diff --git "a/AIRA_FineTuning.ipynb" "b/AIRA_FineTuning.ipynb"
--- "a/AIRA_FineTuning.ipynb"
+++ "b/AIRA_FineTuning.ipynb"
@@ -13,13 +13,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "SBWCrz5GfBXo",
-        "outputId": "34bbeb89-a5d5-4ad4-f87f-a87bffc735a2"
+        "outputId": "1e1ab31b-3b54-4c39-a872-fb39e4c5c8a4"
       },
       "outputs": [
         {
@@ -27,49 +27,32 @@
           "output_type": "stream",
           "text": [
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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-            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
-            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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-            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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-            "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",
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-            "Collecting fuzzywuzzy (from codecarbon)\n",
-            "  Downloading fuzzywuzzy-0.18.0-py2.py3-none-any.whl (18 kB)\n",
+            "Requirement already satisfied: fuzzywuzzy in /usr/local/lib/python3.10/dist-packages (from codecarbon) (0.18.0)\n",
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@@ -78,9 +61,7 @@
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-            "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"
+            "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"
           ]
         }
       ],
@@ -100,21 +81,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 467
         },
         "id": "7MbpXGu-v4f1",
-        "outputId": "7d155c3b-13f2-417d-a5b9-6d0dce08a5d7"
+        "outputId": "3d8858dc-6ea1-4871-cb04-44dcecce51a6"
       },
       "outputs": [
         {
           "data": {
             "text/html": [
               "\n",
-              "  <div id=\"df-b575c0da-5a72-42cd-861a-36728a31f263\">\n",
+              "  <div id=\"df-7ebefa58-d546-469a-8e61-ec63d737689d\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -198,7 +179,7 @@
               "</table>\n",
               "<p>53134 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-7ebefa58-d546-469a-8e61-ec63d737689d')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -249,12 +230,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-b575c0da-5a72-42cd-861a-36728a31f263 button.colab-df-convert');\n",
+              "          document.querySelector('#df-7ebefa58-d546-469a-8e61-ec63d737689d 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-7ebefa58-d546-469a-8e61-ec63d737689d');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -333,55 +314,55 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 241,
           "referenced_widgets": [
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-            "5803df0c4976405c99c756a5e687cc90",
-            "9e22ec2b93524dda84f88b5fef7f5c2a",
-            "14a867b9db464260be3bed64c225c412",
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-            "716067896aba419ead0616e88f4f2dce",
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+            "96de7beae5b94e6a92f9f52b9d824035",
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+            "acbc045dd45c41c484e59304a66336f4",
+            "b8b4e3e26208445e91aa15ee3ea6e6b0"
           ]
         },
         "id": "hfu84fWIv4f9",
-        "outputId": "4269ccc5-e854-4095-dafd-cc5c40d0f12b"
+        "outputId": "4c471e66-3497-433d-bc65-2287608a6f32"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "cd7347fa726445d394c474ecdcd722f5",
+              "model_id": "2231293622594eceb80d6481799ddcad",
               "version_major": 2,
               "version_minor": 0
             },
@@ -395,7 +376,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "db63d0aab8e444a28d827dd96067635a",
+              "model_id": "6539b916daa747adab05dcff94c2cd3c",
               "version_major": 2,
               "version_minor": 0
             },
@@ -409,12 +390,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "e0464c04c0f848cfb6af2652adb8af66",
+              "model_id": "e2ca1209ffe94778a38a383641b09cb9",
               "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": {},
@@ -439,8 +420,8 @@
       "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 = \"gpt2-large\" # \"gpt2\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\", \"pierreguillou/gpt2-small-portuguese\"\n",
+        "model_size = \"774M\" # \"124M\", \"355M\", \"774M\", and \"1.5B\", \"PT-124M\"\n",
         "\n",
         "tokenizer = GPT2Tokenizer.from_pretrained(model,\n",
         "                                          bos_token='<|startoftext|>',\n",
@@ -466,7 +447,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
         "id": "WlbAfMQ4v4gA"
       },
@@ -513,13 +494,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "-IOfa2PEv4gD",
-        "outputId": "83b162aa-a00e-4736-cc9e-0ceb005db30e"
