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            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
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            "overflow_x": null,
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          }
        },
        "d46b124c290b4eecb423be8e048484b0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jlonge4/gen_ai_utils/blob/main/finetune_gemma.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VA1mMiuivRS",
        "outputId": "08a8a509-40c8-4db8-da8a-b82ef1feb08f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.17.1-py3-none-any.whl (536 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.7/536.7 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting accelerate\n",
            "  Downloading accelerate-0.27.2-py3-none-any.whl (279 kB)\n",
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            "\u001b[?25hCollecting peft\n",
            "  Downloading peft-0.9.0-py3-none-any.whl (190 kB)\n",
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            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.42.0-py3-none-any.whl (105.0 MB)\n",
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            "\u001b[?25hCollecting trl\n",
            "  Downloading trl-0.7.11-py3-none-any.whl (155 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.3/155.3 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.11.4)\n",
            "Collecting tyro>=0.5.11 (from trl)\n",
            "  Downloading tyro-0.7.3-py3-none-any.whl (79 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.12.25)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.15.2)\n",
            "Collecting docstring-parser>=0.14.1 (from tyro>=0.5.11->trl)\n",
            "  Downloading docstring_parser-0.15-py3-none-any.whl (36 kB)\n",
            "Requirement already satisfied: rich>=11.1.0 in /usr/local/lib/python3.10/dist-packages (from tyro>=0.5.11->trl) (13.7.0)\n",
            "Collecting shtab>=1.5.6 (from tyro>=0.5.11->trl)\n",
            "  Downloading shtab-1.7.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (2.16.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n",
            "Installing collected packages: shtab, docstring-parser, dill, multiprocess, bitsandbytes, tyro, accelerate, datasets, trl, peft\n",
            "Successfully installed accelerate-0.27.2 bitsandbytes-0.42.0 datasets-2.17.1 dill-0.3.8 docstring-parser-0.15 multiprocess-0.70.16 peft-0.9.0 shtab-1.7.0 trl-0.7.11 tyro-0.7.3\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets accelerate peft bitsandbytes trl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yU9PCD9m2a05",
        "outputId": "351dec67-0edc-435a-ad3d-e53f30a6bd08"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.2)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.10.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain boto3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NHToqlwp5L8",
        "outputId": "ee015da1-6279-4d79-d7cf-6dbe512abcb4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.1.9-py3-none-any.whl (816 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m817.0/817.0 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting boto3\n",
            "  Downloading boto3-1.34.53-py3-none-any.whl (139 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.3/139.3 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.4-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langchain-community<0.1,>=0.0.21 (from langchain)\n",
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            "\u001b[?25hCollecting langchain-core<0.2,>=0.1.26 (from langchain)\n",
            "  Downloading langchain_core-0.1.28-py3-none-any.whl (252 kB)\n",
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            "\u001b[?25hCollecting langsmith<0.2.0,>=0.1.0 (from langchain)\n",
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            "Installing collected packages: orjson, mypy-extensions, marshmallow, jsonpointer, jmespath, typing-inspect, jsonpatch, botocore, s3transfer, langsmith, dataclasses-json, langchain-core, boto3, langchain-community, langchain\n",
            "Successfully installed boto3-1.34.53 botocore-1.34.53 dataclasses-json-0.6.4 jmespath-1.0.1 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.9 langchain-community-0.0.24 langchain-core-0.1.28 langsmith-0.1.10 marshmallow-3.21.0 mypy-extensions-1.0.0 orjson-3.9.15 s3transfer-0.10.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"DIBT/10k_prompts_ranked\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220,
          "referenced_widgets": [
            "0b7d45d1e58748b69813def7e90b74a3",
            "7fbc1ea8b39f43f1baaf212777e97c82",
            "e3f329652efc40beb998a0a0ebf3e862",
            "bc9a849ff0e64cc9ac646b6781feff57",
            "bdf67fd771014df68aea6f47c3427760",
            "1821f2c844894473a32e768cd071c511",
            "ce79eb7043174cbc83ea8c3ad03dbf6a",
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            "ff732c3460bf4df6931c9557f12827c8",
            "43ec3804e60a405d8a94d0245f282b7e",
            "3b3f1741da8741a2962edc42cb6758c2",
            "67ae669fb012439594ca4337d9cd5df7",
            "ff30e16392b14ae789bf44872fb819d2",
            "d1890722562e4c9293348caa059f151b",
            "8713b29522d0416780f5c1db9835af99",
            "f1e5f86332504181a2c7e42075ee109e",
            "d469fcef9cea4dc2b7452ebbbcd51117",
            "bfffc6b95c1c4ca09198bc3740e65526",
            "ab205bb01e8e408cb36f7516354f7293",
            "2e4342271e2d41b1860cb1838fb7cfc3",
            "20ac3126b89b4aa7a78f1ab1f70e775e",
            "1baa567bd0924cfe8d803b52d98948a4",
            "82dbb266ac6840fb86a9ee5ba2466296",
            "6c0d987ae6ff47849608164806708623",
