DeviVerse_update.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/psyuktha/DeviVerse/blob/main/DeviVerse_update.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DeviVerse\n",
        "\n",
        "DeviVerse is a project focused on fine-tuning a large language model (LLM) to delve deeply into the 100 shlokas of Soundarya Lahari. The aim is to create an AI system capable of understanding, interpreting, and providing insights into the profound philosophical, spiritual, and poetic elements of the text."
      ],
      "metadata": {
        "id": "WqeclQ73CHiY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the Dataset\n"
      ],
      "metadata": {
        "id": "mfjto4NlBWUD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "6UXRMhZ31c9_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/output.csv')\n",
        "df=df.iloc[:-2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "pe-38F2F5jeP"
      },
      "outputs": [],
      "source": [
        "df.to_csv('output.csv',index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQE_Z42kIMC0",
        "outputId": "a68d5dff-0be5-4c77-e622-d1c77bdd473c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "adf167c6cc964b4195219d05095355d0",
            "a6122bcae61a4e279906c604ff1c9ea8",
            "58e4c280d6244484b552427f265459cd",
            "76a69c78c7674c1fbdd6f1eaa240b08d",
            "bb3cac602a444a3d8f530a38c2131666",
            "70f1123ffe6f433b91d3a69ba7866298",
            "0c1d7475671a4a76ae3e8d082b5bb501",
            "c3a32b2bb024454391ca479783371fee",
            "2472225394c847f3a63905dbae468b99",
            "74c1e5471dd24ba58e99bbf66fc868e6",
            "251b39aeb5854fc698a584a629c413e5"
          ]
        },
        "id": "U4_1g9vs5rFy",
        "outputId": "aaf32810-dcd4-4f7a-929b-912efce4f4ec"
      },
      "outputs": [
        {
          "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": "adf167c6cc964b4195219d05095355d0"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"csv\", data_files='/content/output.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyU_iro2C35F",
        "outputId": "495b4cfe-9294-41fd-f0dd-25808e9c0456"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Verse', 'Instruction', 'Output'],\n",
              "        num_rows: 178\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "y1x6294bC7dR",
        "outputId": "76dda5e5-6028-432a-f3a2-6f95e99c0359"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Verse                                        Instruction  \\\n",
              "0    1.0  शिवः शक्त्या युक्तो यदि भवति शक्तः प्रभवितुंन ...   \n",
              "1    1.0  Śivaḥ śaktyā yukto yadi bhavati śaktaḥ prabhav...   \n",
              "2    2.0  तनीयांसं पांसुं तव चरण पङ्केरुहभवंविरिञ्चिः सं...   \n",
              "3    2.0  tanīyāṁsaṁ pāṁsuṁ tava caraṇa paṅkeruhabhavaṃv...   \n",
              "4    3.0  अविद्यानामन्तस्तिमिरमिहिरद्वीपनगरी1जडानां चैतन...   \n",
              "\n",
              "                                              Output  \n",
              "0   Śivaḥ - tāntric Śiva i.e. God Himself; yadi -...  \n",
              "1   Śivaḥ - tāntric Śiva i.e. God Himself; yadi -...  \n",
              "2  This verse further explains the Supremacy of Ś...  \n",
              "3  This verse further explains the Supremacy of Ś...  \n",
              "4  Variations in texts:1. nakarī नकरी2. bhavati भ...  "
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              "      <th>Verse</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>Śivaḥ śaktyā yukto yadi bhavati śaktaḥ prabhav...</td>\n",
              "      <td>Śivaḥ - tāntric Śiva i.e. God Himself; yadi -...</td>\n",
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              "      <td>तनीयांसं पांसुं तव चरण पङ्केरुहभवंविरिञ्चिः सं...</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
