codeWriter.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.2/102.2 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip3 install -q -U bitsandbytes peft trl accelerate datasets transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        "    )"
      ],
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        "id": "RiimLVdZJE4m"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
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        "from huggingface_hub import notebook_login\n",
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        },
        "id": "gHf3nOEIMYzU",
        "outputId": "87d0b9b5-bf95-461d-96aa-a0d19f39e54d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"google/gemma-2b-it\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,quantization_config = bnb_config, device_map = {\"\":0})\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, add_eos_token=True)"
      ],
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        "outputId": "cb4e9976-83cb-4a92-aa34-eb4efe047d13"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/627 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/13.5k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
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        {
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          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Gemma's activation function should be approximate GeLU and not exact GeLU.\n",
            "Changing the activation function to `gelu_pytorch_tanh`.if you want to use the legacy `gelu`, edit the `model.config` to set `hidden_activation=gelu`   instead of `hidden_act`. See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "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": "aa2bb1b904194e1c912041c73ef41875"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc3c2f7537274689adc003966a0996e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/888 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f16e090a8ac4b349d851356b847b739"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def inference(query:str,model,tokenizer) -> str:\n",
        "  device = \"cuda:0\"\n",
        "\n",
        "  prompt_template = '''\n",
        "  bos><start_of_turn>user\n",
        "  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "  {query}\n",
        "  <end_of_turn>\\n<start_of_turn>model\n",
        "\n",
        "\n",
        "  '''\n",
        "\n",
        "  prompt = prompt_template.format(query = query)\n",
        "\n",
        "  model_inputs = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True).to(device)\n",
        "  generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n",
        "  decoded = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
        "  return (decoded)"
      ],
      "metadata": {
        "id": "tbiopBkeKWWO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = inference(query=\"Write a bubble sort code in c++.\", model=model, tokenizer=tokenizer)\n",
        "print(result)"
      ],
      "metadata": {
        "id": "wK0naL7rLq2V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"TokenBender/code_instructions_122k_alpaca_style\",split=\"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "id": "l3eIncuBNjBS",
        "outputId": "67cd94c9-6b91-440c-f50b-30290c327bda"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbea2a48c28b454a9f8947f2a014f38f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/169M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afdc590b01904d539ca0f7004104a6b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84bcbdefc4fa48e29408bf2b00858f4a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset.to_pandas()\n",
        "df.head(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "RbinHG8rPgkr",
        "outputId": "989e75f6-89e3-4308-e4b8-ea09740522a7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  \\\n",
              "0  Below is an instruction that describes a task....   \n",
              "1  Below is an instruction that describes a task....   \n",
              "2  Below is an instruction that describes a task....   \n",
              "3  Below is an instruction that describes a task....   \n",
              "4  Below is an instruction that describes a task....   \n",
              "5  Below is an instruction that describes a task....   \n",
              "6  Below is an instruction that describes a task....   \n",
              "7  Below is an instruction that describes a task....   \n",
              "\n",
              "                                              output  \\\n",
              "0  # Python code\\ndef sum_sequence(sequence):\\n  ...   \n",
              "1  def add_strings(str1, str2):\\n    \"\"\"This func...   \n",
              "2  #include <map>\\n#include <string>\\n\\nclass Gro...   \n",
              "3  def bubble_sort(arr):\\n    n = len(arr)\\n \\n  ...   \n",
              "4  import UIKit\\n\\nclass ExpenseViewController: U...   \n",
              "5  <?php\\n$timestamp = $_GET['timestamp'];\\n\\nif(...   \n",
              "6  import requests\\nimport re\\n\\ndef crawl_websit...   \n",
              "7                 [x*x for x in [1, 2, 3, 5, 8, 13]]   \n",
              "\n",
              "                                               input  \\\n",
              "0                                    [1, 2, 3, 4, 5]   \n",
              "1                    str1 = \"Hello \"\\nstr2 = \"world\"   \n",
              "2                                                      \n",
              "3                                 [3, 1, 4, 5, 9, 0]   \n",
              "4                                     Not applicable   \n",
              "5                                     Not Applicable   \n",
              "6  website: www.example.com \\ndata to crawl: phon...   \n",