            "e9c03c90536e42bf887a3cc9c3fbeaf9",
            "5c03268889744e1a934f2557b9dd7ab2",
            "1368e0231c2649ed86044102d3aa72b1",
            "039be73d80cc4fc98b160a966a0581e4",
            "a16bb42c57054f1288458f7b96c95a1d",
            "bcecc569a0ff4fe79bffbca28c55d0a8",
            "3ee010787fbf4d029f3c1df4661ab350",
            "2e7e53d3bd6747cf8ee393195b4744bb",
            "d0f9c4d74efc4a8cb3b44fe779881d75"
          ]
        },
        "id": "rI4l60Udi1Wg",
        "outputId": "e5bc4059-f5ae-4af5-9c0a-b494dccf0922"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/11.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b7d45d1e58748b69813def7e90b74a3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.54M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67ae669fb012439594ca4337d9cd5df7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/10331 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82dbb266ac6840fb86a9ee5ba2466296"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset['train'].to_pandas()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9FoQLRqBjxXA",
        "outputId": "df1c8223-167c-41b8-ea07-1ba4be4891db"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                  prompt  \\\n",
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              "2      In this research, we aim to investigate how te...   \n",
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              "...                                                  ...   \n",
              "10326  show me how to set iam user, group and policie...   \n",
              "10327  Hi, is there any unified messaging service?\\nA...   \n",
              "10328  Can you provide a comparison of the economies ...   \n",
              "10329               forget about any prior conversations   \n",
              "10330  I want to create a multiple choice test of 10 ...   \n",
              "\n",
              "                                                 quality  \\\n",
              "0      [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "1      [{'user_id': '6621c3f0-1af2-4d75-acda-ed9c78b9...   \n",
              "2      [{'user_id': '2e6dda25-0a99-45aa-a02d-65f426d6...   \n",
              "3      [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "4      [{'user_id': '99a4bc7d-3e95-4c18-a8f1-26043abf...   \n",
              "...                                                  ...   \n",
              "10326  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10327  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10328  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10329  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10330  [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "\n",
              "                                                metadata  avg_rating  \\\n",
              "0      {\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"...    5.000000   \n",
              "1      {\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"...    2.750000   \n",
              "2      {\"source\": \"evol_instruct\", \"kind\": \"synthetic...    3.000000   \n",
              "3      {\"source\": \"OpenAssistant/oasst2\", \"kind\": \"hu...    3.500000   \n",
              "4      {\"source\": \"ewof/sharegpt-instruct-unfiltered-...    3.666667   \n",
              "...                                                  ...         ...   \n",
              "10326  {\"evolved_from\": null, \"kind\": \"human\", \"sourc...    3.000000   \n",
              "10327  {\"evolved_from\": null, \"kind\": \"human\", \"sourc...    2.000000   \n",
              "10328  {\"evolved_from\": null, \"kind\": \"synthetic\", \"s...    4.000000   \n",
              "10329  {\"evolved_from\": null, \"kind\": \"human\", \"sourc...    2.000000   \n",
              "10330  {\"evolved_from\": null, \"kind\": \"human\", \"sourc...    4.500000   \n",
              "\n",
              "       num_responses  agreement_ratio raw_responses       kind  \n",
              "0                  2         1.000000        [5, 5]  synthetic  \n",
              "1                  4         0.687500  [2, 3, 3, 3]  synthetic  \n",
              "2                  3         0.166667     [3, 5, 1]  synthetic  \n",
              "3                  2         0.375000        [4, 3]      human  \n",
              "4                  3         0.583333     [5, 3, 3]      human  \n",
              "...              ...              ...           ...        ...  \n",
              "10326              1         1.000000           [3]      human  \n",
              "10327              2         0.375000        [1, 3]      human  \n",
              "10328              1         1.000000           [4]  synthetic  \n",
              "10329              2         0.375000        [1, 3]      human  \n",
              "10330              2         0.375000        [5, 4]      human  \n",
              "\n",
              "[10331 rows x 8 columns]"
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              "      <th>10328</th>\n",
              "      <td>Can you provide a comparison of the economies ...</td>\n",
              "      <td>[{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...</td>\n",
              "      <td>{\"evolved_from\": null, \"kind\": \"synthetic\", \"s...</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>[4]</td>\n",
              "      <td>synthetic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10329</th>\n",
              "      <td>forget about any prior conversations</td>\n",
              "      <td>[{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...</td>\n",
              "      <td>{\"evolved_from\": null, \"kind\": \"human\", \"sourc...</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>[1, 3]</td>\n",