+        "outputId": "ca19c34e-79fe-43ef-f0ad-b4f06e2d8f5f"
       },
       "outputs": [
         {
@@ -556,7 +537,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "id": "cUkCNV-6v4gG"
       },
@@ -567,14 +548,14 @@
         "train_dataloader = DataLoader(\n",
         "            train_dataset,\n",
         "            sampler=RandomSampler(train_dataset),\n",
-        "            batch_size=20 # 32, 20, 8, 6\n",
+        "            batch_size=8 # 32, 20, 8, 8\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=8\n",
         "        )"
       ]
     },
@@ -589,49 +570,49 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 550,
           "referenced_widgets": [
-            "d3706d2eefcd41ec89f3f0247719ec92",
-            "817964cc90334605902c6f13d4e94bcb",
-            "d2669f9ffdfe404b944100abf125d437",
-            "4971c12962724535bf221fac26ee6938",
-            "40c7558bdd57406abf170b0b4600f2cc",
-            "e6062f15b1af4d838e90aaf5a65748fb",
-            "0a394b51e4de479bbc019c02fa6bb9ab",
-            "caac8742661d4238a06d1ab65add439a",
-            "3fa7d040728843de8812d298154673bc",
-            "dc632fb86d7a4b4c93b7315eef2e7a48",
-            "e1ce84fd7f9045158aefd3ec7359e0a9",
-            "317ef8edd3b44443bce2c1f7f0ed6d4b",
-            "376ff945cd5f4705b6db9c747aa7e062",
-            "b952e087a32c46b7a4aee6a06ebe62d5",
-            "c4911938e5c64ca098bbd0d32ac65870",
-            "fd4bb55839b443c9baf35c37f0033337",
-            "c0c83e1a879a4c1387bd6e353514fdfa",
-            "71c676fba8144996b6749aefc46f63fc",
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-            "d62519fa2ae14084ba1aac280a44088c",
-            "39b34f03b78b41f18b50bffdfd6993ba",
-            "b78c2ba8ef6248f6b3d2f0b8f2f31f7c"
+            "0fc39225b0694840b7f421f725c135f2",
+            "4886d255282b4eefa37e7a1db102a132",
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+            "bd0f1d0070294b6eaebba425e9224150",
+            "7cccbb84d04b4aaaa09309bb669f349d",
+            "2cdd431ccdb64e409229913cc43e2544",
+            "ba5408f14ed54e75a9724d39ac67d5c2",
+            "10d2880b45814382951aa1015f4f1f5f",
+            "3657a339af424c51ac7534f91fcb5893",
+            "1bb5acd98f244616905e0f5cb8752a8a",
+            "b023d77e9b854acd94ac0a91feb2de83",
+            "b2d0b131237f49e684451afe60b57593",
+            "8691d4c5d8d54e2494d2bf149feb9980",
+            "7ddef1b949f04c81b6e26df3ce68cbc6",
+            "e125c896502c4ad48b4bfbdbe3dc7383",
+            "1e2b0f9db8654f85938565d9f694030d",
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+            "2d1b573ef7a242c295ed42d790a71f1e",
+            "89f8e93f887d43ee933fa3b44a44fd72",
+            "e58aaf5a1b9a4a2f85fa2546ee88f24e",
+            "3181ccb3dde340c1b5c67ed12076c897",
+            "4a22e5e3eb354b4cb63bf0aeb9105efa"
           ]
         },
         "id": "Rmg-5YJqv4gH",
-        "outputId": "bccf9dea-1980-4234-f26d-e08c586399b5"
+        "outputId": "1f8aa617-07b6-4774-d024-cd50d8148f5b"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "d3706d2eefcd41ec89f3f0247719ec92",
+              "model_id": "0fc39225b0694840b7f421f725c135f2",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading model.safetensors:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
+              "Downloading model.safetensors:   0%|          | 0.00/3.25G [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -640,7 +621,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "317ef8edd3b44443bce2c1f7f0ed6d4b",
+              "model_id": "b2d0b131237f49e684451afe60b57593",
               "version_major": 2,
               "version_minor": 0
             },
@@ -656,19 +637,19 @@
             "text/plain": [
               "GPT2LMHeadModel(\n",
               "  (transformer): GPT2Model(\n",
-              "    (wte): Embedding(50259, 1024)\n",
-              "    (wpe): Embedding(1024, 1024)\n",
+              "    (wte): Embedding(50259, 1280)\n",
+              "    (wpe): Embedding(1024, 1280)\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",
+              "      (0-35): 36 x GPT2Block(\n",
+              "        (ln_1): LayerNorm((1280,), 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((1280,), eps=1e-05, elementwise_affine=True)\n",
               "        (mlp): GPT2MLP(\n",
               "          (c_fc): Conv1D()\n",
               "          (c_proj): Conv1D()\n",
@@ -677,9 +658,9 @@
               "        )\n",
               "      )\n",
               "    )\n",
-              "    (ln_f): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+              "    (ln_f): LayerNorm((1280,), 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=1280, out_features=50259, bias=False)\n",
               ")"
             ]
           },
@@ -712,28 +693,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "MJA-WOoHZePn",
-        "outputId": "7d0895a3-450f-427b-ea97-dc514306407b"
+        "outputId": "dc2c4cea-17c5-479b-b667-b4782ded533a"