              "7                                                      \n",
              "\n",
              "                                         instruction  \n",
              "0  Create a function to calculate the sum of a se...  \n",
              "1       Develop a function that will add two strings  \n",
              "2  Design a data structure in C++ to store inform...  \n",
              "3  Implement a sorting algorithm to sort a given ...  \n",
              "4  Design a Swift application for tracking expens...  \n",
              "5  Create a REST API to convert a UNIX timestamp ...  \n",
              "6  Generate a Python code for crawling a website ...  \n",
              "7  Create a Python list comprehension to get the ...  "
            ],
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              "      <td>Generate a Python code for crawling a website ...</td>\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "  }\n",
              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  @keyframes spin {\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(data_point):\n",
        "  prefix_text = 'Below is an instruction that describes a task. Write a response that ' \\\n",
        "               'appropriately completes the request.\\n\\n'\n",
        "  if data_point['input']:\n",
        "    text = f\"\"\"<start_of_turn>user {prefix_text} {data_point[\"instruction\"]} here are the inputs {data_point[\"input\"]} <end_of_turn>\\n<start_of_turn>model{data_point[\"output\"]} <end_of_turn>\"\"\"\n",
        "  else:\n",
        "    text = f\"\"\"<start_of_turn>user {prefix_text} {data_point[\"instruction\"]} <end_of_turn>\\n<start_of_turn>model{data_point[\"output\"]} <end_of_turn>\"\"\"\n",
        "\n",
        "  return text"
      ],
      "metadata": {
        "id": "2tvTmxw_PpKc"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_column = [generate_prompt(data_point) for data_point in dataset]\n",
        "dataset = dataset.add_column(\"prompt\",text_column)"
      ],
      "metadata": {
        "id": "W5DZO0NMSuPS"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset = dataset.map(generate_prompt)"
      ],
      "metadata": {
        "id": "BRToEv8_Sa0z"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.shuffle(seed=1234)\n",
        "dataset = dataset.map(lambda samples: tokenizer(samples[\"prompt\"]), batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c63ea6e3defd419588850412af92e9f6",
            "4af453f26d724d2499490a8b75c94c56",
            "76c5a96bae8e4a46869e8b4087993311",
            "63030b30e28e40be89d417b7628cb4ea",
            "9e408e9156c94f09accbae3f45b9bb7d",
            "06675a0f73bd45208c349309489b4128",
            "b3feab6f803e4b3cbf980c3c7775e109",
            "fbd94cd3dc2d4bb1addb4fe8566d5a7c",
            "4acb6ce721e445a0955115121adf5932",
            "600b0ca7b6fb43209cc0b7f5e72c6912",
            "a23b8ef4321c4466af5be73822ca16c9"
          ]
        },
        "id": "fiWbMguiUIBy",
        "outputId": "592e122d-e4a8-41de-9fea-657571dc26e7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/121959 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c63ea6e3defd419588850412af92e9f6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.train_test_split(test_size=0.2)\n",
        "train_data = dataset[\"train\"]\n",
        "test_data = dataset[\"test\"]"
      ],
      "metadata": {
        "id": "1HZTiAIEUk70"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.to_pandas().head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MgZ4ASp2VxSJ",
        "outputId": "33a1e6f7-4104-4838-eab1-06be78bafea8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  \\\n",
              "0  Below is an instruction that describes a task....   \n",
              "1  Below is an instruction that describes a task....   \n",
              "2  Below is an instruction that describes a task....   \n",
              "3  Below is an instruction that describes a task....   \n",
              "4  Below is an instruction that describes a task....   \n",
              "5  Below is an instruction that describes a task....   \n",
              "6  Below is an instruction that describes a task....   \n",
              "7  Below is an instruction that describes a task....   \n",
              "8  Below is an instruction that describes a task....   \n",
              "9  Below is an instruction that describes a task....   \n",
              "\n",
              "                                              output  \\\n",
              "0  create table user_accounts (\\n    user_id int ...   \n",
              "1  def distance(x1, y1, x2, y2):\\n    return ((x2...   \n",
              "2  <form action=\"\">\\n  <label for=\"name\">Name</la...   \n",
              "3  def get_target_sum_pair(arr, target_sum):\\n   ...   \n",
              "4  @app.route(\"/get-data\", methods=['GET'])\\ndef ...   \n",
              "5  def capitalize_words(s):\\n    words_list = s.s...   \n",
              "6  def add_one(num):\\n    return num + 1\\n\\nprint...   \n",
              "7  def find_combinations(N, K):\\n    result = []\\...   \n",
              "8  import * as InAppPurchases from 'expo-in-app-p...   \n",
              "9  def max_element(arr):\\n    max = arr[0]\\n    f...   \n",
              "\n",
              "                              input  \\\n",
              "0                    Not applicable   \n",
              "1                                     \n",
              "2                                     \n",
              "3          [1, 2, 3, 4, 5], sum = 7   \n",
              "4                                     \n",
              "5  my_string = \"i love programming\"   \n",
              "6                                     \n",