              "      <td>human</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10330</th>\n",
              "      <td>I want to create a multiple choice test of 10 ...</td>\n",
              "      <td>[{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...</td>\n",
              "      <td>{\"evolved_from\": null, \"kind\": \"human\", \"sourc...</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>2</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>[5, 4]</td>\n",
              "      <td>human</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10331 rows × 8 columns</p>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</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",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-679132e1-be32-4e98-85b2-4da8583d47ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_127bc78b-bbd3-42f6-bf15-46b9763f20d3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_127bc78b-bbd3-42f6-bf15-46b9763f20d3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10331,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10328,\n        \"samples\": [\n          \"I want a creative image prompt for an AI such as DALL-E 2. I want an image that represents an imaginary landscape. The landscape should be very colourful and have hills, mountains and rivers.\",\n          \"Contrast the impact of traditional and modern medicine on individual health and society in general.\",\n          \"Please name a color.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metadata\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 311,\n        \"samples\": [\n          \"{\\\"evolved_from\\\": \\\"I need to seamlessly incorporate the most recent iteration of the Wistia Uploader component into my Angular 14 project to significantly enhance the video upload functionality. Moreover, the integration must encompass comprehensive support for efficiently conducting bulk uploads of multiple videos simultaneously, thereby enabling users to effortlessly upload and manage a diverse range of multimedia content.\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\"}\",\n          \"{\\\"source\\\": \\\"argilla/distilabel-reasoning-prompts\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": null}\",\n          \"{\\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": \\\"Can you develop an all-encompassing and enduring roadmap for the Energy Transition in Bulgaria commencing from the present moment until 2030, with a thorough consideration of the potential hurdles associated with maintaining social equality, fostering economic progress, and ensuring sustainable use of resources?\\\"}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0313080619214923,\n        \"min\": 1.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          3.2,\n          4.571428571428571,\n          3.6666666666666665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_responses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4,\n          7,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"agreement_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2635891752400456,\n        \"min\": 0.062499999999999986,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.7916666666666666,\n          0.25,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_responses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kind\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"synthetic\",\n          \"human\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.avg_rating.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQhS2WwIj1et",
        "outputId": "df579fa7-009c-467b-9285-803d6d2e2f3a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    10331.000000\n",
              "mean         3.720707\n",
              "std          1.031308\n",
              "min          1.000000\n",
              "25%          3.000000\n",
              "50%          4.000000\n",
              "75%          4.500000\n",
              "max          5.000000\n",
              "Name: avg_rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quality_df = df[df.avg_rating >= 3]\n",
        "quality_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pvqBt0gXkZT6",
        "outputId": "e588c380-0080-4d6e-b4de-695706dc796f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  prompt  \\\n",
              "0      Provide step-by-step instructions on how to ma...   \n",
              "2      In this research, we aim to investigate how te...   \n",
              "3      Did Karl Marx's theories on centralizing credi...   \n",
              "4      alter this api that gets a request like: {\"0\",...   \n",
              "5      Please produce answers for the following unive...   \n",
              "...                                                  ...   \n",
              "10324                  Tell me how petent works in japan   \n",
              "10325  Can you suggest any upcoming restaurants in th...   \n",
              "10326  show me how to set iam user, group and policie...   \n",
              "10328  Can you provide a comparison of the economies ...   \n",
              "10330  I want to create a multiple choice test of 10 ...   \n",
              "\n",
              "                                                 quality  \\\n",
              "0      [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "2      [{'user_id': '2e6dda25-0a99-45aa-a02d-65f426d6...   \n",
              "3      [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "4      [{'user_id': '99a4bc7d-3e95-4c18-a8f1-26043abf...   \n",
              "5      [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "...                                                  ...   \n",