       },
       "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:  36\n",
+            "Number of transformer blocks to  un-freeze:  9\n",
+            "Number of frozen layers: 325\n",
+            "Number of trainable layers: 111\n"
           ]
         }
       ],
       "source": [
-        "UNFREEZE_LAST_N = 8 #6, 8, 12, 12\n",
+        "UNFREEZE_LAST_N = 9 #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",
@@ -771,7 +752,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 13,
       "metadata": {
         "id": "qlbLg6tqv4gI"
       },
@@ -780,13 +761,13 @@
         "from transformers import get_linear_schedule_with_warmup\n",
         "\n",
         "# training rounds\n",
-        "epochs = 3 # 5, 3, 2, 1\n",
+        "epochs = 2 # 5, 3, 2, 1\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",
@@ -810,419 +791,736 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "_X_m8XOtv4gR",
-        "outputId": "d3fed0ff-df94-4c24-ef0c-4a4ebe299640"
+        "outputId": "998c5fe2-29d0-411c-a4da-67dffe9d9210"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Beginning epoch 1 of 3\n"
+            "\n",
+            "Beginning epoch 1 of 2\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  7%|▋         | 400/5978 [04:38<1:04:12,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 400 of 5978. Loss:0.6028859615325928.\n",
+            "\n",
+            "\n",
+            "Example output: What is the most secure way to store financial information.The most secure way to store financial information is to have a financial agency that is able to provide secure storage, including cold storage, with a combination of various cryptographic principles.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 13%|█▎        | 800/5978 [09:17<59:35,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 800 of 5978. Loss:0.6626878380775452.\n",
+            "\n",
+            "\n",
+            "Example output: Can you give me advice on setting up a business.One of the best ways to set up a business is to create an account with a reputable financial institution. Start by researching the account you need, and you should be able to find a reliable and trustworthy account to start with. Additionally, look for services that are offered specifically for businesses or professionals and make sure to compare rates. This can help you find a comfortable level of cost for each service. Finally, make sure you have a plan for growth. Research the business and find out the resources and marketing campaigns that can help you reach your desired audience.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 20%|██        | 1200/5978 [13:58<54:59,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 1200 of 5978. Loss:0.655192494392395.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\r 20%|██        | 1201/5978 [14:01<1:39:26,  1.25s/it]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Example output: Please help me find an interesting but classic book to read for my commute this week.One interesting book to read for your commute this week is \"The Hitchhiker's Guide to the Galaxy\" by Douglas Adams. It is a classic fantasy adventure book that follows the protagonist's journey to save the universe.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 27%|██▋       | 1600/5978 [18:37<50:23,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 1600 of 5978. Loss:0.7218917012214661.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\r 27%|██▋       | 1601/5978 [18:38<1:11:20,  1.02it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Example output: Name five different cheeses.Tequila, Chorizo, Mashed Potatoes, Burgundy Cheese, and Mashed Potatoes\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 33%|███▎      | 2000/5978 [23:14<45:47,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 2000 of 5978. Loss:0.4903753697872162.\n",
+            "\n",
+            "\n",
+            "Example output: What is algebra and who invented it?Algebra and the foundations of mathematics are incredibly complex and require a lot of practice in order to learn. The concepts of algebra and the algebraic equations are based on equations and sequences of equations, not on purely numerical information. It is important to differentiate between algorithms and algorithms' algorithms, as Algebra and the Algebraic equations work very effectively in situations where these equations are being used to solve complex problems. For instance, using the equation A=1 and applying it to any real number, such as 1, can solve any equation of that order. The equations of motion are typically solvable by algebraic equations and have some applications in machine learning, where it is possible to identify patterns in data that are difficult to solve with numerical information.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 40%|████      | 2400/5978 [27:56<41:11,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 2400 of 5978. Loss:0.4223511219024658.\n",