              "7                      N = 5, K = 3   \n",
              "8                    Not applicable   \n",
              "9                [5, 6, 2, 7, 1, 8]   \n",
              "\n",
              "                                         instruction  \\\n",
              "0  Create a MySQL database table for user account...   \n",
              "1  Generate a program to calculate the distance b...   \n",
              "2  Construct an HTML form with three text inputs ...   \n",
              "3  Design an algorithm that takes an array of int...   \n",
              "4  Create a REST API endpoint that can read data ...   \n",
              "5  Take the following string, and capitalize all ...   \n",
              "6  Create a function that takes any number and ad...   \n",
              "7  Generate a code for a function that takes two ...   \n",
              "8  Implement an In-App Purchase flow in React nat...   \n",
              "9  Create an appropriate method to find the maxim...   \n",
              "\n",
              "                                              prompt  \\\n",
              "0  <start_of_turn>user Below is an instruction th...   \n",
              "1  <start_of_turn>user Below is an instruction th...   \n",
              "2  <start_of_turn>user Below is an instruction th...   \n",
              "3  <start_of_turn>user Below is an instruction th...   \n",
              "4  <start_of_turn>user Below is an instruction th...   \n",
              "5  <start_of_turn>user Below is an instruction th...   \n",
              "6  <start_of_turn>user Below is an instruction th...   \n",
              "7  <start_of_turn>user Below is an instruction th...   \n",
              "8  <start_of_turn>user Below is an instruction th...   \n",
              "9  <start_of_turn>user Below is an instruction th...   \n",
              "\n",
              "                                           input_ids  \\\n",
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              "1  [2, 106, 1645, 30641, 603, 671, 14239, 674, 19...   \n",
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              "      <td>create table user_accounts (\\n    user_id int ...</td>\n",
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              "      <td>[1, 2, 3, 4, 5], sum = 7</td>\n",
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              "      <td>@app.route(\"/get-data\", methods=['GET'])\\ndef ...</td>\n",
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              "      <td>&lt;start_of_turn&gt;user Below is an instruction th...</td>\n",
              "      <td>[2, 106, 1645, 30641, 603, 671, 14239, 674, 19...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Below is an instruction that describes a task....</td>\n",
              "      <td>def capitalize_words(s):\\n    words_list = s.s...</td>\n",
              "      <td>my_string = \"i love programming\"</td>\n",
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              "      <td>&lt;start_of_turn&gt;user Below is an instruction th...</td>\n",
              "      <td>[2, 106, 1645, 30641, 603, 671, 14239, 674, 19...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Below is an instruction that describes a task....</td>\n",
              "      <td>def add_one(num):\\n    return num + 1\\n\\nprint...</td>\n",
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              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
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              "      <td>[2, 106, 1645, 30641, 603, 671, 14239, 674, 19...</td>\n",
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              "    <tr>\n",
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              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Implement an In-App Purchase flow in React native using the Expo in-app-purchases API. ### Input: Not applicable ### Output: import * as InAppPurchases from 'expo-in-app-purchases';\\n\\nconst doPurchase = async () => {\\n try {\\n const products = await InAppPurchases.getProductsAsync(['productId1', 'productId2']);\\n if (products.length > 0) {        \\n const result = await InAppPurchases.requestPurchaseAsync(product.id);        \\n if (result.purchased) {\\n // product purchased, handle success state\\n } else {\\n // product not purchased, handle failure state\\n }\\n } \\n } catch (err) {\\n // handle error\\n }\\n};\",\n          \"Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Generate a program to calculate the distance between two points in a coordinate plane. ### Input: No input ### Output: def distance(x1, y1, x2, y2):\\n    return ((x2 - x1)**2 + (y2 - y1)**2)**0.5\",\n          \"Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Take the following string, and capitalize all the words in it. ### Input: my_string = \\\"i love programming\\\" ### Output: def capitalize_words(s):\\n    words_list = s.split()\\n    capitalize_words_list = [word.capitalize() for word in words_list]\\n    return \\\" \\\".join(capitalize_words_list)\\n\\ncapitalized = capitalize_words(my_string)\\nprint(capitalized) # I Love Programming\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"import * as InAppPurchases from 'expo-in-app-purchases';\\n\\nconst doPurchase = async () => {\\n try {\\n const products = await InAppPurchases.getProductsAsync(['productId1', 'productId2']);\\n if (products.length > 0) {        \\n const result = await InAppPurchases.requestPurchaseAsync(product.id);        \\n if (result.purchased) {\\n // product purchased, handle success state\\n } else {\\n // product not purchased, handle failure state\\n }\\n } \\n } catch (err) {\\n // handle error\\n }\\n};\",\n          \"def distance(x1, y1, x2, y2):\\n    return ((x2 - x1)**2 + (y2 - y1)**2)**0.5\",\n          \"def capitalize_words(s):\\n    words_list = s.split()\\n    capitalize_words_list = [word.capitalize() for word in words_list]\\n    return \\\" \\\".join(capitalize_words_list)\\n\\ncapitalized = capitalize_words(my_string)\\nprint(capitalized) # I Love Programming\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Not applicable\",\n          \"\",\n          \"[5, 6, 2, 7, 1, 8]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Implement an In-App Purchase flow in React native using the Expo in-app-purchases API.