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              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d058f952-c26b-4df7-99fd-ded1633ee2bc button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 178,\n  \"fields\": [\n    {\n      \"column\": \"Verse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.890615818051018,\n        \"min\": 1.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          45.0,\n          54.0,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 177,\n        \"samples\": [\n          \"sudh\\u0101dh\\u0101r\\u0101s\\u0101rai\\u015bcara\\u1e47ayugal\\u0101ntarvigalitai\\u1e25prapa\\u00f1ca\\u1e41 si\\u00f1cant\\u012b punarapi ras\\u0101mn\\u0101yamahasa\\u1e25|av\\u0101pya sv\\u0101\\u1e41 bh\\u016bmi\\u1e41 bhujaganibhamadhyu\\u1e63\\u1e6davalaya\\u1e41svam\\u0101tm\\u0101na\\u1e41 k\\u1e5btv\\u0101 svapi\\u1e63i kulaku\\u1e47\\u1e0de kuhari\\u1e47i||\",\n          \"tvay\\u0101 h\\u1e5btv\\u0101 v\\u0101ma\\u1e41 vapuraparit\\u1e5bptena manas\\u0101\\u015bar\\u012br\\u0101rdha\\u1e41 \\u015ba\\u1e41bhoraparamapi \\u015ba\\u1e45ke h\\u1e5btamabh\\u016bt |yadetattvadr\\u016bpa\\u1e41 sakalamaru\\u1e47\\u0101bha\\u1e41  trinayana\\u1e41kuc\\u0101bhy\\u0101m\\u0101namra\\u1e41 ku\\u1e6dila\\u015ba\\u015bic\\u016b\\u1e0d\\u0101lamaku\\u1e6dam ||\",\n          \"m\\u1e5b\\u1e47\\u0101l\\u012bm\\u1e5bdv\\u012bn\\u0101\\u1e41 tava bhujalat\\u0101n\\u0101\\u1e41 catas\\u1e5b\\u1e47\\u0101\\u1e41caturbhi\\u1e25 saundarya\\u1e41 sarasijabhava\\u1e25 stauti vadanai\\u1e25 |nakhebhya\\u1e25 sa\\u1e41trasyan prathamamathan\\u0101dandhakaripo\\u1e25catur\\u1e47\\u0101\\u1e41 \\u015b\\u012br\\u1e63\\u0101\\u1e47\\u0101\\u1e41 samamabhayahast\\u0101rpa\\u1e47adhiy\\u0101 ||\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 89,\n        \"samples\": [\n          \"ar\\u0101lai\\u1e25 sv\\u0101bh\\u0101vy\\u0101t \\u2013 naturally fragrant; ali kalabha sa\\u015br\\u012bbhi\\u1e25 - beautiful like young bees (kalabha specifically refers to an young elephant or young camel and here it is used to mean young bees); alakai\\u1e25 - curls; par\\u012bta\\u1e41 te vaktra\\u1e41 - surrounding Your face; parihasati \\u2013 laughs at (mockery); pa\\u1e45keruha rucim \\u2013 beautiful like lotus flowers; dara smere \\u2013 soft smile; yasmin \\u2013 on Your face; da\\u015bana ruci ki\\u00f1jalka rucire \\u2013 being lustrous because of Your teeth; sugandhau m\\u0101dyanti \\u2013 fragrance that gives joy; smara dahan acak\\u1e63ur madhuliha\\u1e25 - bees resembling the eyes of \\u015aiva, who burnt Manmatha.\\u201cYour face is surrounded by Your curly hair, appearing beautiful like a swarm of young bees, making mockery of the beauty of lotus flowers. Soft smile on Your face, which appears lustrous because of Your gleaming teeth, which are so fragrant, due to which these bees swarm around Your face, like the eyes of \\u015aiva.\\u201dPar\\u0101\\u015bakti\\u2019s face, which is beyond any description, is compared to that of a lotus flower. Even then, this description only mocks at the lotus flowers, as Her face cannot be compared to any visual objects. She has curly hair that fall on Her forehead, which appear like a swarm of young bees, adding beauty to Her face. The verse, by referring to young bees, conveys the softness of Her hair. Bees in general are black in colour and by comparing Her hair to bees, \\u015aa\\u1e41kar\\u0101c\\u0101rya indirectly refers to the glowing dark colour of Her hair. When She smiles, Her white teeth shines, enhancing the beauty of Her face. The young bees, which hover around Her beautiful face are compared to the eyes of \\u015aiva. The verse says that even \\u015aiva, who burnt Manmatha, is attracted by Her beautiful face.Lotus has delicious nectar inside the pollen and bees naturally swarm lotus flowers to get the best quality of honey. When they swarm around a lotus, the scenic beauty appears like Her eyes. Bees are compared to eyelashes and the white stem within the lotus petals are compared to Her teeth. All the lotus flowers do not have white stigmas, but only rare species of lotus family. By saying so, \\u015aa\\u1e41kar\\u0101c\\u0101rya has conveyed that the of beauty of Her face is unique.\\u015ar\\u012b \\u015aakti mahimna\\u1e25 (verse 39) also compares Her face to that of a fully blossomed lotus and Her teeth to that of jasmine flowers. Lalit\\u0101 Sahasran\\u0101ma 25 \\u015auddha-vidhy\\u0101\\u1e45kur\\u0101k\\u0101ra-dhvijapak\\u1e45ti-dvayojval\\u0101 describes Her teeth thus:Her teeth appear like \\u015auddha-vidy\\u0101, which means \\u015ar\\u012b Vidy\\u0101.  \\u015ar\\u012b Vidy\\u0101 is considered as the most secret and powerful ritual worship of Lalit\\u0101mbik\\u0101.  This involves a lot of rituals and each ritual has its own meaning and interpretation.  \\u015auddha means pure, vidy\\u0101 means knowledge and \\u015auddha-vidy\\u0101 means pure knowledge.  This is considered pure because this up\\u0101sana m\\u0101rg or the cult of \\u015ar\\u012b Vidy\\u0101 worship emphasizes the non-duality, \\u2018I am That\\u2019 concept.The \\u1e62oda\\u015b\\u012b mantra is considered as the seed for \\u015ar\\u012b Vidy\\u0101.  It has sixteen b\\u012bja-s.  When a seed grows into a sprout, it has two leaves.  Therefore 16 x 2 gives 32, the number of teeth in human beings.   Even though teeth have two rows placed in upper and lower jaws, the jaws are attached to each other internally.  In the same way soul (j\\u012bva) and (Brahman) God are considered as different out of ignorance when both remain the same.  \\u015ar\\u012b Vidy\\u0101 worship is to be done in seclusion, understanding the significance and meanings of the procedures.  Then only the worship yields results. In the mantra initiation procedures of Devi, there are thirty two types of d\\u012bk\\u015ba (types of initiation). Yet another interpretation is also possible.  This Sahasran\\u0101ma starts only with 32 letters out of the 51 alphabets in Sanskrit.  This 32 represents Her teeth.  This could also mean that the initiation into \\u015ar\\u012b Vidy\\u0101 cult is to be done verbally by Guru to his disciple.{Further reading on \\u015auddha-vidy\\u0101: This is the fifteenth tattva, counting from \\u015aiva.  In this tattva, the consciousness of both \\u201cI\\u201d and \\u201cThis\\u201d is equally predominant. Though the germinal universe is seen differently, yet identity runs through it as a thread.  There is identity in diversity at this stage.  Kriya is predominant tattva here.  The consciousness of this state is \\u2018I am, I and also this\\u2019.  Vidy\\u0101 tattva consists of \\u015auddha-vidy\\u0101, sahaja vidy\\u0101 and ka\\u00f1cuka (limited knowledge). Vidy\\u0101tattva consists of \\u015buddhavidy\\u0101, sahajavidy\\u0101 and vidy\\u0101ka\\u00f1cuka. \\u015auddhavidy\\u0101 here is the same as sadvidy\\u0101 (fifth tattva), while sahajavidy\\u0101 is natural knowledge (not a tattva). Natural knowledge implies the emergence of His Freedom. As sahajavidy\\u0101 (natural knowledge) is also known as \\u015buddhavidy\\u0101 (pure knowledge). }These verses attempt to describe Her beauty, so that, aspirants can comfortably visualize Her form.\",\n          \"pavitr\\u012b kartu\\u1e41 na\\u1e25 - to purify us; pa\\u015bupati par\\u0101dh\\u012bna h\\u1e5bdaye \\u2013 wholeheartedly surrendered to \\u015aiva; day\\u0101 mitrai netrai\\u1e25 - compassionate eyes; aru\\u1e47a dhavala \\u015by\\u0101ma rucibhi\\u1e25 - splendorous with red, white and black; nada\\u1e25 - rivers; \\u015bo\\u1e47o ga\\u1e45g\\u0101 \\u2013 \\u015ao\\u1e47o, Ganges; tapana tanaya\\u2013 daughter of sun, known as river Yamuna; iti \\u2013 thus; dhruvam \\u2013 surely; amu\\u1e41 - this; tray\\u0101\\u1e47\\u0101\\u1e41  t\\u012brth\\u0101n\\u0101m \\u2013 these three rivers; upanayasi sa\\u1e41bhedam anagham \\u2013 destroys our sins.\\u201cThe one who has wholeheartedly surrendered unto \\u015aiva! The One who has compassionate eyes shining with red, white and black colours! You have given us the holy confluence of three rivers, Sone, Ganges and Yamuna, to wash off our sins.\\u201dIt is believed that by taking bath in holy rivers wards off one\\u2019s sins. Here \\u015aa\\u1e41kar\\u0101c\\u0101rya talks about the confluence of three sacred rivers Sone, Ganges and Yamuna and if one takes bath in this sacred confluence, all his sins, it is believed will be washed off. It is said that Par\\u0101\\u015bakti, who has wholeheartedly surrendered to \\u015aiva, has created this confluence out of compassion for the humanity.  