diff --git "a/AIRA_FineTuning.ipynb" "b/AIRA_FineTuning.ipynb"
--- "a/AIRA_FineTuning.ipynb"
+++ "b/AIRA_FineTuning.ipynb"
@@ -19,7 +19,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "SBWCrz5GfBXo",
-        "outputId": "34bbeb89-a5d5-4ad4-f87f-a87bffc735a2"
+        "outputId": "c3897ecc-56b3-48fc-b9cb-1f8bb2809fbe"
       },
       "outputs": [
         {
@@ -28,12 +28,18 @@
           "text": [
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Collecting transformers\n",
-            "  Downloading transformers-4.30.1-py3-none-any.whl (7.2 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m77.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
+            "  Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m78.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting datasets\n",
+            "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m47.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting codecarbon\n",
+            "  Downloading codecarbon-2.2.4-py3-none-any.whl (176 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m176.0/176.0 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
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@@ -41,52 +47,56 @@
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             "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
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-            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
-            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
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-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
-            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
-            "Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.1\n",
-            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
-            "Collecting codecarbon\n",
-            "  Downloading codecarbon-2.2.3-py3-none-any.whl (174 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.1/174.1 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hCollecting arrow (from codecarbon)\n",
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+            "Collecting xxhash (from datasets)\n",
+            "  Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
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+            "\u001b[?25hCollecting multiprocess (from datasets)\n",
+            "  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
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+            "\u001b[?25hCollecting pynvml (from codecarbon)\n",
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+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.6.3)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.16)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.5.7)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
             "Requirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from arrow->codecarbon) (2.8.2)\n",
-            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->codecarbon) (2022.7.1)\n",
-            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas->codecarbon) (1.22.4)\n",
-            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->codecarbon) (1.26.15)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->codecarbon) (2022.12.7)\n",
-            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->codecarbon) (2.0.12)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->codecarbon) (3.4)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->arrow->codecarbon) (1.16.0)\n",
-            "Installing collected packages: fuzzywuzzy, pynvml, arrow, codecarbon\n",
-            "Successfully installed arrow-1.2.3 codecarbon-2.2.3 fuzzywuzzy-0.18.0 pynvml-11.5.0\n"
+            "Installing collected packages: tokenizers, safetensors, fuzzywuzzy, xxhash, pynvml, dill, multiprocess, huggingface-hub, arrow, transformers, codecarbon, datasets\n",
+            "Successfully installed arrow-1.2.3 codecarbon-2.2.4 datasets-2.13.1 dill-0.3.6 fuzzywuzzy-0.18.0 huggingface-hub-0.15.1 multiprocess-0.70.14 pynvml-11.5.0 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2 xxhash-3.2.0\n"
           ]
         }
       ],
       "source": [
-        "!pip install transformers\n",
-        "!pip install codecarbon"
+        "!pip install transformers datasets codecarbon"
       ]
     },
     {
@@ -104,17 +114,308 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 467
+          "height": 702,
+          "referenced_widgets": [
+            "34e200081e97446c8cd7856137d2ed39",
+            "3e9822a5f0d046728156b5a0c3e3c082",
+            "6c0f0718a27944a49df4feeb5eb7f0a6",
+            "94ce8d2f7bf24a3cb3487ea88094beda",
+            "579d1717cad5475e9f772e84bdbabfc1",
+            "1120e50796944b58af0f4dbd639482d2",
+            "53f08c1231c74d14a81bd9485ba96deb",
+            "5456362a6850407db4bebc1ded3cbe1a",
+            "f3aaec6e08094e19bff0f311e6a64158",
+            "485ffa98fc3a473096e4af30be5aa60a",
+            "78fa4f26ce174a108c43033b0dd5f2b6",
+            "158650cfa6d14e00ab4825953fabe91e",
+            "16d718b78ba240d7b930ff422c9cbc67",
+            "1092f96bf4b2456893e925e26d200d57",
+            "02a61e2a34b3458a8b9313608812ca7d",
+            "a6d3f7f2b090401f9cba84ccb242c5ec",
+            "f1f0f70d41b64c4892da63d0aa6ec3c9",
+            "edb44c8f27af400cab27572250043b11",
+            "9b37e0bc0c91405a9ce5a925fb46ba7e",
+            "8e07a97bcc0548fbb8681ad1bbd1dcbe",
+            "857f65e34e8a4cee92857a61fb3febb2",
+            "e0f81131b319404d8f48a1ba4edac5bc",
+            "a39380581e804f5fb96018c6058ec4ac",
+            "29e76265e50842c3b3aa3e68825c1cc9",
+            "4622678ef1c440eaba2b21dabaef8a4f",
+            "dac43af304db4a2da06c7b18201413f9",
+            "00485f3c3dbc40b5be49c8f4fd49c3d7",
+            "095609fe73cb4e9cbd5838422855c951",
+            "7bc85c49a9154847b716aeaaf47d4558",
+            "aba8b6027ea447a8b927b8674a3c78c7",
+            "54995b0a7e01403d816d4c5dcea3ec39",
+            "9cfd52a8054f436fa406dd12657d697c",
+            "380d234d63644d31b43286726d4346e0",
+            "1962a1b46f7d4c0bac70d45435b1b0b8",
+            "476c1fd9e1644ceda44275a4630cff71",
+            "c56b207b5c554646b09ee80b6c6f87cc",
+            "295050c536e04607be4944fa8827a66d",
+            "7848eb314e39456faac551687946b12b",
+            "9d9e50a4097f4aa38e07d69728e47298",
+            "7b3c5ea2a594482a9d6e60510d9e335f",
+            "8c988598365c414a9fb20a7f6b37365e",
+            "0377a5e70f2540878f435c3c13b915fc",
+            "20ec5a844483409f8df19a37e41cb0c9",
+            "d5b5905850d74cf8b1e8f2625658b13e",
+            "380e286c3a844a91aa34bb28439cc430",
+            "5771f51108984384a0d4a458320f74f3",
+            "c0ecbbd2b8c3474f848a017f34a1bbaa",
+            "41260544bc6c4d25986e0819ae970219",
+            "09b056bc424b4322ab06b9a5adc53f8e",
+            "bb2e0757b2f549b4af6695711ab9b3f0",
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+            "c5b458f29c6d4932926f33171d21922d",
+            "fae6fa9f6f6b4273851008bde9e21e9a",
+            "838feb07922241239a29e6640de20f72",
+            "dc91135f04184e29817d1d8f26637e17"
+          ]
         },
         "id": "7MbpXGu-v4f1",
-        "outputId": "7d155c3b-13f2-417d-a5b9-6d0dce08a5d7"
+        "outputId": "f0085c70-8f57-47f3-c719-16f95e40fa40"
       },