\",\n          \"Generate a program to calculate the distance between two points in a coordinate plane.\",\n          \"Take the following string, and capitalize all the words in it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"<start_of_turn>user Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n Implement an In-App Purchase flow in React native using the Expo in-app-purchases API. here are the inputs Not applicable <end_of_turn>\\n<start_of_turn>modelimport * as InAppPurchases from 'expo-in-app-purchases';\\n\\nconst doPurchase = async () => {\\n try {\\n const products = await InAppPurchases.getProductsAsync(['productId1', 'productId2']);\\n if (products.length > 0) {        \\n const result = await InAppPurchases.requestPurchaseAsync(product.id);        \\n if (result.purchased) {\\n // product purchased, handle success state\\n } else {\\n // product not purchased, handle failure state\\n }\\n } \\n } catch (err) {\\n // handle error\\n }\\n}; <end_of_turn>\",\n          \"<start_of_turn>user Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n Generate a program to calculate the distance between two points in a coordinate plane. <end_of_turn>\\n<start_of_turn>modeldef distance(x1, y1, x2, y2):\\n    return ((x2 - x1)**2 + (y2 - y1)**2)**0.5 <end_of_turn>\",\n          \"<start_of_turn>user Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n Take the following string, and capitalize all the words in it. here are the inputs my_string = \\\"i love programming\\\" <end_of_turn>\\n<start_of_turn>modeldef capitalize_words(s):\\n    words_list = s.split()\\n    capitalize_words_list = [word.capitalize() for word in words_list]\\n    return \\\" \\\".join(capitalize_words_list)\\n\\ncapitalized = capitalize_words(my_string)\\nprint(capitalized) # I Love Programming <end_of_turn>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"input_ids\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attention_mask\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "TY-wwo-3UvRy"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUz32ggVWfyd",
        "outputId": "89fb2864-c494-47d1-a8ef-3f7cd0ef3613"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GemmaForCausalLM(\n",
              "  (model): GemmaModel(\n",
              "    (embed_tokens): Embedding(256000, 2048, padding_idx=0)\n",
              "    (layers): ModuleList(\n",
              "      (0-17): 18 x GemmaDecoderLayer(\n",
              "        (self_attn): GemmaSdpaAttention(\n",
              "          (q_proj): Linear4bit(in_features=2048, out_features=2048, bias=False)\n",
              "          (k_proj): Linear4bit(in_features=2048, out_features=256, bias=False)\n",
              "          (v_proj): Linear4bit(in_features=2048, out_features=256, bias=False)\n",
              "          (o_proj): Linear4bit(in_features=2048, out_features=2048, bias=False)\n",
              "          (rotary_emb): GemmaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): GemmaMLP(\n",
              "          (gate_proj): Linear4bit(in_features=2048, out_features=16384, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=2048, out_features=16384, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=16384, out_features=2048, bias=False)\n",
              "          (act_fn): PytorchGELUTanh()\n",
              "        )\n",
              "        (input_layernorm): GemmaRMSNorm()\n",
              "        (post_attention_layernorm): GemmaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): GemmaRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=2048, out_features=256000, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import bitsandbytes as bnb\n",
        "def find_all_linear_names(model):\n",
        "  cls = bnb.nn.Linear4bit #if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n",
        "  lora_module_names = set()\n",
        "  for name, module in model.named_modules():\n",
        "    if isinstance(module, cls):\n",
        "      names = name.split('.')\n",
        "      lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
        "    if 'lm_head' in lora_module_names: # needed for 16-bit\n",
        "      lora_module_names.remove('lm_head')\n",
        "  return list(lora_module_names)\n",
        "\n",
        "modules = find_all_linear_names(model)\n",
        "print(modules)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ng3npXS9WgyG",
        "outputId": "f54ec963-4fb7-4134-c6f2-13f905db16db"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['q_proj', 'k_proj', 'gate_proj', 'o_proj', 'down_proj', 'v_proj', 'up_proj']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig(\n",
        "    r = 64,\n",
        "    lora_alpha=32,\n",
        "    target_modules=modules,\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "0OtrqTFxW3su"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainable, total = model.get_nb_trainable_parameters()\n",
        "print(f\"Trainable: {trainable} | total: {total} | Percentage: {trainable/total*100:.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENgtN6Jibmlo",
        "outputId": "5babd047-500d-4e6b-88d0-1fa789b3b3a9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainable: 78446592 | total: 2584619008 | Percentage: 3.0351%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, DataCollatorForLanguageModeling\n",
