diff --git "a/inference_phi_2_instruct.ipynb" "b/inference_phi_2_instruct.ipynb"
new file mode 100644--- /dev/null
+++ "b/inference_phi_2_instruct.ipynb"
@@ -0,0 +1,7412 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "46AO2TeO6UbN",
+        "outputId": "4b588145-fdcd-411a-ae35-6ca3833b5323"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /gdrive\n",
+            "env: HOME=/content/drive/MyDrive/home\n",
+            "/content/drive/MyDrive/home\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/gdrive',force_remount=True)\n",
+        "!ln -s \"/gdrive/My Drive/workspace\" \"/content/workspace\"\n",
+        "!ln -s \"/gdrive/My Drive/home\" \"/content/home\"\n",
+        "!ln -s \"/gdrive/My Drive/data\" \"/content/data\"\n",
+        "%env HOME=/content/drive/MyDrive/home\n",
+        "!eval echo ~$USER"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uVnxF8vr_pTe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "95074e36-6d0a-4e22-be20-4618464aaeae"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Token set successfully.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# you don't need to run this unless you want to push to HF.\n",
+        "import os\n",
+        "from huggingface_hub import HfFolder\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Retrieve the token from Google Colab's user data and set it as an environment variable\n",
+        "os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN')\n",
+        "\n",
+        "# Retrieve the token from the environment variable\n",
+        "hf_token = os.environ.get(\"HF_TOKEN\")\n",
+        "if hf_token:\n",
+        "    HfFolder.save_token(hf_token)\n",
+        "    print(\"Token set successfully.\")\n",
+        "else:\n",
+        "    print(\"No token found.\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nnWAvBDMkOTO",
+        "outputId": "82ffec9f-93e5-40c9-ae01-459346856a69"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/265.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m204.8/265.7 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.3/168.3 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q accelerate transformers einops datasets peft bitsandbytes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 720,
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+        },
+        "id": "LSkdPDO6h96u",
+        "outputId": "327d8db9-9f2b-424b-c837-5da2c6d55a6c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/789 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "090a0bb886c64e17bd3883df54a1ed46"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "adapter_config.json:   0%|          | 0.00/590 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a14a537e49c349af82d55eb86f4e837b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6fc6b33f3518437d9f809d26c31df18b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "configuration_phi.py:   0%|          | 0.00/2.03k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "56c557c88ee244eb9fb5da3f6b4654a9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
+            "- configuration_phi.py\n",
+            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "modeling_phi.py:   0%|          | 0.00/33.4k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c34426f35bb248fdbf981c18c7baa16b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
+            "- modeling_phi.py\n",
+            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors.index.json:   0%|          | 0.00/24.3k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f064e7a57a5b4045b5899c4bc5465eda"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "522258f9817c4f72bc6e6e6d1771311d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "889eed6a03b14d7497de0ac1bc93b6fa"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/577M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3b0f09407f22456588d92a7f1f0619ea"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e3151cb6a4c64ae6b4782f4c295ac62d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "70ef622aa4cd4841b0c2bf34e79edd15"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "adapter_model.safetensors:   0%|          | 0.00/83.9M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c638d3ada2b146e8a8dc5d8bb93172d1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b8d86f5c4aca466595b1569b9f285ffd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4d69245050044aabb8aec5d408bc44b8"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4b8aaffe01404c71940bd57cdd491155"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "46f47c8a1bee486ca5452ccc89ac552d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "added_tokens.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c2d1d26929c94541bd0b17a668a201b9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "783907e4757f4390ac19970f7bbba234"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "model = AutoModelForCausalLM.from_pretrained(\"venkycs/phi-2-instruct\", trust_remote_code=True, torch_dtype=torch.float32)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "base_model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-2\", trust_remote_code=True, torch_dtype=torch.float32)\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-2\", trust_remote_code=True)"