       "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "34e200081e97446c8cd7856137d2ed39",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/5.12k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/nicholasKluge___parquet/nicholasKluge--fine-tuning-instruct-aira-4077fd700c38fc36/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7...\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "158650cfa6d14e00ab4825953fabe91e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data files:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a39380581e804f5fb96018c6058ec4ac",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/1.71M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1962a1b46f7d4c0bac70d45435b1b0b8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/13.5M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "380e286c3a844a91aa34bb28439cc430",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d8e16763af2a46f0852402390bb7ce96",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "76153959dfc74095b0412eb96a2a6b57",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Extracting data files:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3cca4d9f55024c8e950a1f011d2a9e71",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating aira_english split:   0%|          | 0/9183 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "093d5f2f31d248e49b53e13e34a39d19",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating aira_instruct_english split:   0%|          | 0/48666 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "02b3bb2540344873bdc2621a283e4b9d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating aira_instruct_portuguese split:   0%|          | 0/48571 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6831afd511fc4a468023a637abef4ffc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating aira_portuguese split:   0%|          | 0/9204 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/nicholasKluge___parquet/nicholasKluge--fine-tuning-instruct-aira-4077fd700c38fc36/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7. Subsequent calls will reuse this data.\n"
+          ]
+        },
         {
           "data": {
             "text/html": [
               "\n",
-              "  <div id=\"df-b575c0da-5a72-42cd-861a-36728a31f263\">\n",
+              "  <div id=\"df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -141,28 +442,28 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>Which is a species of fish? Tope or Rope</td>\n",
-              "      <td>Tope</td>\n",
+              "      <td>Como é ser a última pessoa na terra?</td>\n",
+              "      <td>Você pensaria que o mundo seria seu para explo...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>Why can camels survive for long without water?</td>\n",
-              "      <td>Camels use the fat in their humps to keep them...</td>\n",
+              "      <td>Você pode explicar o conceito de Anarquismo?</td>\n",
+              "      <td>O anarquismo é uma filosofia política que defe...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>Alice's parents have three daughters: Amy, Jes...</td>\n",
-              "      <td>The name of the third daughter is Alice</td>\n",
+              "      <td>Qual é a melhor maneira de pendurar uma pintur...</td>\n",
+              "      <td>A melhor maneira de pendurar uma pintura com s...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>Who gave the UN the land in NY to build their HQ</td>\n",
-              "      <td>John D Rockerfeller</td>\n",
+              "      <td>Qual é a relação entre o problema de controle ...</td>\n",
+              "      <td>A noção de controle se manifesta quando contem...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>Why mobile is bad for human</td>\n",
-              "      <td>We are always engaged one phone which is not g...</td>\n",
+              "      <td>Identifique qual instrumento é corda ou percus...</td>\n",
+              "      <td>Liuqin é corda, Nagareh é percussão.</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -170,35 +471,35 @@
               "      <td>...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>53129</th>\n",
-              "      <td>How do computers communicate and network with ...</td>\n",
-              "      <td>Computers communicate and network with each ot...</td>\n",
+              "      <th>48566</th>\n",
+              "      <td>Qual é a história da luta de sumô.</td>\n",
+              "      <td>A luta de sumô é uma forma antiga de arte marc...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>53130</th>\n",
-              "      <td>How are websites different from web applications?</td>\n",
-              "      <td>Websites and web applications are similar in t...</td>\n",
+              "      <th>48567</th>\n",
+              "      <td>Como posso melhorar falar em público.</td>\n",
+              "      <td>Comece estando preparado. Pesquise o tópico, p...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>53131</th>\n",
-              "      <td>What is open-source software and its benefits?</td>\n",
-              "      <td>Open-source software is software that is made ...</td>\n",
+              "      <th>48568</th>\n",
+              "      <td>Como faço para navegar até um determinado loca...</td>\n",
+              "      <td>Para navegar até um determinado local usando t...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>53132</th>\n",
-              "      <td>What is a cookie and how is it used in web bro...</td>\n",
-              "      <td>A cookie is a small piece of data that a websi...</td>\n",
+              "      <th>48569</th>\n",
+              "      <td>Quais são algumas dicas de etiqueta de mensage...</td>\n",
+              "      <td>Apresente-se - Deixe a pessoa saber quem você ...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>53133</th>\n",
-              "      <td>What is cloud storage and its advantages for d...</td>\n",
-              "      <td>Cloud storage is a service that allows you to ...</td>\n",
+              "      <th>48570</th>\n",
+              "      <td>Estou pensando em participar de um concurso de...</td>\n",
+              "      <td>Eu recomendaria que você se concentrasse em su...</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>53134 rows × 2 columns</p>\n",
+              "<p>48571 rows × 2 columns</p>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b575c0da-5a72-42cd-861a-36728a31f263')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -249,12 +550,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-b575c0da-5a72-42cd-861a-36728a31f263 button.colab-df-convert');\n",
+              "          document.querySelector('#df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-b575c0da-5a72-42cd-861a-36728a31f263');\n",
+              "          const element = document.querySelector('#df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -277,32 +578,32 @@
             ],
             "text/plain": [
               "                                                  prompt  \\\n",
-              "0               Which is a species of fish? Tope or Rope   \n",
-              "1         Why can camels survive for long without water?   \n",
-              "2      Alice's parents have three daughters: Amy, Jes...   \n",
-              "3       Who gave the UN the land in NY to build their HQ   \n",
-              "4                            Why mobile is bad for human   \n",
+              "0                   Como é ser a última pessoa na terra?   \n",
+              "1           Você pode explicar o conceito de Anarquismo?   \n",
+              "2      Qual é a melhor maneira de pendurar uma pintur...   \n",
+              "3      Qual é a relação entre o problema de controle ...   \n",
+              "4      Identifique qual instrumento é corda ou percus...   \n",
               "...                                                  ...   \n",
-              "53129  How do computers communicate and network with ...   \n",
-              "53130  How are websites different from web applications?   \n",
-              "53131     What is open-source software and its benefits?   \n",
-              "53132  What is a cookie and how is it used in web bro...   \n",
-              "53133  What is cloud storage and its advantages for d...   \n",
+              "48566                 Qual é a história da luta de sumô.   \n",
+              "48567              Como posso melhorar falar em público.   \n",
+              "48568  Como faço para navegar até um determinado loca...   \n",
+              "48569  Quais são algumas dicas de etiqueta de mensage...   \n",