Waters of Sona River is said to be red in colour; waters of Ganges is said to be white in colour and waters of Yamuna is believed to be black in colour. However, \\u015by\\u0101ma also means dark blue and possibly \\u015aa\\u1e41kar\\u0101c\\u0101rya should have used the word to mean dark blue waters.The tinges of these colours were already described in the previous verse and the previous verse said that through these colours, She creates three gu\\u1e47a-s, which in turn cause creation. In addition to this description, \\u015aa\\u1e41kar\\u0101c\\u0101rya speaks about Her wholehearted surrender to \\u015aiva. This appears to be out of context. But \\u015aa\\u1e41kar\\u0101c\\u0101rya is not a person who says something out of context. So, there should be some subtle conveyance through this verse. He subtly conveys the benefit arising out of ku\\u1e47\\u1e0dalin\\u012b meditation. The three n\\u0101\\u1e0di-s i\\u1e0d\\u0101, pi\\u1e45gala and su\\u1e63umna are referred through these three rivers. These three n\\u0101\\u1e0di-s join together at \\u0101j\\u00f1\\u0101cakra, also known as third eye. In Her subtlest form ku\\u1e47\\u1e0dalin\\u012b, She ascends through su\\u1e63umna n\\u0101\\u1e0di to conjoin Her Consort \\u015aiva at sahasr\\u0101ra. There are two aspects in this interpretation. An aspirant who could activate \\u0101j\\u00f1\\u0101cakra is able to ward off his sins. After leaving \\u0101j\\u00f1\\u0101cakra, She proceeds to sahasr\\u0101ra to surrender unto \\u015aiva. Whole hearted surrender can be interpreted to mean that there will no two separate entities such as \\u015aiva and \\u015aakti at sahasr\\u0101ra. They both merge together to become one and there exists only \\u015aiva. This phenomenon is known \\u015aiva-\\u015aakti ikya and is discussed in Lalit\\u0101 Sahasran\\u0101ma 999. A yogi who is able to experience Her presence in \\u0101j\\u00f1\\u0101cakra and the presence of both \\u015aiva and \\u015aakti at sahasr\\u0101ra will have all his sins annihilated. However, this situation is possible only for those, whose karmic accounts permit this to happen. It can also be said that unless karmic account permits, even \\u0101j\\u00f1\\u0101cakra will not be activated.\",\n          \"catu\\u1e63\\u1e63\\u1e6dy\\u0101 tantrai\\u1e25 - through sixty four tantra-s; sakalam atisandh\\u0101ya bhuvana\\u1e41 - betrayed all the worlds; sthita\\u1e25 tat tat siddhi prasava para tantrai\\u1e25 - each capable of conferring various psychic powers; pa\\u015bupati\\u1e25 - \\u015aiva; puna\\u1e25 tvan nirbandh\\u0101t \\u2013 subsequently, at Your insistence; akhila puru\\u1e63\\u0101rtha eka gha\\u1e6dan\\u0101 svatantra\\u1e41 - capable of conferring all the four human pursuits; te tantra\\u1e41 - Your tantra; k\\u1e63iti talam \\u2013 the habitat world (the earth); av\\u0101t\\u012btarat \\u2013 revealed; idam \\u2013 this;\\u201c\\u015aiva, by merely revealing sixty four tantra-s that are capable of conferring various psychic powers betrayed the world. But He, due to Your insistence, has subsequently revealed Your tantra, which is capable of conferring all the four human pursuits to the world of humanity.\\u201d\\u015aiva is a Yogic Personality and always remains in meditative mood. He talks only to His Consort, Par\\u0101\\u015bakti and reveals to Her various methods to attain Him as, in order to attain final liberation, one has to merge with Him. It is only Par\\u0101\\u015bakti, who reveals \\u015aiva and He cannot be approached directly. The following scene unfolds in Mount Kaila\\u015ba (it is only my thought and does not form part of the verse). \\u015aiva is in a relaxed mood and as usual, Par\\u0101\\u015bakti is sitting close to Him. During this very rare moment, Par\\u0101\\u015bakti asks a small favour from Him. \\u201cO! My Lord! You have revealed sixty four tantra-s to the world that are capable of conferring different superhuman powers. What my devotees are going to do with these siddhi-s? My devotees will never pursue these superhuman powers (it shows Her confidence in Her devotees). They want to attain You and I want to offer them liberation by revealing You to them. Can You kindly do me a favour by revealing a tatnra to my devotees that makes them realize and in turn I will bring them (perfected devotees) up to You for conferring liberation? I will bring before You only the best of my devotees who make themselves fit by practicing the tantra that You may choose to reveal now. I am asking this favour from You for the sake of my devotees. Will You please be kind enough to oblige me, as usual?