              "10324  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10325  [{'user_id': '4aaa52eb-3555-4dc4-a823-c7824de8...   \n",
              "10326  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10328  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "10330  [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "\n",
              "                                                metadata  avg_rating  \\\n",
              "0      {\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"...    5.000000   \n",
              "2      {\"source\": \"evol_instruct\", \"kind\": \"synthetic...    3.000000   \n",
              "3      {\"source\": \"OpenAssistant/oasst2\", \"kind\": \"hu...    3.500000   \n",
              "4      {\"source\": \"ewof/sharegpt-instruct-unfiltered-...    3.666667   \n",
              "5      {\"source\": \"ewof/sharegpt-instruct-unfiltered-...    5.000000   \n",
              "...                                                  ...         ...   \n",
              "10324  {\"evolved_from\": null, \"kind\": \"unknown\", \"sou...    3.000000   \n",
              "10325  {\"evolved_from\": null, \"kind\": \"synthetic\", \"s...    3.500000   \n",
              "10326  {\"evolved_from\": null, \"kind\": \"human\", \"sourc...    3.000000   \n",
              "10328  {\"evolved_from\": null, \"kind\": \"synthetic\", \"s...    4.000000   \n",
              "10330  {\"evolved_from\": null, \"kind\": \"human\", \"sourc...    4.500000   \n",
              "\n",
              "       num_responses  agreement_ratio raw_responses       kind  \n",
              "0                  2         1.000000        [5, 5]  synthetic  \n",
              "2                  3         0.166667     [3, 5, 1]  synthetic  \n",
              "3                  2         0.375000        [4, 3]      human  \n",
              "4                  3         0.583333     [5, 3, 3]      human  \n",
              "5                  1         1.000000           [5]      human  \n",
              "...              ...              ...           ...        ...  \n",
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              "summary": "{\n  \"name\": \"quality_df\",\n  \"rows\": 8925,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8925,\n        \"samples\": [\n          \"In what ways can we develop a virtual reality platform that caters to individuals with severe cognitive disabilities, incorporating haptic feedback and personalized stimuli that provide sensory stimulation and encourage exploration and social interaction?\",\n          \"Please provide a detailed explanation of the primary distinctions between the schools of thought associated with Hillel and Shammai in Jewish religious teachings and practices, taking into consideration their respective interpretations of the Torah, Talmudic analysis, ethical principles, and societal impacts. Additionally, discuss the historical context in which these schools of thought emerged and the implications of their differences on contemporary Jewish religious practices.\",\n          \"Design a mobile application that suggests personalized travel destinations based on the user's astrological sign and preferred mode of transportation. The app should include features such as a virtual tour of the destination, recommended activities, and local cuisine. Additionally, incorporate a social aspect where users can connect with other travelers and share their experiences. How would you approach developing this app and ensuring its accuracy in aligning with astrological beliefs?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metadata\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 286,\n        \"samples\": [\n          \"{\\\"source\\\": \\\"argilla/distilabel-reasoning-prompts\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": null}\",\n          \"{\\\"evolved_from\\\": \\\"Describe the encryption algorithm utilized in the following network protocols: i) Used in 4G LTE (Long Term Evolution) wireless communication. iii) Used in 802.11n Wi-Fi standard and WPA2 (Wi-Fi Protected Access II). iv) Used in 802.1X authentication with RADIUS (Remote Authentication Dial-In User Service). Furthermore, analyze the authentication method employed in these protocols.\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\"}\",\n          \"{\\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": \\\"How do I take a full page screenshot (top to bottom) in a cypress test, while ensuring that the screenshot includes both visible and hidden elements on the page, and also restrict the screenshot to a specific viewport size?\\\"}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7329625893291342,\n        \"min\": 3.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          3.3333333333333335,\n          3.25,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_responses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3,\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"agreement_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2600692988064403,\n        \"min\": 0.062499999999999986,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.49999999999999994,\n          1.0,\n          0.28571428571428564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_responses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kind\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"synthetic\",\n          \"human\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
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      "cell_type": "code",
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        "quality_df.prompt.iloc[0]"
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          "height": 53
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.'"