        "from trl import SFTTrainer\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=test_data,\n",
        "    dataset_text_field=\"prompt\",\n",
        "    peft_config=lora_config,\n",
        "    args=TrainingArguments(\n",
        "        per_device_train_batch_size=2,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=0.03,\n",
        "        max_steps=200,\n",
        "        learning_rate=2e-4,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        save_strategy=\"epoch\",\n",
        "    ),\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222,
          "referenced_widgets": [
            "697a6c31d78f4768b42bcec704d0e1d2",
            "ee5d77ad6c234795a9f191c90fc62000",
            "082a1dff3ec942e380d9e43bf387f68a",
            "a03fca3487344e5a8ae65b02b3de0929",
            "d1ce9af9dda54a398a561016ba547d3b",
            "cc7fe14ee1e44e2c87fb36f90d19773b",
            "2af3c1298d5342f4b0fa9b2acf0bef63",
            "2c3008283f3e4e238d943ea6e4fb5f47",
            "1d8b674ba8e147b0a4e8510131d9916c",
            "3c1e35ffd03a4c6a83cff31ac873f40d",
            "1da297d756294fe1af175e035a4e6462",
            "8fafda4e61e743d69940707ea66cba90",
            "ffbaf780faf047e4922e7931f37f724c",
            "3491148ffd684e1e851b13de8aab6cde",
            "80e0901b21ec452fad6eaaaaf24295b9",
            "1a35ae786d324b3896a9628267b9fab0",
            "cc48f4f90d804bc8987095acb3d4664c",
            "908aa11e5d504f7e97a946579a5cb09f",
            "fc7e6102f52b41f3b4ae681727aeeb27",
            "b246e7e453ee4c228d4e9e35786527be",
            "6d61719b6941428e8f1ce7bcba35943b",
            "a5efc3a1f1bb41349890db5db94bf740"
          ]
        },
        "id": "wHAmHL3Fbvn0",
        "outputId": "b9cdcc0b-2570-40a1-b44e-842604eb84d0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:245: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/97567 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "697a6c31d78f4768b42bcec704d0e1d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/24392 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fafda4e61e743d69940707ea66cba90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:317: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.use_cache = False\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dtrh1cDQdyic",
        "outputId": "6d6f94d7-1851-43e9-bcbd-a6008c685408"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 35:43, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.165100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.013100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.116000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.534500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.825800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.967200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.620400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.557700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.685100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.132500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.174500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.144600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.116100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.092100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.404700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.840900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.959400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.021700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.949900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.120400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.901900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.887600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.875500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.783800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.753800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.700400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.109300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.906800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.840900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.207100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.851600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.756200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.876500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.781700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.899700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.882900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.764400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.699100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.752600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.699300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.710500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.848000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.822700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.087700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.691900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.763800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.746100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.705600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.714600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.856400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.798600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.793300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.867500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.802000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.631600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.788100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