\\u201d \\u015aiva is always pleased with His Consort and agrees to reveal a tantra to attain Her. This is the crux of this verse. It is also equally important to note that liberation can be attained only by worshipping Her. Par\\u0101\\u015bakti said to \\u015aiva in Mah\\u0101nirv\\u0101\\u1e47a Tantra (IV.45), \\u201cMy Lord! I would like to know the most excellent worship of me leading to unification with You, which You have already described.\\u201d \\u015aiva says (IV.9), \\u201cOnly by worshipping You, individuals can become one with Me.\\u201d There is no other evidence required to prove that \\u015aiva can be attained only through the Divine Mother, Par\\u0101\\u015bakti. Lalit\\u0101 Sahasran\\u0101ma 727 is \\u015biva-j\\u00f1\\u0101na-prad\\u0101yin\\u012b, which means that She alone can impart knowledge of \\u015aiva.\\u015aiva reveals a \\u1e62o\\u1e0da\\u015b\\u012b mantra in Mah\\u0101nirv\\u0101\\u1e47a Tantra (V.17), which is not known to many. While describing about this mantra \\u015aiva said, \\u201c\\u1e63o\\u1e0da\\u015b\\u012bya\\u1e41 sam\\u0101khy\\u0101t\\u0101 sarvatanre\\u1e63u gopit\\u0101 \\u0937\\u094b\\u0921\\u0936\\u0940\\u092f\\u0902 \\u0938\\u092e\\u093e\\u0916\\u094d\\u092f\\u093e\\u0924\\u093e \\u0938\\u0930\\u094d\\u0935\\u0924\\u0928\\u094d\\u0930\\u0947\\u0937\\u0941 \\u0917\\u094b\\u092a\\u093f\\u0924\\u093e\\u201d which means \\u201cthe mantra consisting of sixteen letters will be formed which lies hidden in all the tantra-s and which has been described be me.\\u201d This is the mantra revealed by \\u015aiva: hr\\u012b\\u1e41 \\u015br\\u012b\\u1e41 kr\\u012b\\u1e41  parame\\u015bvari k\\u0101like hr\\u012b\\u1e41 \\u015br\\u012b\\u1e41 kr\\u012b\\u1e41 sv\\u0101h\\u0101 | \\u0939\\u094d\\u0930\\u0940\\u0902 \\u0936\\u094d\\u0930\\u0940\\u0902 \\u0915\\u094d\\u0930\\u0940\\u0902  \\u092a\\u0930\\u092e\\u0947\\u0936\\u094d\\u0935\\u0930\\u093f \\u0915\\u093e\\u0932\\u093f\\u0915\\u0947 \\u0939\\u094d\\u0930\\u0940\\u0902 \\u0936\\u094d\\u0930\\u0940\\u0902 \\u0915\\u094d\\u0930\\u0940\\u0902 \\u0938\\u094d\\u0935\\u093e\\u0939\\u093e\\u0964There are many such mantras, though available even today, are nor known and pursued properly. It is also said that by reciting only the first three letters - hr\\u012b\\u1e41 \\u015br\\u012b\\u1e41 kr\\u012b\\u1e41 one gets material wealth (Mah\\u0101nirv\\u0101\\u1e47a Tantra V.14). The above mantra is capable of giving only the three of the four puru\\u1e63\\u0101rtha-s, where mok\\u1e63a is omitted. \\u015aiva is a very clever personality, as after all He is the Supreme Creator. Par\\u0101\\u015bakti knows that the above mantra or any other mantras revealed by \\u015aiva so far, are not capable of conferring liberation. The one b\\u012bja that is capable of conferring mok\\u1e63a is par\\u0101b\\u012bj\\u0101 (sau\\u1e25 \\u0938\\u094c\\u0903). If we look at any of \\u015ar\\u012b Vidy\\u0101 mantras such as B\\u0101l\\u0101, Saubh\\u0101gya Pa\\u00f1cada\\u015b\\u012b and \\u1e62o\\u1e0da\\u015b\\u012b, they will have par\\u0101b\\u012bj\\u0101. (sau\\u1e25 is made up of three b\\u012bja-s and they are \\u201cs\\u201d, which stands for sat Universal Spirit, \\u201cau\\u201d which stands for three powers of the Supreme Spirit viz. icc\\u0101 \\u015bakti, j\\u00f1\\u0101na \\u015bakti, and kriy\\u0101 \\u015bakti and \\u201c\\u1e25\\u201d stands for the Pure Consciousness of \\u015aiva).