+              "48570  Estou pensando em participar de um concurso de...   \n",
               "\n",
               "                                              completion  \n",
-              "0                                                   Tope  \n",
-              "1      Camels use the fat in their humps to keep them...  \n",
-              "2                The name of the third daughter is Alice  \n",
-              "3                                    John D Rockerfeller  \n",
-              "4      We are always engaged one phone which is not g...  \n",
+              "0      Você pensaria que o mundo seria seu para explo...  \n",
+              "1      O anarquismo é uma filosofia política que defe...  \n",
+              "2      A melhor maneira de pendurar uma pintura com s...  \n",
+              "3      A noção de controle se manifesta quando contem...  \n",
+              "4                   Liuqin é corda, Nagareh é percussão.  \n",
               "...                                                  ...  \n",
-              "53129  Computers communicate and network with each ot...  \n",
-              "53130  Websites and web applications are similar in t...  \n",
-              "53131  Open-source software is software that is made ...  \n",
-              "53132  A cookie is a small piece of data that a websi...  \n",
-              "53133  Cloud storage is a service that allows you to ...  \n",
+              "48566  A luta de sumô é uma forma antiga de arte marc...  \n",
+              "48567  Comece estando preparado. Pesquise o tópico, p...  \n",
+              "48568  Para navegar até um determinado local usando t...  \n",
+              "48569  Apresente-se - Deixe a pessoa saber quem você ...  \n",
+              "48570  Eu recomendaria que você se concentrasse em su...  \n",
               "\n",
-              "[53134 rows x 2 columns]"
+              "[48571 rows x 2 columns]"
             ]
           },
           "metadata": {},
@@ -313,9 +614,11 @@
         "import pandas as pd\n",
         "from datasets import load_dataset\n",
         "\n",
-        "dataset = load_dataset(\"nicholasKluge/fine-tuning-instruct-aira\", split=\"aira_instruct_english\")\n",
+        "dataset = load_dataset(\"nicholasKluge/fine-tuning-instruct-aira\", split='aira_instruct_portuguese')\n",
         "\n",
         "df = dataset.to_pandas()\n",
+        "df = df.sample(frac=1)\n",
+        "df = df.reset_index(drop=True)\n",
         "\n",
         "display(df)"
       ]
@@ -337,56 +640,106 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 241,
+          "height": 249,
           "referenced_widgets": [
-            "cd7347fa726445d394c474ecdcd722f5",
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-            "27174e961c764f5c9c549dc9d2972670",
-            "967f86de457447be875d09e85b343242",
-            "053a5571006e41a6aef0b77fa1e36faa",
-            "621fe101bbfb41a09148346855b940bf",
-            "d74a865eb33a46daa004a8e270fd3a5d",
-            "120d61a1947446bbb007302398eb9606",
-            "87770c1808654d7d97642e7a66a38aac",
-            "32b18e36b9024f1d94f38440b1984455",
-            "db63d0aab8e444a28d827dd96067635a",
-            "5803df0c4976405c99c756a5e687cc90",
-            "9e22ec2b93524dda84f88b5fef7f5c2a",
-            "14a867b9db464260be3bed64c225c412",
-            "241225d9398e4660a407eb856500a58e",
-            "e90b5b1082c147c5917a45ce3bee81a7",
-            "1cc20edfc4cc4adfb981fd30cf241b6b",
-            "d2911fcf195940bfa5de8b51593e0522",
-            "1b3dd18c2ca9423d9b2a3b8e0486447d",
-            "b70c83376ad7470aa26fe948175be35c",
-            "eb6a35ee97a240bb9c3cfa27a78bc1dc",
-            "e0464c04c0f848cfb6af2652adb8af66",
-            "8b5f90795192467da0843b23eeb60dc2",
-            "e530a4b1974240e39c178c12771b0e1e",
-            "bd91b647889c48a3802ee0799f2d6a80",
-            "c3530939d43b4d5eabc6dafb4b62cb1c",
-            "b9d223416be348b19c0ad543d308576f",
-            "a3f33b3a1ecc4ca890dde23d9345a73d",
-            "6153dcb6691b4ffaa819961284b22d69",
-            "28d859aaedd84e07affe428793505964",
-            "716067896aba419ead0616e88f4f2dce",
-            "67d5ea32ed8c449f9804e4a32a5c2e99"
+            "443c675b8bd84607aaab840247c5545b",
+            "a24ed74c448449a8b664cbd90196d619",
+            "951f6012a0024154a37315926060771e",
+            "2e338173edce4e9f85721160e21953e0",
+            "76851dcba39e4ce29c3af6d3a8b1b921",
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+            "bf20230be54c49a99d424f9d425126e9",
+            "8a3ee7d864294179935fa0fdbe2f39f5",
+            "bf99c1050a3f463a99ab20bf092d590f",
+            "cecb1b5f2ac741408870249832258d9d",
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+            "ed64ff192edc43039c046ab16586b8d3",
+            "7efd91c0df7a4e49ad9e0d78bbc9da6a",
+            "e008fd683b1e468493f715d131bf74b2",
+            "e5673df10cff4db8bc52494f21ac3293",
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+            "4011ca1817d84cdf8affe6471af9a2ea",
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+            "dcc963ee0bf742a9bb15eca4fa4b0eb2",
+            "c4ac8065b06f4612a0dd8fcdfe7dba2b",
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+            "ca14d5905436432daabe2919e1146478",
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+            "fd89e21c891d46f1b46ecf7c273a7f71",
+            "f5acb7f98ddd4684937fe3ea12caa8f1",
+            "fd8b7adfa675427182bf34a6a6049c96",
+            "845d2d22ca134c8ea15a7ec87c02c801",
+            "519affc31a4140299e35b58d2f5cb71d",
+            "791b7be413f14f9395592e2db713ddd1",
+            "dca90772f4f84fe1aa7236b9362f322b",
+            "408cfb44d5414bcf9eac364c4379c0e8",
+            "fd45b12d73014ad599b418d9a49562f3",
+            "6d665b94f7594e778dc01fd2146b2d7c",
+            "93f86b1501ef46d083e0e23e787600aa",
+            "ea37914cd2424e69851d93685029004b",
+            "e3c6915e5f504ce2b834d91878c22e9f",
+            "dc099648ade5422e8a55110a6a966456"
           ]
         },
         "id": "hfu84fWIv4f9",
-        "outputId": "4269ccc5-e854-4095-dafd-cc5c40d0f12b"
+        "outputId": "b5743a8d-78a4-4e12-de10-bea1b374df38"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "cd7347fa726445d394c474ecdcd722f5",
+              "model_id": "443c675b8bd84607aaab840247c5545b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/850k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ed64ff192edc43039c046ab16586b8d3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/508k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dcc963ee0bf742a9bb15eca4fa4b0eb2",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
+              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/120 [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -395,12 +748,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "db63d0aab8e444a28d827dd96067635a",
+              "model_id": "2f860322641b46c7b7b0ff54bdd40685",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+              "Downloading (…)okenizer_config.json:   0%|          | 0.00/92.0 [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -409,12 +762,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "e0464c04c0f848cfb6af2652adb8af66",
+              "model_id": "845d2d22ca134c8ea15a7ec87c02c801",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading (…)lve/main/config.json:   0%|          | 0.00/718 [00:00<?, ?B/s]"
+              "Downloading (…)lve/main/config.json:   0%|          | 0.00/666 [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -431,16 +784,16 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Total number of demonstrations:  53134\n",
-            "The longest demonstration is 300 tokens long.\n"
+            "Total number of demonstrations:  48571\n",
+            "The longest demonstration is 324 tokens long.\n"
           ]
         }
       ],
       "source": [
         "from transformers import GPT2Tokenizer\n",
         "\n",
-        "model = \"gpt2-medium\" # \"gpt2\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\", \"pierreguillou/gpt2-small-portuguese\"\n",