+            "\n",
+            "\n",
+            "Example output: How can I improve my learning habits, so that the material I'm studying is stored in my memory more effectively.One of the most effective ways to improve your learning habits is to set small achievable goals. Break down your learning into smaller chunks and focus on mastering the material you are learning. This will help to stay motivated to study and make progress. Additionally, make sure to practice with native materials to help develop your understanding of the language and familiarize yourself with what you are learning. Finally, look for resources like books, podcasts, and websites that are designed to be visual and engaging, allowing you to experience the language from different angles and perspectives.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 47%|████▋     | 2800/5978 [32:37<36:35,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 2800 of 5978. Loss:0.7402357459068298.\n",
+            "\n",
+            "\n",
+            "Example output: What is neoliberalism and what are its key tenets?Foliberalism is an ideology that emphasizes the importance of individual freedom and autonomy, while recognizing the importance of developing policies and technologies that guarantee social, economic, and political stability. It is often associated with economic deregulation, deregulation of industries and industries on a large scale, deregulation of industries within governments, deregulation of industries within state and local governments, and deregulation of industries within global companies. This ideology involves preserving individual freedom and autonomy, while encouraging technological advancements and exploring the world around us. It is often associated with the deregulation of the economy and the deregulation of the economy in general, as well as deregulation of government services and the deregulation of government and industries. Neoliberalism also involves the deregulation of political and social systems, including deregulation of democratic reforms, deregulation of laws, and deregulation of industries to ensure a strong welfare state. It is often associated with a free-market perspective, and the deregulation of individual and collective freedom and autonomy.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 13%|█▎        | 300/2391 [04:13<29:02,  1.20it/s]"
+            " 54%|█████▎    | 3200/5978 [37:21<31:58,  1.45it/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",
+            "Batch 3200 of 5978. Loss:0.5701470971107483.\n",
             "\n",
-            "1. What is a polynomial?\n",
             "\n",
-            "2. What is a trigonomial?\n",
-            "3. What is an eigendecid?\n",
+            "Example output: Write a brief paragraph of the benefits of attending Arizona State UniversityArizona State University is one of the oldest universities in the United States, dating back to 1867. It is a private institution with over 200 degree programs. It is also one of the best universities in the United States for those with a degree or master's degree in mathematics, economics, research science, public health, or education. Arizona State University offers its students a wide range of program offerings, including the renowned Stanford University University, University of Southern California, Arizona State University, Arizona State University, Arizona State University. Arizona State University offers its students a wide range of program offerings, including the renowned Stanford University University, University of Southern California, Arizona State University, Arizona State University, Arizona State University. Phoenix and Arizona are both located in Arizona and offer a range of student activities such as Arizona State University School of Business, Arizona State University School of Law, Arizona State University School of State University, and Arizona State University College of Business\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 60%|██████    | 3600/5978 [42:05<27:21,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 3600 of 5978. Loss:0.7429184913635254.\n",
+            "\n",
+            "\n",
+            "Example output: How do I organize my home office.To organize your home office, you will need to find a storage space, decide what items you need and determine what you will get in exchange. Then, decide what features you want the office to have such as a filing system, tools, and materials. After that, you can decide if you want to keep everything in the closet or invest in some organizers. Once you have your space organized, you can start by sorting the items by type. This will help make use of the space better, such as organizing small items like the necessary items like coffee and paper. Finally, you can set realistic goals for the items you want and determine how much you need each one.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 67%|██████▋   | 4000/5978 [46:47<22:45,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 4000 of 5978. Loss:0.7125013470649719.\n",