\\u015aiva has already revealed sixty four tantra-s (catu\\u1e63\\u1e63\\u1e6dy\\u0101 tantrai\\u1e25) to the world, by way His teachings to Par\\u0101\\u015bakti. Though the names of all these tantra-s are available, the names of tantra-s vary. It is also believed that total number of original tantra-s were more than sixty four, but only sixty four are available today and the rest were lost with time. These tantra-s, if practiced as revealed in the original Scriptures, are bound to give results much faster. Unfortunately, today there is no proper guidance to teach as per original tantric Scriptures and hence no one derives siddhi-s in the present world.Worshipping Devi is known as Tantra and the Supreme form of Her worship is \\u015ar\\u012b Vidy\\u0101, which means auspicious knowledge. It is called auspicious because, all the mantras of \\u015ar\\u012b Vidy\\u0101 confer liberation, provided the procedures are meticulously followed. The basis of \\u015ar\\u012b Vidy\\u0101 is Pa\\u00f1cada\\u015b\\u012b Mantra, which is explained in THIS ARTICLE. But Par\\u0101\\u015bakti pleads with \\u015aiva to reveal a tantra that is capable of conferring all the four puru\\u1e63\\u0101rtha-s \\u2013 dharma (discharging duty as per precepts of dharma); artha (acquiring material wealth in a proper way); k\\u0101ma (gratifying desires in the manner prescribed in the above two); and mok\\u1e63a (final liberation, emancipation). Hence \\u015ar\\u012b Vidy\\u0101 comes under tantra-s. There are other interpretations about what \\u015aiva has actually revealed to Par\\u0101\\u015bakti. There are arguments saying that what \\u015aiva had revealed is not \\u015ar\\u012b Vidy\\u0101 tantra but V\\u0101make\\u015bvara tantra, which also deals with Her worship. The difference between \\u015ar\\u012b Vidy\\u0101 and V\\u0101make\\u015bvara is where the former takes the worshipper directly to Par\\u0101\\u015bakti and in the latter, one has to go through m\\u0101t\\u1e5bk\\u0101 dev\\u012bs and tithi nity\\u0101 dev\\u012bs. Therefore, it is always considered that \\u015ar\\u012b Vidy\\u0101 is the best form of worship to attain Her. Worshipping Goddesses is known as Tantra.\\u015aiva Himself worshipped Her through His own Pa\\u00f1cada\\u015b\\u012b Mantra:\\u0939 \\u0938 \\u0915 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902\\u0964 \\u0939 \\u0938 \\u0915 \\u0939 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902\\u0964 \\u0938 \\u0915 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902\\u0964 \\u0939 \\u0938 \\u0915 \\u0932 \\u0939 \\u0938 \\u0915 \\u0939 \\u0932 \\u0938 \\u0915 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902 \\u0964ha sa ka la hr\\u012b\\u1e41 | ha sa ka ha la hr\\u012b\\u1e41 | sa ka la hr\\u012b\\u1e41 | ha sa ka la ha sa ka ha la sa ka la hr\\u012b\\u1e41  There is another variation:\\u0915 \\u090f \\u0908 \\u0932 \\u0939\\u094d\\u0930\\u093f\\u0902\\u0964 \\u0939  \\u0938 \\u0915 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902\\u0964 \\u0938 \\u0939 \\u0938 \\u0915 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902\\u0964 \\u0915 \\u090f \\u0908 \\u0932 \\u0939 \\u0938 \\u0915 \\u0939 \\u0932 \\u0938 \\u0915 \\u0938 \\u0915 \\u0932 \\u0939\\u094d\\u0930\\u0940\\u0902 \\u0964ka e \\u012b la hri\\u1e41 | ha  sa ka la hr\\u012b\\u1e41 | sa ha sa ka la hr\\u012b\\u1e41 | ka e \\u012b la ha sa ka ha la sa ka sa ka la hr\\u012b\\u1e41  |Probably, the purpose of \\u015aiva\\u2019s \\u201cworship\\u201d could have been different, for example, energising Her, transferring His Absolute and Independent Power of Authority (Sv\\u0101tantrya \\u015aakti) to Her, etc. Question is asked by Par\\u0101\\u015bakti in this verse and the answer is by given in the next verse (32).  When \\u015aiva reveals to Her, \\u015ar\\u012b Vidy\\u0101 Tantra, it overrides all the other sixty four tantra-s as \\u015ar\\u012b Vidy\\u0101 gives emancipation, which other tantra-s do not give that easily. Hence it is often said that \\u015ar\\u012b Vidy\\u0101 is \\u201ckevalam mok\\u1e63a s\\u0101danam\\u201d (it is the only way to liberation). However, this verse clearly says that the tantra that is going to be revealed in the next verse confers all the four puru\\u1e63\\u0101rtha-s (caturvidha puru\\u1e63\\u0101rtha siddhyartham). \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finetune Helsinki-NLP/opus-mt-mul-en model as it supports multilanguage."
