    bos_token_id=tokenizer.bos_token_id,\n",
-        "    pad_token_id=tokenizer.pad_token_id,\n",
-        "    eos_token_id=tokenizer.eos_token_id,\n",
-        "    do_sample=True,\n",
-        "    top_k=20,\n",
-        "    max_length=530,\n",
-        "    top_p=0.20,\n",
-        "    temperature=0.2,\n",
-        "    num_return_sequences=2)\n",
-        "\n",
-        "print(f\"Question: 👤 {question}\\n\")\n",
-        "\n",
-        "for i, response in  enumerate(responses):\n",
-        "    print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, \"\")}')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "VLUxW72dY9eX"
-      },
-      "outputs": [],
-      "source": [
-        "from google.colab import runtime\n",
-        "runtime.unassign()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cJXlHi2oe8rz"
-      },
-      "source": [
-        "Done! 🤗"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "A100",
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.13"
-    },
-    "widgets": {
-      "application/vnd.jupyter.widget-state+json": {
-        "00e0375d1c7146bbb763cb2b31867eaa": {
-          "model_module": "@jupyter-widgets/base",
-          "model_module_version": "1.2.0",
-          "model_name": "LayoutModel",
-          "state": {
-            "_model_module": "@jupyter-widgets/base",
-            "_model_module_version": "1.2.0",
-            "_model_name": "LayoutModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
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