             "\n",
-            "4. What is the real square?\n",
             "\n",
-            "5. What is a logarithmic square?\n"
+            "Example output: Is violence good sometimes?As a manager, what is good for you is not always good for you, and what is bad sometimes is not necessarily bad in the long run. This could apply to conflicts or disputes, relationships with others or time in the workplace. It's important to acknowledge that it's okay to be willing to take some calculated risk or involve yourself in an argument to reach a resolution or finding a mutually beneficial solution. It's okay to be willing to try different strategies and to be open to finding ways to avoid conflict. It's also important to try to prioritize finding a productive solution and finding ways to communicate better and compromise when needed.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 25%|██▌       | 600/2391 [08:26<24:52,  1.20it/s]"
+            " 74%|███████▎  | 4400/5978 [51:29<18:09,  1.45it/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 4400 of 5978. Loss:0.9460142850875854.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 38%|███▊      | 900/2391 [12:40<20:42,  1.20it/s]"
+            "\r 74%|███████▎  | 4401/5978 [51:31<27:22,  1.04s/it]"
           ]
         },
         {
           "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",
+            "Example output: Classify each item as being at a playground or a gym: slide, swing, dumbbell, squat rack, seesawSlide: playground\n",
+            "Swing: Gym\n",
+            "Dumbbell: Gym\n",
+            "Squat Rack: Gym\n",
+            "Seesaw: Gym\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 80%|████████  | 4800/5978 [56:06<13:33,  1.45it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
             "\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",
+            "Batch 4800 of 5978. Loss:0.8724031448364258.\n",
             "\n",
-            "3. Learn pronunciation\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"
+            "Example output: What are some useful strategies for solving a Rubik's cube.Crackers \n",
+            "2. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "3. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "4. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "5. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "6. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "7. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "8. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "9. Turn the cube clockwise by moving one of the bottom pieces clockwise. \n",
+            "10 Turn the cube clockwise by moving one of the bottom pieces clockwise.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 50%|█████     | 1200/2391 [16:56<16:33,  1.20it/s]"
+            " 87%|████████▋ | 5200/5978 [1:00:50<08:57,  1.45it/s]"
           ]
         },
         {
           "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",
+            "Batch 5200 of 5978. Loss:0.5923346281051636.\n",
+            "\n",
+            "\n",
+            "Example output: How much should I save each month to purchase a house in five years.The amount you should save each month to purchase a house in five years will vary based on the size of your home, as well as the condition of the property and the financial commitment you need to commit. However, you may want to set a goal that limits the amount of time you need to save each month to purchase a house. Additionally, make sure to factor in any unexpected expenses you may incur, such as insurance or maintenance.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 63%|██████▎   | 1500/2391 [21:07<12:22,  1.20it/s]"
+            " 94%|█████████▎| 5600/5978 [1:05:30<04:20,  1.45it/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 5600 of 5978. Loss:0.5626195669174194.\n",
+            "\n",
+            "\n",
+            "Example output: I heard that meditation can be beneficial, do you know of any tips.Yes, regular meditation can be beneficial. Studies have found that regular physical activity can help keep the body moving and stay in shape. Regular physical activity can also help maintain a healthy diet and may also support other health benefits. Additionally, learning relaxation techniques such as deep breathing, progressive muscle relaxation, and progressive muscle relaxation (PMA) can help keep the body relaxed and focused on a task. Finally, talking to a friend or counselor can help create a supportive environment for regular meditation or other stress management techniques.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 75%|███████▌  | 1800/2391 [25:19<08:12,  1.20it/s]"
+            "100%|██████████| 5978/5978 [1:09:55<00:00,  1.42it/s]\n"
           ]
         },
         {