-        "model_size = \"355M\" # \"124M\", \"355M\", \"774M\", and \"1.5B\", \"PT-124M\"\n",
+        "model = \"pierreguillou/gpt2-small-portuguese\" # \"gpt2\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\", \"pierreguillou/gpt2-small-portuguese\"\n",
+        "model_size = \"PT-124M\" # \"124M\", \"355M\", \"774M\", and \"1.5B\", \"PT-124M\"\n",
         "\n",
         "tokenizer = GPT2Tokenizer.from_pretrained(model,\n",
         "                                          bos_token='<|startoftext|>',\n",
@@ -519,15 +872,15 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "-IOfa2PEv4gD",
-        "outputId": "83b162aa-a00e-4736-cc9e-0ceb005db30e"
+        "outputId": "8461dd32-ffd9-4584-a265-f9481baee350"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Number of training samples: 47,820\n",
-            "Number of validation samples: 5,314\n"
+            "Number of training samples: 43,713\n",
+            "Number of validation samples: 4,858\n"
           ]
         }
       ],
@@ -567,14 +920,14 @@
         "train_dataloader = DataLoader(\n",
         "            train_dataset,\n",
         "            sampler=RandomSampler(train_dataset),\n",
-        "            batch_size=20 # 32, 20, 8, 6\n",
+        "            batch_size=32 # 32, 20, 8, 4\n",
         "        )\n",
         "\n",
         "# validation data loader doesn't need randomization\n",
         "validation_dataloader=DataLoader(\n",
         "            val_dataset,\n",
         "            sampler=SequentialSampler(val_dataset),\n",
-        "            batch_size=20\n",
+        "            batch_size=32\n",
         "        )"
       ]
     },
@@ -593,59 +946,34 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 550,
+          "height": 517,
           "referenced_widgets": [
-            "d3706d2eefcd41ec89f3f0247719ec92",
-            "817964cc90334605902c6f13d4e94bcb",
-            "d2669f9ffdfe404b944100abf125d437",
-            "4971c12962724535bf221fac26ee6938",
-            "40c7558bdd57406abf170b0b4600f2cc",
-            "e6062f15b1af4d838e90aaf5a65748fb",
-            "0a394b51e4de479bbc019c02fa6bb9ab",
-            "caac8742661d4238a06d1ab65add439a",
-            "3fa7d040728843de8812d298154673bc",
-            "dc632fb86d7a4b4c93b7315eef2e7a48",
-            "e1ce84fd7f9045158aefd3ec7359e0a9",
-            "317ef8edd3b44443bce2c1f7f0ed6d4b",
-            "376ff945cd5f4705b6db9c747aa7e062",
-            "b952e087a32c46b7a4aee6a06ebe62d5",
-            "c4911938e5c64ca098bbd0d32ac65870",
-            "fd4bb55839b443c9baf35c37f0033337",
-            "c0c83e1a879a4c1387bd6e353514fdfa",
-            "71c676fba8144996b6749aefc46f63fc",
-            "fbd280fe0da8457e8e83f40bc0bf5fdc",
-            "d62519fa2ae14084ba1aac280a44088c",
-            "39b34f03b78b41f18b50bffdfd6993ba",
-            "b78c2ba8ef6248f6b3d2f0b8f2f31f7c"
+            "eff22b21655241d181443ac7bfa1e70a",
+            "8790ed1eb51543278e0c069b0b951d98",
+            "0f5196fc47ed4b148937b9029812dab1",
+            "4f3aee9bbcdb4f239ed5395b130faf07",
+            "a5cc4fdd4d3e4954853e7090ff332bdc",
+            "8e450d0f99684a29856d262b4350694c",
+            "776567de650f4925921578f0b9414dfe",
+            "df9f8573d0d9476fbf6025f7d1ad3146",
+            "b59bac7b693d421cae391e4b9ceb18ef",
+            "fbf913f79be947a5bab1b2fa729f0241",
+            "642dcbab44674aacb34462b3cf32f6f7"
           ]
         },
         "id": "Rmg-5YJqv4gH",
-        "outputId": "bccf9dea-1980-4234-f26d-e08c586399b5"
+        "outputId": "61181abe-9d4d-4ccd-c2d7-ffd52d8cdcb3"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "d3706d2eefcd41ec89f3f0247719ec92",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading model.safetensors:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "317ef8edd3b44443bce2c1f7f0ed6d4b",
+              "model_id": "eff22b21655241d181443ac7bfa1e70a",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
+              "Downloading pytorch_model.bin:   0%|          | 0.00/510M [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -656,19 +984,19 @@
             "text/plain": [
               "GPT2LMHeadModel(\n",
               "  (transformer): GPT2Model(\n",
-              "    (wte): Embedding(50259, 1024)\n",
-              "    (wpe): Embedding(1024, 1024)\n",
+              "    (wte): Embedding(50259, 768)\n",
+              "    (wpe): Embedding(1024, 768)\n",
               "    (drop): Dropout(p=0.1, inplace=False)\n",
               "    (h): ModuleList(\n",
-              "      (0-23): 24 x GPT2Block(\n",
-              "        (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+              "      (0-11): 12 x GPT2Block(\n",
+              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
               "        (attn): GPT2Attention(\n",
               "          (c_attn): Conv1D()\n",
               "          (c_proj): Conv1D()\n",
               "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
               "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
               "        )\n",
-              "        (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
               "        (mlp): GPT2MLP(\n",
               "          (c_fc): Conv1D()\n",
               "          (c_proj): Conv1D()\n",
@@ -677,13 +1005,13 @@
               "        )\n",
               "      )\n",
               "    )\n",
-              "    (ln_f): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
               "  )\n",
-              "  (lm_head): Linear(in_features=1024, out_features=50259, bias=False)\n",
+              "  (lm_head): Linear(in_features=768, out_features=50259, bias=False)\n",
               ")"
             ]
           },
-          "execution_count": 9,
+          "execution_count": 8,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -718,22 +1046,22 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "MJA-WOoHZePn",
-        "outputId": "7d0895a3-450f-427b-ea97-dc514306407b"
+        "outputId": "15530ae0-f41c-4f58-bf9a-378123835a74"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Number of transformer blocks in the model:  24\n",
-            "Number of transformer blocks to  un-freeze:  8\n",
-            "Number of frozen layers: 193\n",
-            "Number of trainable layers: 99\n"
+            "Number of transformer blocks in the model:  12\n",
+            "Number of transformer blocks to  un-freeze:  6\n",
+            "Number of frozen layers: 73\n",
+            "Number of trainable layers: 75\n"
           ]
         }
       ],
       "source": [
-        "UNFREEZE_LAST_N = 8 #6, 8, 12, 12\n",
+        "UNFREEZE_LAST_N = 6 #6, 8, 9, 12\n",
         "\n",
         "print(\"Number of transformer blocks in the model: \", model.config.n_layer)\n",
         "print(\"Number of transformer blocks to  un-freeze: \", UNFREEZE_LAST_N)\n",
@@ -780,13 +1108,13 @@
         "from transformers import get_linear_schedule_with_warmup\n",
         "\n",
         "# training rounds\n",
-        "epochs = 3 # 5, 3, 2, 1\n",
+        "epochs = 5 # 5, 3, 2, 2\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 = 300\n",
+        "sample_every = 400\n",
         "\n",
         "optimizer = torch.optim.AdamW(model.parameters(), lr = 5e-4, eps = 1e-8)\n",
         "\n",
@@ -816,420 +1144,446 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "_X_m8XOtv4gR",
-        "outputId": "d3fed0ff-df94-4c24-ef0c-4a4ebe299640"
+        "outputId": "3c1993c1-d3f9-46b2-cd5f-401f5c7f7eb0"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Beginning epoch 1 of 3\n"
+            "\n",
+            "Beginning epoch 1 of 5\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 13%|█▎        | 300/2391 [04:13<29:02,  1.20it/s]"
+            " 29%|██▉       | 400/1367 [03:29<08:18,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 300 of 2391. Loss:0.8311468958854675.\n",