      ],
      "metadata": {
        "id": "ywC8W-QzBdHV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the Tokenizer"
      ],
      "metadata": {
        "id": "Dm8uk86gCOik"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          ]
        },
        "id": "XemMAAPG5v7p",
        "outputId": "a2b67bd0-b755-404d-8ffa-b2ae308f56f3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df9d53f812b24fd9b4b9ea00749c71dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/44.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6cf43c4197cc4f87a361a06fe4c9b813",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "baf8d88674cf46a7b956e0ebef3b1901",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "source.spm:   0%|          | 0.00/707k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d398d785aea549759c6ccb6308d09036",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "target.spm:   0%|          | 0.00/791k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "420d29abb1c04d5cb41361560a5a6056",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.42M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id=\"Helsinki-NLP/opus-mt-mul-en\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preprocess the dataset"
      ],
      "metadata": {
        "id": "AGLV4H0-CbnH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "9b00f3b197c1414380b05eafa1027535"
          ]
        },
        "id": "WuxVeG3K50uP",
        "outputId": "f78fb16a-d663-47af-cb2f-b3a8c2c316e1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "61ce76a4f20d4b45a78167d20c0791f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/178 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import concatenate_datasets\n",
        "import numpy as np\n",
        "\n",
        "tokenized_inputs = dataset.map(lambda x: tokenizer(x[\"Instruction\"], truncation=True), batched=True)\n",
        "try:\n",
        "  input_lenghts =[]\n",
        "  for i in tokenized_inputs['train']:\n",
        "    input_lenghts.append(len(i['input_ids']))\n",
        "  max_source_length = int(np.percentile(input_lenghts, 85))\n",
        "except:\n",
        "  pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "c7a140f37090435e9feddec13f522065",
            "187193d322fa4efcb445b7b05870a116",
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            "1fc3017565f14dee8e3b765d0ac9073e"
          ]
        },
        "id": "LRmBg8Uj54cu",
        "outputId": "59a135ea-e35c-45a7-90e3-a6f849a6434a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51e6621ffd234141a9c0bb278f0e36ba",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/178 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenized_targets = dataset.map(lambda x: tokenizer(x[\"Output\"], truncation=True), batched=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yxXhf2y28hyV"
      },
      "outputs": [],
      "source": [
        "target_lengths=[]\n",
        "for i in tokenized_targets['train']:\n",
        "  target_lengths.append(len(i['input_ids']))\n",
        "max_target_length = int(np.percentile(target_lengths, 90))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tokenize the dataset"
      ],
      "metadata": {
        "id": "HNaZTLajDDON"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "f453dfb48e0f408e9d2916938ccee0dc",
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            "83cf0f3f7df8454982256f2cce864351",
            "7f95b9e5891f481eb88052f208b82bea",
            "fc29f9f99e3b468388f75a9c9f034bc1",
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            "a0af4faf02e24628976eb666400c557e",
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          ]
        },
        "id": "8aJPMOQn8sDS",
        "outputId": "be86335b-7a41-4471-fba0-a3754ce920ee"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f453dfb48e0f408e9d2916938ccee0dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/178 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of tokenized dataset: ['Verse', 'Instruction', 'Output', 'input_ids', 'attention_mask', 'labels']\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "291ffeb84c6d4b7c86e17a1c9331c852",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/178 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    inputs = [\"Explain: \" + item for item in sample[\"Instruction\"]]\n",
        "\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    labels = tokenizer(text_target=sample[\"Output\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")\n",
        "\n",
        "tokenized_dataset[\"train\"].save_to_disk(\"data/train\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-1awPZjdkVGR"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "from huggingface_hub import notebook_login\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the base model"
      ],
      "metadata": {
        "id": "d1T1dcAaDL_9"
      }
    },
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      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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          "height": 81,
          "referenced_widgets": [
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        },
        "id": "e9uTqMh9kVtG",
        "outputId": "29046d9f-f3fa-4af0-d167-46c6ebef8387"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c36e6ac2f6dd48c4937aa1b904d9f53b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/310M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"Helsinki-NLP/opus-mt-mul-en\", torch_dtype=\"auto\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Lora parameters"