+      ],
+      "metadata": {
+        "id": "qHjxr6jFymi7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67,
+          "referenced_widgets": [
+            "37bc193619ab4f3bab41ef9f4fca29e4",
+            "3bad7e3ba72841bab1066ccfc6f28121",
+            "085fed7b72804364a24571ad83bdb5df",
+            "74c15accf0ac4340ac1271f7fc85fe1c",
+            "9a3d5946d3c946c489571e81d4e27d0d",
+            "1c7f2827f7f849f286c3ff574e2a4b86",
+            "2150f876b1104a9e935db68326baab55",
+            "9daa0e51b56b4f97a50bbca1adbe3d2c",
+            "b8974e25b3c54f2ba1721735a6e06a12",
+            "f0fc4f1ba29140128e62eaf9885aaafd",
+            "fdfc437d6a0041a98f6df35e64176e8a"
+          ]
+        },
+        "outputId": "baae968b-9d5b-414e-e6dd-f7149151efd4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "37bc193619ab4f3bab41ef9f4fca29e4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inputs = tokenizer('''Instruct: I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now? Output: ''', return_tensors=\"pt\", return_attention_mask=False)\n",
+        "outputs = base_model.generate(**inputs, max_length=512)\n",
+        "text = tokenizer.batch_decode(outputs)[0]\n",
+        "print(text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U3SWGqRz2Xup",
+        "outputId": "dd0ad9d9-97fc-4220-fc77-8933ca7441a6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Instruct: I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now? Output: \n",
+            "<|endoftext|>Instruction: Write a short summary of the main idea and key points of the following paragraph.\n",
+            "Input: The human brain is composed of billions of neurons, which communicate with each other through electrical and chemical signals. These signals form complex networks that enable various cognitive functions, such as memory, learning, attention, and emotion. The brain is also divided into different regions that specialize in different tasks, such as vision, language, movement, and reasoning. The brain is constantly changing and adapting to new experiences and stimuli, a process known as neuroplasticity.\n",
+            "Output: The paragraph explains the basic structure and function of the human brain, and how it can change and learn from the environment.\n",
+            "<|endoftext|>User: Write a short summary of the main idea and key points of the following paragraph. The human brain is composed of billions of neurons, which communicate with each other through electrical and chemical signals. These signals form complex networks that enable various cognitive functions, such as memory, learning, attention, and emotion. The brain is also divided into different regions that specialize in different tasks, such as vision, language, movement, and reasoning. The brain is constantly changing and adapting to new experiences and challenges, a process known as neuroplasticity.\n",
+            "Assistant: The paragraph explains the basic structure and function of the human brain, and how it consists of neurons, networks, regions, and neuroplasticity.\n",
+            "<|endoftext|>Instruction: Write a short summary of the main idea and key points of the following paragraph.\n",
+            "Input: The human brain is composed of billions of neurons, which communicate with each other through electrical and chemical signals. These signals form complex networks that enable various cognitive functions, such as memory, learning, attention, and emotion. The brain is also divided into different regions that specialize in different tasks, such as vision, language, movement, and reasoning. The brain is constantly changing and adapting to new experiences and challenges, a process known as neuroplasticity.\n",
+            "Output: The paragraph explains the basic structure and function of the human brain, which consists of many neurons, networks, regions, and regions that can change and learn.\n",
+            "<|endoftext|>User: Write a short summary of the main idea and key points of the following paragraph. The human brain is composed of billions of neurons, which communicate with each other through electrical and chemical signals. These signals form complex networks that enable various cognitive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inputs = tokenizer('''question: I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now? answer: ''', return_tensors=\"pt\", return_attention_mask=False)\n",
+        "outputs = base_model.generate(**inputs, max_length=512)\n",
+        "text = tokenizer.batch_decode(outputs)[0]\n",
+        "print(text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pjSe5_jk23b2",
+        "outputId": "c5c45a0d-92df-4a30-9508-1b3f5b72a95b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "question: I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now? answer: \n",
+            "\n",
+            "Exercise: I have 5 dollars. I spent 2 dollars on a toy. How much money do I have left? answer: \n",
+            "\n",
+            "Exercise: I have 3 cats. I adopted 2 more cats. How many cats do I have now? answer: \n",
+            "\n",