-            "Example output: I want to learn mathematics from a beginner level. Where should I begin.There are plenty of mathematics problems that you may not have the time or resources to solve. Take a look at the following list of questions:\n",
-            "\n",
-            "10\n",
-            "\n",
-            "1. What is a polynomial?\n",
             "\n",
-            "2. What is a trigonomial?\n",
-            "3. What is an eigendecid?\n",
+            "Batch 400 of 1367. Loss:0.8450407981872559.\n",
             "\n",
-            "4. What is the real square?\n",
             "\n",
-            "5. What is a logarithmic square?\n"
+            "Example output: Como faço para manter um carro corretamente.Comece avaliando o carro como ele funciona no dia e avaliando sua durabilidade e estabilidade.\n",
+            "2. Se você possui um carro e deseja substituir o carro com um novo, você deve escolher o melhor ajuste, como usar um limpadores ou amortecedores ou um novo filtro elétrico.\n",
+            "3. Se você não for um bom carro, encontre maneiras de trocar de gasolina e reduzir o consumo de combustível, como usar um motor elétrico ou usar carros de baixo consumo.\n",
+            "4. Se você não deseja substituir o carro com um novo, encontre maneiras de trocar de óleo ou substituir a transmissão.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 25%|██▌       | 600/2391 [08:26<24:52,  1.20it/s]"
+            " 59%|█████▊    | 800/1367 [06:58<04:52,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 600 of 2391. Loss:0.6423898935317993.\n",
-            "Example output: How many years have passed since you were born?The age of the world has ended with the rise of AI, machine learning, and data-driven decision making. These advancements have allowed us to navigate a new era of abundance in which we no longer possess ownership over the resources we have allocated to life and are instead reliant on machines to provide us with the most efficient and cost-effective solutions.\n",
             "\n",
-            "While the technology behind these transformations remains uncertain, there are a few promising trends that point in the direction of AI's ability to revolutionize our daily lives. These trends include advances in machine learning, personalized healthcare and finance, and augmented and virtual reality.\n"
+            "Batch 800 of 1367. Loss:0.9422707557678223.\n",
+            "\n",
+            "\n",
+            "Example output: Como posso meditar de forma eficaz.Uma maneira de meditar de forma eficaz é praticar técnicas de meditação guiada por atenção plena. Pratique respiração profunda e respiração profunda até atingir sua concentração no momento presente. Tente encontrar uma maneira de se concentrar em sua respiração, percebendo sua respiração e pensamentos, concentrando-se em perceber seus próprios estados mentais. Além disso, certifique-se de praticar técnicas de meditação de mantra para manter um equilíbrio de momento presente.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 38%|███▊      | 900/2391 [12:40<20:42,  1.20it/s]"
+            " 88%|████████▊ | 1200/1367 [10:26<01:26,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 900 of 2391. Loss:0.7947258949279785.\n",
-            "Example output: How can I learn a new language online.Start by familiarizing yourself with the basics of the language. Understand that there are several popular online language learning apps available, such as Duolingo, edX, or edX China. Be sure to take a course or practice a native language regularly before you go to an online language school. \n",
             "\n",
-            "2. Read, write, and study the language\n",
+            "Batch 1200 of 1367. Loss:0.9989928007125854.\n",
             "\n",
-            "Practice speaking the language regularly and engaging in conversations with native speakers. Encourage native speakers to teach and explain to you, even when they don't understand your grammar or pronunciation. Additionally, use language learning apps to practice speaking the language and to learn slang and slang terms.\n",
             "\n",
-            "3. Learn pronunciation\n",
+            "Example output: Você pode fornecer uma definição para emulação de cérebro inteiro (WBE)?Eliezer Yudkowsky cunhou o termo \"WBE\" em 1996, argumentando que sistemas de IA são capazes de criar e analisar imagens cerebrais complexas.\n",
             "\n",
-            "Learn to read and write the language by reading, listening, and speaking with native speakers. Talk to a native speaker over the phone to get a sense of what they are saying. Listen to native speakers during classes or online and identify any slang or slang terms.\n"
+            "Por sua vez, o conceito de Whole Brain Emulation (WBE) foi cunhado pelo neurocientista russo Vladimir S. Gozhanin em 1971, que se enquadra no grupo \"Whole Brain Emulation: A Roadmap\".\n",
+            "\n",
+            "Nos anos recentes, a tecnologia moderna de inteligência artificial tem aumentado sua proficiência na resolução de problemas NP-completos, tornando-se cada vez mais uma força vital para tarefas relacionadas à visão computacional. Além disso, a robótica tem se tornando um recurso valioso no campo, contribuindo significativamente para a inteligência artificial moderna.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 50%|█████     | 1200/2391 [16:56<16:33,  1.20it/s]"
+            "100%|██████████| 1367/1367 [11:54<00:00,  1.91it/s]\n"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1200 of 2391. Loss:0.6887515783309937.\n",
-            "Example output: What are the benefits of living by a lake.The benefits of living by a lake include improved hydration levels and increased immunity, increased energy levels, and improved mental health.\n"
+            "\n",
+            "Average Training Loss: 0.9471002545074206.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.7749460453265592.\n",
+            "\n",
+            "\n",
+            "Beginning epoch 2 of 5\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 63%|██████▎   | 1500/2391 [21:07<12:22,  1.20it/s]"
+            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1500 of 2391. Loss:0.557076096534729.\n",
-            "Example output: What kind of fertilizer is best for a vegetable garden.The best fertilizer for a vegetable garden depends on the variety and size of the plants. Composted compost materials can be a great choice for vegetable garden fertilizers, as they contain less greenhouse gases than their raw counterparts. Composted fertilizers are also more environmentally friendly as they are usually biodegradable and provide better nutrient levels for your garden. Additionally, compost is often used to fill in natural gaps in compost piles.\n"
+            "\n",
+            "Batch 400 of 1367. Loss:0.7124763131141663.\n",
+            "\n",
+            "\n",
+            "Example output: Que fatos interessantes você pode me contar sobre o presidente Abraham Lincoln?Lincoln, o 13º presidente dos Estados Unidos, foi um advogado que foi responsável por estabelecer a Lei de Escândalo do Bankingestado em 1903. Ele foi acusado de fornecer informações que poderiam ter sido usadas para fazer uma série de negócios fraudulentos.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 75%|███████▌  | 1800/2391 [25:19<08:12,  1.20it/s]"
+            " 59%|█████▊    | 800/1367 [06:53<04:52,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1800 of 2391. Loss:0.7273032665252686.\n",
-            "Example output: What is the most popular spaghetti dish in Italy.The most popular dish in Italy is spaghetti with pesto.\n"
+            "\n",
+            "Batch 800 of 1367. Loss:0.6731818318367004.\n",
+            "\n",
+            "\n",
+            "Example output: O motor do meu carro começou a fazer um barulho estranho. O que poderia estar causando isso.A primeira etapa é verificar a pressão do ar na roda. Certifique-se de que está funcionando corretamente. Verifique se há vazamentos de óleo, pneus, freios ou outros problemas. Se estiver de funcionar, substitua o motor quanto à quilometragem da roda. Se a aceleração for muito alta, verifique o painel do acelerador para garantir que esteja funcionando corretamente e certifique-se de que o bujão de freio esteja funcionando corretamente.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 2100/2391 [29:30<04:02,  1.20it/s]"