.022700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.698600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.812300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.542900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.693400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.594100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.835800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.586100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>1.118400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.578700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.700700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.665800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.671300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.679400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.742000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.672600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.633000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.627500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.572800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.633300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.635100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.755900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.647100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.706000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.594300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.562500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.708300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.618100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.841800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.705800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.654800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.763100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.512200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.664000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.699200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.547500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.718300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.691500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.643400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.640000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.759700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.559200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.630800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.646800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>0.597300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.576500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.609100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.645700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>0.661600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.606000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.552300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>0.602400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.769500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>0.604800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>0.635100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>0.632300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>0.656100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>0.604200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>0.661900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>0.707700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>0.691300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>0.665600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.759000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121</td>\n",
              "      <td>0.612800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122</td>\n",
              "      <td>0.697800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>0.642800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>0.740800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.993300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126</td>\n",
              "      <td>0.620600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127</td>\n",
              "      <td>0.555300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>0.754900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129</td>\n",
              "      <td>0.788900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.678900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131</td>\n",
              "      <td>0.619400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132</td>\n",
              "      <td>0.619600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133</td>\n",
              "      <td>0.653400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134</td>\n",
              "      <td>0.807800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135</td>\n",
              "      <td>0.601200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136</td>\n",
              "      <td>0.773800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137</td>\n",
              "      <td>0.748400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138</td>\n",
              "      <td>0.756500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139</td>\n",
              "      <td>0.710400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.913400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141</td>\n",
              "      <td>0.707800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142</td>\n",
              "      <td>0.715900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143</td>\n",
              "      <td>0.669000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144</td>\n",