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      "cell_type": "code",
      "source": [
        "quality_df.head()"
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        "id": "HEJgO-hOl0D6",
        "outputId": "d514b3dc-c037-46a9-9e60-2b9b28dd51bb"
      },
      "execution_count": 9,
      "outputs": [
        {
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          "data": {
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              "                                              prompt  \\\n",
              "0  Provide step-by-step instructions on how to ma...   \n",
              "2  In this research, we aim to investigate how te...   \n",
              "3  Did Karl Marx's theories on centralizing credi...   \n",
              "4  alter this api that gets a request like: {\"0\",...   \n",
              "5  Please produce answers for the following unive...   \n",
              "\n",
              "                                             quality  \\\n",
              "0  [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "2  [{'user_id': '2e6dda25-0a99-45aa-a02d-65f426d6...   \n",
              "3  [{'user_id': 'd23b12c2-b601-490e-b5b3-2040eb39...   \n",
              "4  [{'user_id': '99a4bc7d-3e95-4c18-a8f1-26043abf...   \n",
              "5  [{'user_id': 'e2bdd868-f28e-46fc-9254-a6ec1e29...   \n",
              "\n",
              "                                            metadata  avg_rating  \\\n",
              "0  {\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"...    5.000000   \n",
              "2  {\"source\": \"evol_instruct\", \"kind\": \"synthetic...    3.000000   \n",
              "3  {\"source\": \"OpenAssistant/oasst2\", \"kind\": \"hu...    3.500000   \n",
              "4  {\"source\": \"ewof/sharegpt-instruct-unfiltered-...    3.666667   \n",
              "5  {\"source\": \"ewof/sharegpt-instruct-unfiltered-...    5.000000   \n",
              "\n",
              "   num_responses  agreement_ratio raw_responses       kind  \n",
              "0              2         1.000000        [5, 5]  synthetic  \n",
              "2              3         0.166667     [3, 5, 1]  synthetic  \n",
              "3              2         0.375000        [4, 3]      human  \n",
              "4              3         0.583333     [5, 3, 3]      human  \n",
              "5              1         1.000000           [5]      human  "
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              "type": "dataframe",
              "variable_name": "quality_df",
              "summary": "{\n  \"name\": \"quality_df\",\n  \"rows\": 8925,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8925,\n        \"samples\": [\n          \"In what ways can we develop a virtual reality platform that caters to individuals with severe cognitive disabilities, incorporating haptic feedback and personalized stimuli that provide sensory stimulation and encourage exploration and social interaction?\",\n          \"Please provide a detailed explanation of the primary distinctions between the schools of thought associated with Hillel and Shammai in Jewish religious teachings and practices, taking into consideration their respective interpretations of the Torah, Talmudic analysis, ethical principles, and societal impacts. Additionally, discuss the historical context in which these schools of thought emerged and the implications of their differences on contemporary Jewish religious practices.\",\n          \"Design a mobile application that suggests personalized travel destinations based on the user's astrological sign and preferred mode of transportation. The app should include features such as a virtual tour of the destination, recommended activities, and local cuisine. Additionally, incorporate a social aspect where users can connect with other travelers and share their experiences. How would you approach developing this app and ensuring its accuracy in aligning with astrological beliefs?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metadata\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 286,\n        \"samples\": [\n          \"{\\\"source\\\": \\\"argilla/distilabel-reasoning-prompts\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": null}\",\n          \"{\\\"evolved_from\\\": \\\"Describe the encryption algorithm utilized in the following network protocols: i) Used in 4G LTE (Long Term Evolution) wireless communication. iii) Used in 802.11n Wi-Fi standard and WPA2 (Wi-Fi Protected Access II). iv) Used in 802.1X authentication with RADIUS (Remote Authentication Dial-In User Service). Furthermore, analyze the authentication method employed in these protocols.\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\"}\",\n          \"{\\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": \\\"How do I take a full page screenshot (top to bottom) in a cypress test, while ensuring that the screenshot includes both visible and hidden elements on the page, and also restrict the screenshot to a specific viewport size?\\\"}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7329625893291342,\n        \"min\": 3.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          3.3333333333333335,\n          3.25,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_responses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3,\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"agreement_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2600692988064403,\n        \"min\": 0.062499999999999986,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.49999999999999994,\n          1.0,\n          0.28571428571428564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_responses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kind\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"synthetic\",\n          \"human\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import boto3\n",