+            " 88%|████████▊ | 1200/1367 [10:20<01:26,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 2100 of 2391. Loss:0.7546969652175903.\n",
-            "Example output: How can I make a healthy breakfast that takes less than 10 minutes to make.To make a healthy breakfast, start by gathering the ingredients you'll need: eggs, fruit, yogurt, nuts, avocados, peanut butter, and syrup. Heat a non-stick pan over medium-high heat and add your eggs, then your fruit, yogurt, and peanut butter. Allow the pan to cook for a few minutes before adding your peanut butter and any other ingredients. Flip the eggs every so often and cook until they are completely set. Serve with a side of boiled egg or toast.\n"
+            "\n",
+            "Batch 1200 of 1367. Loss:0.6759688258171082.\n",
+            "\n",
+            "\n",
+            "Example output: Você pode me falar sobre os benefícios para a saúde das maçãs.As maçãs têm uma grande variedade de vitaminas, minerais e outros nutrientes benéficos. Estudos demonstraram que as maçãs podem ajudar a melhorar a saúde, como reduzir a inflamação, ajudar a melhorar a saúde digestiva e estimular a criatividade. Comer maçãs pode ajudar a reduzir o risco de doenças cardíacas e algumas doenças cardíacas crônicas, como câncer e diabetes. Também pode ajudar a diminuir o risco de certos tipos de câncer e melhorar o desempenho geral. As maçãs também são baixas em calorias e gorduras não saudáveis, o que é importante para uma dieta saudável e equilibrada. Além disso, as maçãs são uma excelente fonte de fibra e pode ajudar na digestão, que é vital para ajudar a manter uma boa saúde geral.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2391/2391 [33:35<00:00,  1.19it/s]\n"
+            "100%|██████████| 1367/1367 [11:48<00:00,  1.93it/s]\n"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Average Training Loss: 0.8037641328879851.\n",
-            "Validation loss: 0.6114786967523116.\n",
-            "Beginning epoch 2 of 3\n"
+            "\n",
+            "Average Training Loss: 0.7373575027963936.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.7309620019636656.\n",
+            "\n",
+            "\n",
+            "Beginning epoch 3 of 5\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 13%|█▎        | 300/2391 [04:10<29:02,  1.20it/s]"
+            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 300 of 2391. Loss:0.5744898319244385.\n",
-            "Example output: Create a Python script to find the sum of all numbers ranging from k to n inclusive.Sum: k + n + k\n",
-            "      + n + k\n"
+            "\n",
+            "Batch 400 of 1367. Loss:0.6310281753540039.\n",
+            "\n",
+            "\n",
+            "Example output: Quero começar a escrever um blog, mas estou tendo problemas para ter ideias. Você pode sugerir alguns temas.Proposta: O primeiro passo é criar um título que resuma seus pontos principais. Você deve começar apresentando conteúdo sobre como ele funciona e como ele funciona com um leitor.\n",
+            "\n",
+            "2 Depois que seu título seja lançado, comece a escrever links para ele. Certifique-se de incluir palavras-chave relevantes para seus leitores e mantenha seus leitores voltando.\n",
+            "\n",
+            "3 Depois disso, crie uma lista de tópicos do seu blog que deseja explorar e acompanhar seus leitores por alguns dias. Certifique-se de dar atenção a quaisquer tendências, opiniões e análises de blogs que possam mudar suas postagens.\n",
+            "\n",
+            "4 Por fim, considere escrever um breve resumo das principais seções do seu blog para ajudá-lo a identificar as áreas que você recomendaria para leitores mais novos.\n",
+            "\n",
+            "5 Em última análise, cada tópico é um bom cenário para começar sua nova postagem. Boa\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 25%|██▌       | 600/2391 [08:20<24:56,  1.20it/s]"
+            " 59%|█████▊    | 800/1367 [06:55<04:51,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 600 of 2391. Loss:0.6873356103897095.\n",
-            "Example output: What criticisms have been made of updateless decision theory?UDT is a decision theory system with two fundamental pillars: an agent theory and an RNN. UDT's decision trees allow for seamless decision-making, which is a crucial aspect in current state-of-the-art UDT-based decision-making systems. UDT is Turing complete and offers a wide range of decision-making options for different tasks, such as monitoring a customer’s purchases, playing chess, or investing in stocks and bonds. For more details, refer to \"Updateless Decision Theory: A Review of 200 Guidelines and Recommendations for a New Decision Theory.\"\n"
+            "\n",
+            "Batch 800 of 1367. Loss:0.681230366230011.\n",
+            "\n",
+            "\n",
+            "Example output: Quais oportunidades de trabalho estão disponíveis para alguém com graduação em química.Existem muitos cursos disponíveis para pessoas com graduação em química. A Harvard University e o University of Pennsylvania oferecem serviços com experiência em química. O Stanford College of Medicine oferece aulas de bacharel em química e doutorado em química. O Brigham and Women's College of Science, Massachusetts Institute of Technology, Dartmouth College of Medicine e Harvard Medical School são dois dos dois centros médicos.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 38%|███▊      | 900/2391 [12:34<20:42,  1.20it/s]"
+            " 88%|████████▊ | 1200/1367 [10:22<01:26,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 900 of 2391. Loss:0.5231805443763733.\n",
-            "Example output: Does the Earth revolve around the moon.Yes, the Earth revolved around the moon millions of years ago.\n"
+            "\n",
+            "Batch 1200 of 1367. Loss:0.6577063202857971.\n",
+            "\n",
+            "\n",
+            "Example output: Qual é o objetivo da regulamentação geral de privacidade e proteção de dados (GDPR)?No âmbito da ética da IA, o princípio da privacidade encapsula um domínio fundamental de propriedade parcial e controle sobre a exposição de informações pessoais a uma rede. Com uma compreensão desta técnica impactada, torna-se imperativo desenvolver medidas proativas para salvaguardar o bem-estar das pessoas e sistemas de IA.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 50%|█████     | 1200/2391 [16:44<16:32,  1.20it/s]"
+            "100%|██████████| 1367/1367 [11:49<00:00,  1.93it/s]\n"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1200 of 2391. Loss:0.49868226051330566.\n",
-            "Example output: What do you mean by sustainable AI?Sustainability refers to AI technology being developed that is non-polluting, energy efficient, and uses limited resources.\n"
+            "\n",
+            "Average Training Loss: 0.6574105381878432.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.7102324895952877.\n",
+            "\n",
+            "\n",
+            "Beginning epoch 4 of 5\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 63%|██████▎   | 1500/2391 [20:55<12:22,  1.20it/s]"
+            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1500 of 2391. Loss:0.6849273443222046.\n",
-            "Example output: How do I make the best cup of coffee.To make the best cup of coffee, start by choosing a cup that is light, steaming, and has a good burr. Once the burr is set, heat the water to about 400°F. Pour the coffee into the cup and let the brew for a few minutes before pouring the rest of the coffee into the cup. Finally, top the cup off with your favorite toppings and enjoy.\n"
+            "\n",
+            "Batch 400 of 1367. Loss:0.5170938372612.\n",
+            "\n",
+            "\n",