           "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",
+            "Average Training Loss: 0.6872660141050636.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.6161282828427795.\n",
+            "\n",
+            "\n",
+            "Beginning epoch 2 of 2\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 2100/2391 [29:30<04:02,  1.20it/s]"
+            "  7%|▋         | 400/5978 [04:36<1:04:12,  1.45it/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 400 of 5978. Loss:0.3264784812927246.\n",
+            "\n",
+            "\n",
+            "Example output: What are the signs of an ingrown toenail.Common signs of an toenail toenail include pain when urinating, limited range of motion when urinating, and an urge to urinate when urinating. Other signs can include nausea, constipation, and tiredness.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2391/2391 [33:35<00:00,  1.19it/s]\n"
+            " 13%|█▎        | 800/5978 [09:14<59:36,  1.45it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Average Training Loss: 0.8037641328879851.\n",
-            "Validation loss: 0.6114786967523116.\n",
-            "Beginning epoch 2 of 3\n"
+            "\n",
+            "Batch 800 of 5978. Loss:0.560775876045227.\n",
+            "\n",
+            "\n",
+            "Example output: What is the best way to plan a vacation on a budget.The best way to plan a vacation on a budget is to compare prices across different sites, look for deals and discounts, and plan to have enough cash to make the most of your vacation. Additionally, consider budgeting for food, entertainment, and travel, and consider staying at a cheaper or better accommodation. Finally, if you need help staying within your budget, consider visiting a local local community center or community center with affordable or free activities or entertainment.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 13%|█▎        | 300/2391 [04:10<29:02,  1.20it/s]"
+            " 20%|██        | 1200/5978 [13:54<54:58,  1.45it/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 1200 of 5978. Loss:0.41560453176498413.\n",
+            "\n",
+            "\n",
+            "Example output: What is the easiest way to learn a language by myself.The easiest way to learn a language by yourself is to find a language learning program that offers tutorials and activities to help you learn the language. These programs usually provide comprehensive lessons, with examples and exercises to help you understand the language. Additionally, many provide virtual courses, which provide content and exercises in a more streamlined way than a tutorial.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 25%|██▌       | 600/2391 [08:20<24:56,  1.20it/s]"
+            " 27%|██▋       | 1600/5978 [18:33<50:23,  1.45it/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 1600 of 5978. Loss:0.35295894742012024.\n",
+            "\n",
+            "\n",
+            "Example output: What are some tips and tricks for mastering a computer game.Make sure to practice regularly - even if it’s only for a few minutes each day. \n",
+            "2  Break down the game into manageable chunks and set achievable goals. \n",
+            "3  Participate in team discussions and work together to solve problems.\n",
+            "4  Identify any issues you may have and delegate the tasks to the team.\n",
+            "5  Take breaks, and keep yourself motivated.\n",
+            "6  Take breaks from work to reward yourself.\n",
+            "7  Find a comfortable, quiet space and focus on learning the game.\n",
+            "8  Test yourself often to make sure you are having fun and enjoying the game.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 38%|███▊      | 900/2391 [12:34<20:42,  1.20it/s]"
+            " 33%|███▎      | 2000/5978 [23:15<45:47,  1.45it/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 2000 of 5978. Loss:0.5085460543632507.\n",
+            "\n",
+            "\n",
+            "Example output: What activities can I do with my family to stay active.Some activities you can do with your family to stay active include playing active video games, doing yoga, taking a nature walk, doing a workout, going for a hike, or biking. You can also watch a movie or read a book together, or listen to music.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 50%|█████     | 1200/2391 [16:44<16:32,  1.20it/s]"
+            " 40%|████      | 2400/5978 [27:53<41:10,  1.45it/s]"
           ]
         },
         {
           "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",
+            "Batch 2400 of 5978. Loss:0.458877831697464.\n",
+            "\n",
+            "\n",