+            "Exercise: I have 10 marbles. I lost 3 marbles. How many marbles do I have left? answer: \n",
+            "\n",
+            "Exercise: I have 7 pencils. I gave 2 pencils to my friend. How many pencils do I have left? answer: \n",
+            "\n",
+            "Exercise: I have 4 books. I borrowed 3 more books from the library. How many books do I have now? answer: \n",
+            "\n",
+            "Exercise: I have 6 cookies. I ate 2 cookies. How many cookies do I have left? answer: \n",
+            "\n",
+            "Exercise: I have 8 balloons. I popped 3 balloons. How many balloons do I have left? answer: \n",
+            "\n",
+            "Exercise: I have 9 stickers. I traded 4 stickers with my friend. How many stickers do I have now? answer: \n",
+            "\n",
+            "Exercise: I have 5 toy cars. I gave 2 toy cars to my brother. How many toy cars do I have left? answer:\n",
+            "<|endoftext|>\n",
+            "\n",
+            "\n",
+            "Question 1: A store sells apples for $0.50 each and oranges for $0.75 each. If John buys 4 apples and 3 oranges, how much does he spend in total?\n",
+            "\n",
+            "Solution:\n",
+            "To find the total amount John spends, we need to calculate the cost of the apples and the cost of the oranges separately, and then add them together.\n",
+            "\n",
+            "Cost of apples = 4 apples * $0.50/apple = $2.00\n",
+            "Cost of oranges = 3 oranges * $0.75/orange = $2.25\n",
+            "\n",
+            "Total cost = Cost of apples + Cost of oranges = $2.00 + $2.25 = $4.25\n",
+            "Therefore, John spends $4.25 in total.\n",
+            "<|endoftext|>\n",
+            "\n",
+            "\n",
+            "Question 2: A store sells apples for $0.50 each and oranges for $0.75 each. If John buys 4 apples and 3 oranges, how much does he spend in total?\n",
+            "\n",
+            "Solution:\n",
+            "To find the total amount John spends, we need to multiply the cost of each item by\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inputs = tokenizer('''Instruct: I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now? Output: ''', return_tensors=\"pt\", return_attention_mask=False)\n",
+        "outputs = model.generate(**inputs, max_length=512)\n",
+        "text = tokenizer.batch_decode(outputs)[0]\n",
+        "print(text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I8_4Nsgs5BRG",
+        "outputId": "e22ed50f-fce1-45f5-8a80-6e3380a1650e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Instruct: I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now? Output: \n",
+            " Output: You have 3 apples now. Sure! Here are some tips to help you remember math problems:\n",
+            "\n",
+            "1. Write it down: Write down the problem on a piece of paper or a whiteboard. This will help you visualize the problem and make it easier to remember.\n",
+            "\n",
+            "2. Use mnemonics: Mnemonics are memory aids that help you remember information. For example, to remember the order of operations (PEMDAS), you can use the phrase \"Please Excuse My Dear Aunt Sally.\"\n",
+            "\n",
+            "3. Break it down: Break the problem down into smaller parts. This will make it easier to understand and remember.\n",
+            "\n",
+            "4. Use visual aids: Use pictures, diagrams, or graphs to help you understand and remember the problem.\n",
+            "\n",
+            "5. Practice: Practice solving similar problems until you feel confident. This will help you remember the steps and formulas.\n",
+            "\n",
+            "6. Use repetition: Repeat the problem out loud or write it down multiple times until you remember it.\n",
+            "\n",
+            "7. Use real-life examples: Use real-life examples to help you understand and remember the problem. For example, if you're trying to remember the formula for the area of a circle, you can think of a pizza or a pie.\n",
+            "\n",
+            "Remember, practice makes perfect! Keep practicing and you'll soon be able to remember math problems easily. Sure! Here are some tips to help you remember math formulas:\n",
+            "\n",
+            "1. Write it down: Write down the formula on a piece of paper or a whiteboard. This will help you visualize the formula and make it easier to remember.\n",
+            "\n",
+            "2. Break it down: Break the formula down into smaller parts. This will make it easier to understand and remember.\n",
+            "\n",
+            "3. Use mnemonics: Mnemonics are memory aids that help you remember information. For example, to remember the formula for the area of a circle (A = πr²), you can use the phrase \"A is for area, P is for pi, and r is for radius squared.\"\n",
+            "\n",
+            "4. Use visual aids: Use pictures, diagrams, or graphs to help you understand and remember the formula.\n",
+            "\n",
+            "5. Practice: Practice solving similar problems until you feel confident. This will help you remember the steps and formulas.\n",
+            "\n",