              "      <td>0.681200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145</td>\n",
              "      <td>0.726500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146</td>\n",
              "      <td>0.652000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147</td>\n",
              "      <td>0.660900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148</td>\n",
              "      <td>0.834300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149</td>\n",
              "      <td>0.594900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.546100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151</td>\n",
              "      <td>0.829100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152</td>\n",
              "      <td>0.893900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153</td>\n",
              "      <td>0.780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154</td>\n",
              "      <td>0.651200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>0.540500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156</td>\n",
              "      <td>0.818800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157</td>\n",
              "      <td>0.618200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158</td>\n",
              "      <td>0.642100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159</td>\n",
              "      <td>0.613500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.599600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161</td>\n",
              "      <td>0.689500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162</td>\n",
              "      <td>0.886000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163</td>\n",
              "      <td>0.664400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>0.802000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165</td>\n",
              "      <td>0.775900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166</td>\n",
              "      <td>0.846100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167</td>\n",
              "      <td>0.782600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168</td>\n",
              "      <td>0.607200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169</td>\n",
              "      <td>0.812000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>0.665600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171</td>\n",
              "      <td>0.741500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172</td>\n",
              "      <td>0.641300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173</td>\n",
              "      <td>0.594600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174</td>\n",
              "      <td>0.582300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.755300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176</td>\n",
              "      <td>0.581600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177</td>\n",
              "      <td>0.671500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178</td>\n",
              "      <td>0.674400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179</td>\n",
              "      <td>0.684700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.542500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181</td>\n",
              "      <td>0.591300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182</td>\n",
              "      <td>0.629400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183</td>\n",
              "      <td>0.963700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184</td>\n",
              "      <td>0.697600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185</td>\n",
              "      <td>0.620500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186</td>\n",
              "      <td>0.709600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187</td>\n",
              "      <td>0.594500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188</td>\n",
              "      <td>0.619600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189</td>\n",
              "      <td>0.584100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>0.699600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191</td>\n",
              "      <td>0.515300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192</td>\n",
              "      <td>0.574100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193</td>\n",
              "      <td>0.666700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194</td>\n",
              "      <td>0.846600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195</td>\n",
              "      <td>0.857100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196</td>\n",
              "      <td>0.647600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197</td>\n",
              "      <td>0.648800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198</td>\n",
              "      <td>0.492400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199</td>\n",
              "      <td>0.710600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.642400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=200, training_loss=0.8043752980232238, metrics={'train_runtime': 2156.6284, 'train_samples_per_second': 0.742, 'train_steps_per_second': 0.093, 'total_flos': 4964227461267456.0, 'train_loss': 0.8043752980232238, 'epoch': 0.02})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fine_tuned_model = \"gemma-2b-it-code-finetuned\""
      ],
      "metadata": {
        "id": "lNRvLYI-ernE"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.model.save_pretrained(fine_tuned_model)"
      ],
      "metadata": {