+            "Example output: Which country has the best soccer team in the world.The answer to this will depend on who you ask. Some people may point to countries like Germany, Italy, or Spain as the best soccer teams to play in. Each has its own strengths and weaknesses and will create its own answer. For example, some people may point to Argentina as the best soccer team to play in because of their technical abilities, while others may point to Brazil as the best soccer team to play in based on their passion and passion for their sport. Ultimately, it really comes down to what you are looking for when evaluating a soccer team and who you are interested in playing with.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 63%|██████▎   | 1500/2391 [20:55<12:22,  1.20it/s]"
+            " 47%|████▋     | 2800/5978 [32:35<36:34,  1.45it/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 2800 of 5978. Loss:0.8314157724380493.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 75%|███████▌  | 1800/2391 [25:08<08:12,  1.20it/s]"
+            "\r 47%|████▋     | 2801/5978 [32:36<48:37,  1.09it/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",
+            "Example output: What are some words that rhyme with orangeorange, orange, orange, orange, orange, orange, orange, orange, orange\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 2100/2391 [29:19<04:02,  1.20it/s]"
+            " 54%|█████▎    | 3200/5978 [37:12<31:58,  1.45it/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 3200 of 5978. Loss:0.5375759601593018.\n",
+            "\n",
+            "\n",
+            "Example output: What are some beginner tips for learning guitar.Start with simple songs such as 'Happy on Their Own' by Phish.\n",
+            "2. Learn chords and scales on guitar.\n",
+            "3. Practice and refine your technique.\n",
+            "4. Learn improvisation and practice scales and chords together.\n",
+            "5. Learn to play along with songs.\n",
+            "6. Learn to play along with songs written by other musicians.\n",
+            "7. Listen to recordings of professional guitarists.\n",
+            "8. Use online lessons and tutorials to learn the basics.\n",
+            "9. Play and enjoy playing guitar.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2391/2391 [33:24<00:00,  1.19it/s]\n"
+            " 60%|██████    | 3600/5978 [41:52<27:21,  1.45it/s]"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Average Training Loss: 0.5702004936986865.\n",
-            "Validation loss: 0.5858413227518698.\n",
-            "Beginning epoch 3 of 3\n"
+            "\n",
+            "Batch 3600 of 5978. Loss:0.5550445914268494.\n",
+            "\n",
+            "\n",
+            "Example output: I'm applying for a new job and need to know what to expect in an interview in my industry.In an interview for your industry, you should expect the interviewer to ask a variety of thoughtful questions to demonstrate that you understand and demonstrate your enthusiasm for the role. Additionally, it is important to be honest, clear, and respectful throughout the interview.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 13%|█▎        | 300/2391 [04:10<29:03,  1.20it/s]"
+            " 67%|██████▋   | 4000/5978 [46:31<22:45,  1.45it/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 4000 of 5978. Loss:0.33791348338127136.\n",
+            "\n",
+            "\n",
+            "Example output: What are the best YouTube channels for cooking tutorials.The best YouTube channels for cooking tutorials are those that provide step-by-step instructions for simple cooking projects. They usually focus on food basics like boiling, roasting, or grilling, and usually have tutorials on the basics of basic recipes. YouTube also has channels that offer cooking tutorials in a variety of cuisines, from traditional Chinese to West African. Additionally, YouTube has a wealth of cooking instructional materials ranging from video tutorials to infographics and much more. Good luck.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
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+            " 74%|███████▎  | 4400/5978 [51:11<18:09,  1.45it/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 4400 of 5978. Loss:0.47749656438827515.\n",
+            "\n",
+            "\n",
+            "Example output: Can you provide a recipe for making tomato soup.Ingredients:\n",
+            "- 3 cups diced onion\n",
+            "- 1 onion, diced\n",
+            "- 2 cloves garlic, minced\n",
+            "- 1 pound beef stew meat\n",
+            "- 1 teaspoon Worcestershire sauce\n",
+            "- 1 teaspoon dried basil\n",
+            "- 1 teaspoon dried thyme\n",
+            "- 1 teaspoon ground coriander\n",
+            "- 1/2 teaspoon paprika\n",
+            "- 1/2 teaspoon cumin\n",
+            "- 1/4 teaspoon garlic powder\n",
+            "- Salt and pepper, to taste\n",
+            "\n",
+            "Instructions:\n",
+            "1. Heat a large pot over medium heat.\n",
+            "2. Add the onion, garlic, and beef. Cook until the beef is softened and the onion is fragrant, about 15 minutes.\n",
+            "3. Add the Worcestershire sauce and basil, and cook for 1 minute more.\n",