+            "Example output: Qual é a melhor maneira de aumentar os níveis de energia pela manhã.A melhor maneira de aumentar os níveis de energia pela manhã é fazer atividades que você goste, como caminhar ou correr, ler um livro, meditar ou meditar, ouvir uma música relaxante, praticar atividade física regular, comer uma dieta balanceada e fazer uma pausa em determinados momentos da semana para relaxar e recarregar.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 75%|███████▌  | 1800/2391 [25:08<08:12,  1.20it/s]"
+            " 59%|█████▊    | 800/1367 [06:53<04:52,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1800 of 2391. Loss:0.7784386277198792.\n",
-            "Example output: Where can I find low-cost flights to Alaska.You can find low-cost flights to Alaska by searching on airline websites such as Expedia, Skyscanner, and Kayak. You can also search directly for flights online using airline search engines, such as Google Flights.\n"
+            "\n",
+            "Batch 800 of 1367. Loss:0.6901150941848755.\n",
+            "\n",
+            "\n",
+            "Example output: Quais escolas de ética surgiram na tradição ocidental e o que elas implicam?A Filosofia Moral, que é uma divisão da Filosofia, concentra-se nos princípios que nos conduzem ao desenvolvimento e na tomada de decisões éticas. Abrange o exame díspares das convicções éticas encontradas nos indivíduos, abrangendo a deontologia, a ética das virtudes, a ética da virtude e a ética da virtude.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 2100/2391 [29:19<04:02,  1.20it/s]"
+            " 88%|████████▊ | 1200/1367 [10:20<01:26,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 2100 of 2391. Loss:0.7476993799209595.\n",
-            "Example output: What are the benefits of using renewable energy sources.The benefits of using renewable energy sources include reducing dependence on finite resources, reducing the need for large-scale fossil fuels, providing access to renewable energy resources and reducing carbon emissions from the energy sector. Additionally, renewable energy sources can provide a way for people to reduce their reliance on fossil fuels by transitioning to renewable energy sources such as solar, wind, and hydro power.\n"
+            "\n",
+            "Batch 1200 of 1367. Loss:0.4600249230861664.\n",
+            "\n",
+            "\n",
+            "Example output: Forneça conselhos sobre como atingir minhas metas de condicionamento físico.A melhor maneira de atingir seus objetivos de condicionamento físico é praticar. Comece definindo metas alcançáveis e mensuráveis e divida-as em etapas alcançáveis e alcançáveis. Acompanhe seu progresso, depois use esses resultados para desenvolver um plano de estudo e use-o para garantir que você fique em forma. Você também pode tentar incorporar exercícios de baixo impacto ou atividades com uma força positiva, como caminhar, correr ou ouvir música. Depois de atingir essas metas, você pode investir em atividades de maior intensidade, como treinamento de força, treinamento de flexibilidade, e treinamento intervalado. Por fim, recompense-se por cumprir o trabalho árduo e atingir seus objetivos de condicionamento físico.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2391/2391 [33:24<00:00,  1.19it/s]\n"
+            "100%|██████████| 1367/1367 [11:48<00:00,  1.93it/s]\n"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Average Training Loss: 0.5702004936986865.\n",
-            "Validation loss: 0.5858413227518698.\n",
-            "Beginning epoch 3 of 3\n"
+            "\n",
+            "Average Training Loss: 0.5974375438040508.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.7050648294389248.\n",
+            "\n",
+            "\n",
+            "Beginning epoch 5 of 5\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 13%|█▎        | 300/2391 [04:10<29:03,  1.20it/s]"
+            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 300 of 2391. Loss:0.4317343533039093.\n",
-            "Example output: I'm concerned about my finances and want to develop a budget. Can you help.I'm sorry, I'm not a financial expert, but I can help you identify how much money you have available to save and what savings strategies you may be able to implement to help you achieve your financial goals. It can be helpful to review the budget periodically and be sure to stay on track. Additionally, consider talking to a financial consultant who can provide guidance to help make the process smoother.\n"
+            "\n",
+            "Batch 400 of 1367. Loss:0.6328504681587219.\n",
+            "\n",
+            "\n",
+            "Example output: Qual é o papel da entropia cruzada no aprendizado de máquina?A entropia cruzada mede a dissimilaridade entre duas distribuições de probabilidade. É uma ferramenta indispensável no campo da avaliação de distribuições de probabilidade e mede a divergência entre duas distribuições de probabilidade.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 25%|██▌       | 600/2391 [08:22<24:51,  1.20it/s]"
+            " 59%|█████▊    | 800/1367 [06:53<04:52,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 600 of 2391. Loss:0.3909016251564026.\n",
-            "Example output: What is the significance of the \"p versus np\" problem?A problem is defined as a problem that can be solved in a number of steps bounded by a polynomial function of n, where n corresponds to the power of the input length, for example, a problem of algorithm complexity is restricted by a polynomial function of n. Although NP is the formal name of decision theory, it has a simpler and more concise definition than other complexity classes such as linear or quadratic, due to the fact that all problems solvable by any algorithm can also be solved.\n"
+            "\n",
+            "Batch 800 of 1367. Loss:0.5364123582839966.\n",
+            "\n",
+            "\n",
+            "Example output: O que significa aprendizagem federada?FL é um método em aprendizado de máquina que treina modelos usando exemplos descentralizados em dispositivos como smartphones para aprender uma nova habilidade. Nessa abordagem, um servidor central distribui o modelo atual ao servidor, que a treina em uma versão de coordenação (C) e armazenada na rede. A rede é então usada para fazer melhorias no modelo, o que pode levar a melhorias na qualidade do modelo geral ou até mesmo gerar um modelo global mais personalizado.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 38%|███▊      | 900/2391 [12:35<20:43,  1.20it/s]"
+            " 88%|████████▊ | 1200/1367 [10:20<01:26,  1.94it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 900 of 2391. Loss:0.3943908214569092.\n",
-            "Example output: How big of a storage tank do I need to store 1,000 gallons of water.To store 1,000 gallons of water, you will need a storage tank or other water-saving method. You will also need to purchase water supplies such as a rain barrel, faucet, showerhead, or other water filtration system. You will also need to install a dishwasher, dish washing machine, and washing machine.\n"
+            "\n",
+            "Batch 1200 of 1367. Loss:0.5115535855293274.\n",
+            "\n",
+            "\n",
+            "Example output: Quero começar a me exercitar, mas não sei como. Você pode me fornecer um plano de exercícios para iniciantes.Eu recomendo começar com exercícios básicos de peso corporal, como flexões, flexões e agachamentos. Esses exercícios podem ajudá-lo a construir força, reduzir a tensão muscular e queimar calorias rapidamente. Existem muitos livros e vídeos online que fornecem exercícios para iniciantes. Esses exercícios podem ajudá-lo a desenvolver e manter uma boa saúde cardiovascular e mental.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 50%|█████     | 1200/2391 [16:47<16:32,  1.20it/s]"
+            "100%|██████████| 1367/1367 [11:47<00:00,  1.93it/s]\n"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Batch 1200 of 2391. Loss:0.5626760721206665.\n",