      ],
      "metadata": {
        "id": "Ea2Z1gaVDV7J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWXPUKREkv8K",
        "outputId": "f3068b32-9978-4cbf-e6c1-702bd2842458"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 589,824 || all params: 78,108,672 || trainable%: 0.7551\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model, TaskType\n",
        "\n",
        "lora_config = LoraConfig(\n",
        " r=16,\n",
        " lora_alpha=32,\n",
        " target_modules=[\"q_proj\", \"v_proj\"],\n",
        "        # \"model.encoder.layers.*.self_attn.q_proj\",  # Encoder self-attention query projection\n",
        "        # \"model.encoder.layers.*.self_attn.v_proj\",  # Encoder self-attention value projection\n",
        "        # \"model.decoder.layers.*.self_attn.q_proj\",  # Decoder self-attention query projection\n",
        "        # \"model.decoder.layers.*.self_attn.v_proj\",  # Decoder self-attention value projection\n",
        "        # \"model.decoder.layers.*.encoder_attn.q_proj\",  # Decoder encoder-attention query projection\n",
        "        # \"model.decoder.layers.*.encoder_attn.v_proj\"   # Decoder encoder-attention value projection\n",
        "    # ],\n",
        " lora_dropout=0.05,\n",
        " bias=\"none\",\n",
        " task_type=TaskType.SEQ_2_SEQ_LM\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, lora_config)\n",
        "model.print_trainable_parameters()\n",
        "\n",
        "# trainable params: 18874368 || all params: 11154206720 || trainable%: 0.16921300163961817"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the Data Collator for finetuning"
      ],
      "metadata": {
        "id": "sr5JZBhBDfwH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uz9FmEjqk184"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "label_pad_token_id = -100\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer,\n",
        "    model=model,\n",
        "    label_pad_token_id=label_pad_token_id,\n",
        "    pad_to_multiple_of=8\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the Training Args"
      ],
      "metadata": {
        "id": "jEs0g4vYDnT_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xO4_pMZzlByH"
      },
      "outputs": [],
      "source": [
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "\n",
        "output_dir=\"output\"\n",
        "\n",
        "# Define training args\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "\tauto_find_batch_size=True,\n",
        "    learning_rate=1e-3, # higher learning rate\n",
        "    num_train_epochs=5,\n",
        "    logging_dir=f\"{output_dir}/logs\",\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_steps=500,\n",
        "    save_strategy=\"no\",\n",
        "    report_to=\"tensorboard\",\n",
        ")\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        ")\n",
        "model.config.use_cache = False"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model"
      ],
      "metadata": {
        "id": "2zlzTGd1DrXY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "FZdWq8F0lD_y",
        "outputId": "81aab81d-c783-40ef-9e37-094ea9768aca"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='115' max='115' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [115/115 44:43, Epoch 5/5]\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",
              "  </tbody>\n",
              "</table><p>"
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            "text/plain": [
              "TrainOutput(global_step=115, training_loss=6.329153575067934, metrics={'train_runtime': 2714.4596, 'train_samples_per_second': 0.328, 'train_steps_per_second': 0.042, 'total_flos': 24840319795200.0, 'train_loss': 6.329153575067934, 'epoch': 5.0})"
            ]
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          "metadata": {},
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        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wPyi8D21lH4m",
        "outputId": "64ab170a-8931-4a03-9f8d-917466d2375b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('results/tokenizer_config.json',\n",
              " 'results/special_tokens_map.json',\n",
              " 'results/vocab.json',\n",
              " 'results/source.spm',\n",
              " 'results/target.spm',\n",
              " 'results/added_tokens.json')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "peft_model_id=\"results\"\n",
        "trainer.model.save_pretrained(peft_model_id)\n",
        "tokenizer.save_pretrained(peft_model_id)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
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        "outputId": "c08c705e-51bb-40e5-d99a-d6f15891eed7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Peft model loaded\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "# Load peft config for pre-trained checkpoint etc.\n",
        "peft_model_id = \"results\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "# load base LLM model and tokenizer\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"Helsinki-NLP/opus-mt-mul-en\", torch_dtype=\"auto\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id, device_map={\"\":0})\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device) # This line is the crucial change\n",
        "\n",
        "model.eval()\n",
        "\n",
        "print(\"Peft model loaded\")"
      ]
    },
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      "source": [],
      "metadata": {
        "id": "fyC-Tj-STV15"
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