+            "4. Add the thyme, coriander, paprika, and cumin and cook for 1 minute more.\n",
+            "5. Stir in the soup broth\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
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+            " 80%|████████  | 4800/5978 [55:55<13:33,  1.45it/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 4800 of 5978. Loss:0.4787990152835846.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 50%|█████     | 1200/2391 [16:47<16:32,  1.20it/s]"
+            "\r 80%|████████  | 4801/5978 [55:57<23:44,  1.21s/it]"
           ]
         },
         {
           "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",
+            "Example output: Can you explain updateless decision theory?UDT is a decision theory that aims to solve an issue present in decision-making theories, which is the need to consider the decision maker as a component of the environment in which the decisions are made.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 63%|██████▎   | 1500/2391 [20:59<12:22,  1.20it/s]"
+            " 87%|████████▋ | 5200/5978 [1:00:33<08:57,  1.45it/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"
+            "\n",
+            "Batch 5200 of 5978. Loss:0.311775803565979.\n",
+            "\n",
+            "\n",
+            "Example output: I'm having trouble with algebra, can you help me solve this equation.I'm sorry, I'm not able to help you with algebra equations. First, let's look at some examples. Let's consider an equation with solutions to it. What is the equation y=x+2 and what is the second equation y=2+x? First, we need to know what is the x-coordinates of the two equations. Then we need to know the solutions for the first equation. Once we have all of this information, we can use algebra to solve the equation y=2+x.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 75%|███████▌  | 1800/2391 [25:10<08:13,  1.20it/s]"
+            " 94%|█████████▎| 5600/5978 [1:05:13<04:20,  1.45it/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"
+            "\n",
+            "Batch 5600 of 5978. Loss:0.44621706008911133.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 2100/2391 [29:22<04:02,  1.20it/s]"
+            "\r 94%|█████████▎| 5601/5978 [1:05:15<06:27,  1.03s/it]"
           ]
         },
         {
           "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"
+            "\n",
+            "Example output: Can you define data augmentation?Data Augmentation is a method that generates \"fake samples\" by modifying the training data to create synthetic instances of the true data.\n",
+            "\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2391/2391 [33:27<00:00,  1.19it/s]\n"
+            "100%|██████████| 5978/5978 [1:09:35<00:00,  1.43it/s]\n"
           ]
         },
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Average Training Loss: 0.4873914483300516.\n",
-            "Validation loss: 0.5831270120421747.\n",
+            "\n",
+            "Average Training Loss: 0.46858145299066306.\n",
+            "\n",
+            "\n",
+            "Validation loss: 0.5825508199688187.\n",
+            "\n",
             "Training complete!\n"
           ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "('/content/drive/MyDrive/Colab Notebooks/Aira-774M/tokenizer_config.json',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-774M/special_tokens_map.json',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-774M/vocab.json',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-774M/merges.txt',\n",
+              " '/content/drive/MyDrive/Colab Notebooks/Aira-774M/added_tokens.json')"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -1359,19 +1657,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 439
         },
         "id": "J1-hAY9Av4gT",
-        "outputId": "d35d961f-d9cc-4813-8963-817b43a19c19"
+        "outputId": "d8909676-fba0-470f-80cd-22d29cf2ecb1"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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             "text/plain": [
               "<Figure size 1200x600 with 1 Axes>"
             ]
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     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
       "metadata": {
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+        "colab": {
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+        },
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       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter your question: What is the capital of France?\n",
+            "Question: 👤 What is the capital of France?\n",
+            "\n",
+            "Response 1: 🤖 The capital of France is Paris.\n",
+            "Response 2: 🤖 The capital of France is Paris.\n"
+          ]
+        }
+      ],
       "source": [
         "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
         "import torch\n",
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