-            "Example output: I need help with understanding a specific math problem.The best way to understand a math problem is to break it down into smaller parts and study each one step-by-step. Identify the steps that need to be taken to solve the problem and then work backward to identify the best way to solve them. After you have an understanding of the problem, you can then work to make sure that the solution is correct.\n"
+            "\n",
+            "Average Training Loss: 0.551684551763988.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.7048302759465418.\n",
+            "\n",
+            "Training complete!\n"
           ]
         },
         {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 63%|██████▎   | 1500/2391 [20:59<12:22,  1.20it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Batch 1500 of 2391. Loss:0.6661339998245239.\n",
-            "Example output: What are the three main types of clouds.The three main types of clouds are cumulus, cumulonimbus, and polygon.\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 75%|███████▌  | 1800/2391 [25:10<08:13,  1.20it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Batch 1800 of 2391. Loss:0.5321910381317139.\n",
-            "Example output: What is the best way to clean a wool sweater.The best way to clean a wool sweater is to use a mild detergent and warm water, spot clean and condition the sweater with a mild detergent and warm water, then launder the sweater according to the care instructions on the care label. Rinse the garment in clean water and dry thoroughly before hanging it up.\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            " 88%|████████▊ | 2100/2391 [29:22<04:02,  1.20it/s]"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Batch 2100 of 2391. Loss:0.4964297413825989.\n",
-            "Example output: Give me an idea of the mental exercises you do to inspire yourself.\n",
-            " Input: - Exercise regularly\n",
-            "- Take breaks\n",
-            "- Set realistic goals\n",
-            "- Connect with friends\n",
-            "- Engage with nature\n",
-            "- Limit distractions\n",
-            "- Participate in activities you enjoy\n",
-            "- Talk to others\n",
-            "- Set realistic expectations\n",
-            "- Take a break to focus on a specific task\n",
-            "- Write down your thoughts\n",
-            "- Use positive affirmations\n",
-            "- Read more about it.\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "100%|██████████| 2391/2391 [33:27<00:00,  1.19it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Average Training Loss: 0.4873914483300516.\n",
-            "Validation loss: 0.5831270120421747.\n",
-            "Training complete!\n"
-          ]
+          "data": {
+            "text/plain": [
+              "('/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/tokenizer_config.json',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/special_tokens_map.json',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/vocab.json',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/merges.txt',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/added_tokens.json')"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
         }
       ],
       "source": [
         "from codecarbon import EmissionsTracker\n",
         "import tqdm\n",
         "\n",
-        "output_dir = f'./Aira-{model_size}'\n",
+        "output_dir = f'/content/drive/MyDrive/Colab Notebooks/Aira-{model_size}'\n",
         "\n",
         "tracker = EmissionsTracker(\n",
         "    project_name=\"Aira_emissions\",\n",
@@ -1359,19 +1713,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 439
+          "height": 592
         },
         "id": "J1-hAY9Av4gT",
-        "outputId": "d35d961f-d9cc-4813-8963-817b43a19c19"
+        "outputId": "e568f437-8084-4d89-9efb-30c981336d00"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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",
             "text/plain": [
               "<Figure size 1200x600 with 1 Axes>"
             ]
@@ -1387,18 +1741,14 @@
         "\n",
         "df_stats = pd.read_parquet(f\"{output_dir}/training_stats.parquet\")\n",
         "\n",
-        "# Use plot styling from seaborn.\n",
         "sns.set(style='darkgrid')\n",
         "\n",
-        "# Increase the plot size and font size.\n",
         "sns.set(font_scale=1.5)\n",
         "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
         "\n",
-        "# Plot the learning curve.\n",
         "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
         "plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n",
         "\n",
-        "# Label the plot.\n",
         "plt.title(\"Training & Validation Loss\")\n",
         "plt.xlabel(\"Epoch\")\n",
         "plt.ylabel(\"Loss\")\n",
@@ -1419,11 +1769,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
-        "id": "mpBJNtYuAY9D"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mpBJNtYuAY9D",
+        "outputId": "66532b2e-1635-483d-d20a-9daad799ea57"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Question: 👤 Como eu posso aprender a programar?\n",
+            "\n",
+            "Response 1: 🤖 Uma das melhores maneiras de aprender a programar é começar familiarizando-se com os fundamentos, como codificação, programação e análise de dados. Depois de dominar os fundamentos, você pode começar a praticar a programar em um campo específico, como ciência de dados, robótica ou inteligência artificial. Depois de ter uma boa compreensão dos conceitos básicos, você pode começar a praticar a codificação em um pequeno conjunto de desafios, como codificar um site básico, criar um aplicativo e começar a desenvolver aplicativos móveis.\n",
+            "Response 2: 🤖 A programar é uma habilidade que você pode aprender com uma variedade de materiais, como livros, tutoriais online, cursos e cursos. Em seguida, você pode praticar em um centro de tutoria ou em uma aula de certificação. Isso lhe dará uma compreensão mais profunda do processo de criação de programas de software.\n"
+          ]
+        }
+      ],
       "source": [
         "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
         "import torch\n",
@@ -1454,7 +1819,6 @@
         "print(f\"Question: 👤 {question}\\n\")\n",
         "\n",
         "for i, response in  enumerate(responses):\n",
-        "    # print only the response and remove the question\n",
         "    print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, \"\")}')\n"
       ]
     },
@@ -1493,7 +1857,7 @@
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "053a5571006e41a6aef0b77fa1e36faa": {
+        "00485f3c3dbc40b5be49c8f4fd49c3d7": {
           "model_module": "@jupyter-widgets/base",
           "model_module_version": "1.2.0",
           "model_name": "LayoutModel",
@@ -1545,135 +1909,7 @@
             "width": null
           }
         },
-        "0a394b51e4de479bbc019c02fa6bb9ab": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_module_version": "1.5.0",
-          "model_name": "DescriptionStyleModel",
-          "state": {
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "DescriptionStyleModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/base",
-            "_view_module_version": "1.2.0",
-            "_view_name": "StyleView",
-            "description_width": ""
-          }
-        },
-        "0c01e2b05f3c4bdd929aeae62379d0a8": {
-          "model_module": "@jupyter-widgets/controls",
-          "model_module_version": "1.5.0",
-          "model_name": "FloatProgressModel",
-          "state": {
-            "_dom_classes": [],
-            "_model_module": "@jupyter-widgets/controls",
-            "_model_module_version": "1.5.0",
-            "_model_name": "FloatProgressModel",
-            "_view_count": null,
-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
-            "_view_name": "ProgressView",
-            "bar_style": "success",
-            "description": "",
-            "description_tooltip": null,
-            "layout": "IPY_MODEL_d74a865eb33a46daa004a8e270fd3a5d",
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