        "id": "n_wp68Y0fVhu"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "merge the lora configured model with the base model and save the model and tokenizer to push it to the hub"
      ],
      "metadata": {
        "id": "YYmKKKaZvy4z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map={\"\": 0},\n",
        ")\n",
        "merged_model= PeftModel.from_pretrained(base_model, fine_tuned_model)\n",
        "merged_model= merged_model.merge_and_unload()\n",
        "\n",
        "# Save the merged model\n",
        "merged_model.save_pretrained(\"merged_model\",safe_serialization=True)\n",
        "tokenizer.save_pretrained(\"merged_model\")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4f0468deb4984cd085de6e6de65ceecc",
            "8e35196cc99244c28f66479015d105c6",
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            "0ed79a3b4a36473a9676a6c08be67944",
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            "cb5bd3115a29474a94a374313500ecee",
            "7e41945a1e034e83bead2b3c31919692",
            "558c78205a1e4b43956b8230592cfea8",
            "3dd54c7d5a6b4715b9265db1b3118d90",
            "34040442723543bbbaf6302c53067931",
            "dfc6da6a1f204e63ba99fc206309971d"
          ]
        },
        "id": "lwiAnpnkfk4i",
        "outputId": "00314508-9e75-4427-b96e-6bf561067a4b"
      },
      "execution_count": 29,
      "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": "4f0468deb4984cd085de6e6de65ceecc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
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            "afc5046ae0da4e5095d4cf21813ebbb6"
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        },
        "id": "5QVYNBpnw2IF",
        "outputId": "65ecbb59-a770-42b0-89df-112247565bbe"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model.push_to_hub(fine_tuned_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(fine_tuned_model, use_temp_dir=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
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        },
        "id": "L-6K3ergvsd2",
        "outputId": "7adc168e-312d-4f9e-ec6e-4522e808ee91"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
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          "data": {
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              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
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              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/omparghale/gemma-2b-it-code-finetuned/commit/102cc5f5e0bf3d3dbfc1828763054b989ef26b58', commit_message='Upload tokenizer', commit_description='', oid='102cc5f5e0bf3d3dbfc1828763054b989ef26b58', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Test the fine-tuned model:**\n",
        "\n",
        "\n",
        "**Task 1:**<br>Write a c++ code for fibonacci sequence upto input n using recursion.<br><br>\n",
        "**Task 2:**<br>Your task is to minimize the function via Gradient Descent: f(x)=x^2.Write code in python. sample function declaration: def get_minimizer(self, iterations: int, learning_rate: float, init: int)"
      ],
      "metadata": {
        "id": "8r1SUc4C2P0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gen_code = inference(query = '''''',model=merged_model,tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Y_YMb3NwdKE",
        "outputId": "74a509dc-ee06-4808-96de-fd66d62e27ac"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(gen_code)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRLj8SrHz7Iv",
        "outputId": "6fc8cc4e-e74c-4a4c-f0f3-ada0e992e3ba"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  bos>user\n",
            "  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "  Your task is to minimize the function via Gradient Descent: f(x)=x^2.Write code in python. sample function declaration: def get_minimizer(self, iterations: int, learning_rate: float, init: int)\n",
            "  \n",
            "model\n",
            "\n",
            "\n",
            "  ```python\n",
            "import numpy as np\n",
            "\n",
            "def get_minimizer(self, iterations: int, learning_rate: float, init: int) -> None:\n",
            "    \"\"\"\n",
            "    This function minimizes the function f(x) = x^2 using gradient descent.\n",
            "\n",
            "    Args:\n",
            "        iterations (int): Number of iterations to run the gradient descent algorithm.\n",
            "        learning_rate (float): Learning rate for the gradient descent algorithm.\n",
            "        init (int): Index of the initial point to start gradient descent from.\n",
            "\n",
            "    Returns:\n",
            "        None\n",
            "    \"\"\"\n",
            "\n",
            "    # Set up the initial gradient descent step size\n",
            "    step_size = 0.01\n",
            "\n",
            "    # Initialize the gradient and set the initial position\n",
            "    gradient = np.zeros_like(init)\n",
            "    position = init\n",
            "\n",
            "    # Run the gradient descent algorithm for the specified number of iterations\n",
            "    for i in range(iterations):\n",
            "        # Calculate the gradient of the function\n",
            "        gradient = 2 * position\n",
            "\n",
            "        # Update the position using the gradient\n",
            "        position -= learning_rate * gradient\n",
            "\n",
            "        if np.linalg.norm(position - init) < 1e-6:\n",
            "            break\n",
            "\n",
            "    print(\"Minimum found at:\", position)\n",
            "```\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VzIJ6ECN0BcQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}