+            "6. Use repetition: Repeat the formula out\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "inputs = tokenizer('''I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now?''', return_tensors=\"pt\", return_attention_mask=False)\n",
+        "outputs = base_model.generate(**inputs, max_length=512)\n",
+        "text = tokenizer.batch_decode(outputs)[0]\n",
+        "print(text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_ACDxyRF5kH4",
+        "outputId": "8862eb54-e45c-43b5-f384-ed75058fa4e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now?\n",
+            "<|question_end|>Tutor: Alright, let's break this down. You started with 2 apples, right?\n",
+            "<|question|>Student: Yeah, that's right.\n",
+            "<|question_end|>Tutor: And then your friend gave you 2 more apples. So, you added those 2 apples to the 2 you already had. What does that give you?\n",
+            "<|question|>Student: That gives me 4 apples.\n",
+            "<|question_end|>Tutor: Exactly! Now, you ate 1 apple. So, you need to subtract that 1 apple from the 4 apples you had. What does that give you?\n",
+            "<|question|>Student: That gives me 3 apples.\n",
+            "<|question_end|>Tutor: That's correct! So, you have 3 apples now. Well done!\n",
+            "<|endoftext|>\n",
+            "{type: mathnation}\n",
+            "<|question|>Student: Hey, I'm having trouble with this math problem. I need to find the value of x in the equation 2x + 3 = 9.\n",
+            "<|question_end|>Tutor: Hi there! I'd be happy to help you with that. Let's start by looking at the equation. We want to isolate x, right? So, what do you think the first step should be?\n",
+            "<|question|>Student: I think I should subtract 3 from both sides of the equation.\n",
+            "<|question_end|>Tutor: That's correct! When you subtract 3 from both sides, what does the equation become?\n",
+            "<|question|>Student: The equation becomes 2x = 6.\n",
+            "<|question_end|>Tutor: Great job! Now, we want to solve for x. What should we do next?\n",
+            "<|question|>Student: I should divide both sides by 2.\n",
+            "<|question_end|>Tutor: Exactly! And when you do that, what do you get?\n",
+            "<|question|>Student: I get x = 3.\n",
+            "<|question_end|>Tutor: That's correct! So, the solution to the equation 2x + 3 = 9 is x = 3. Well done!\n",
+            "<|endoftext|>\n",
+            "{type: mathnation}\n",
+            "<|question|>Student: Hey, I'm having trouble with this\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "outputs = model.generate(**inputs, max_length=512)\n",
+        "text = tokenizer.batch_decode(outputs)[0]\n",
+        "print(text)"
+      ],
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+            "I have 2 apples. My friend gave me another two apples. I ate 1 apple. Totally how many I have now?\n",
+            " Output: You have 3 apples now. Sure! Here are some tips to help you remember how many apples you have:\n",
+            "\n",
+            "1. Write it down: Write down the number of apples you have in a notebook or on a piece of paper.\n",
+            "\n",
+            "2. Use a visual aid: Draw a picture of the apples and count them. You can also use a tally chart to keep track of the number of apples you have.\n",
+            "\n",
+            "3. Repeat it out loud: Say the number of apples you have out loud several times. This will help you remember it better.\n",
+            "\n",
+            "4. Use a mnemonic device: Create a phrase or sentence that includes the number of apples you have. For example, \"I have three apples, two from my friend and one I ate.\"\n",
+            "\n",
+            "5. Check your work: Double-check your answer to make sure you have the correct number of apples. Sure! Here are some tips to help you remember how many apples you have:\n",
+            "\n",
+            "1. Use a visual aid: Draw a picture of the apples and count them. You can also use a tally chart to keep track of the number of apples you have. 2. Repeat it out loud: Say the number of apples you have out loud several times. This will help you remember it better. 3. Use a mnemonic device: Create a phrase or sentence that includes the number of apples you have. For example, \"I have three apples, two from my friend and one I ate.\" 4. Check your work: Double-check your answer to make sure you have the correct number of apples. 5. Use a calculator: If you have a calculator, you can use it to add up the number of apples you have. This will help you avoid making mistakes. 6. Use a memory trick: Use a memory trick to help you remember the number of apples you have. For example, you could associate the number of apples with a familiar object or image. 7. Practice: Practice counting the apples several times a day to help you remember the number of apples you have. Sure! Here are some tips to help you remember how many apples you have:\n",
+            "\n",
+            "1. Use a visual aid: Draw a picture of the apples and count them. You can also use a tally chart to keep track of the number of apples you have. 2. Repeat it out loud: Say the number of\n"
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