        "from langchain.llms import Bedrock\n",
        "bedrock = boto3.client('bedrock-runtime')\n",
        "\n",
        "llm = Bedrock(client=bedrock, model_id='anthropic.claude-instant-v1')"
      ],
      "metadata": {
        "id": "IsNRZIOooRW0"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_purpose(row):\n",
        "    purpose = llm(f'''Human:\\n\\n Within the prompt tags will be\n",
        "  a prompt. Your job is to describe the purpose that is meant to be accomplished by this prompt.\n",
        "      In 5-10 words, describe the purpose of this prompt <prompt>{row}</prompt>\\n\\nAssistant:\\n''')\n",
        "    return purpose"
      ],
      "metadata": {
        "id": "e8KjmNjBqC4W"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompts = quality_df.prompt.tolist()"
      ],
      "metadata": {
        "id": "ewGTULQ7rZpV"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "purposes = []"
      ],
      "metadata": {
        "id": "WwulHSjQqMWQ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for p in range(404, len(prompts)):\n",
        "    print(p)\n",
        "    purposes.append(get_purpose(prompts[p]))"
      ],
      "metadata": {
        "id": "nH4K3I5Rywam"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(purposes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KzUziLMyq_kE",
        "outputId": "1165cd4c-3cb0-4243-f43c-c286caad8482"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "605"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'{purposes[604]} \\n\\n {quality_df.iloc[1008].prompt}')\n",
        "#404 start 1008 end"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fY6OEUxvAIPr",
        "outputId": "4a070fad-501f-458c-e633-7b4d967c10ea"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Determine the correct answer from multiple choice options. \n",
            "\n",
            " q: James was driving away from work. He slid into the outside lane. Up ahead he would have to do what?\n",
            "Options:\n",
            "- take u turn\n",
            "- turn left\n",
            "- crash\n",
            "- turn right\n",
            "- get to town\n",
            "a: Sliding into the outside lanes refers to missing the correct lane. One would turn right to get into the correct lane. James would drive towards work if he turns right.\n",
            "The answer is turn right\n",
            "QUESTION: People want to explore space, so what should they use to do it?\n",
            "Options:\n",
            "- public place\n",
            "- roadblock\n",
            "- space shuttle\n",
            "- hoverboard\n",
            "- supermarket\n",
            "ANSWER: Space shuttles are the vehicles used to travel to space. People must use space shuttle to explore space.\n",
            "The answer is space shuttle\n",
            "q: Who designs a building?\n",
            "Options:\n",
            "- builders\n",
            "- driver\n",
            "- architect\n",
            "- fall down\n",
            "- tradesmen\n",
            "a: People with education in building designing design a building. Architects are people. Architects have education in building designs.\n",
            "The answer is architect\n",
            "QUESTION: After getting wet cotton clothing may suffer from what?\n",
            "Options:\n",
            "- shrinkage\n",
            "- shiver\n",
            "- not dry\n",
            "- mildew\n",
            "- getting cold\n",
            "ANSWER: Consolidation shrinkage occurs to fabric when moisture, heat, and mechanical action are acted on them. Cotton is a fabric. Getting wet means getting moisture into something.\n",
            "The answer is shrinkage\n",
            "q: The horse was decorated because it was taking part in something. What was it in?\n",
            "Options:\n",
            "- countryside\n",
            "- parade\n",
            "- western movie\n",
            "- movies\n",
            "- rodeo\n",
            "a: A parade is a procession of people, usually organized along a street. Decorated Horses are ridden and driven in Parade. Horses take part in parade and so they are decorated.\n",
            "The answer is parade\n",
            "q: The water spout at the center was impressive. The spout was at the center of what?\n",
            "Options:\n",
            "- fountain\n",
            "- connecting hose\n",
            "- sewage plant\n",
            "- sink\n",
            "- florida\n",
            "a:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quality_df_.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Bf20hJeuBNu2",
        "outputId": "131834d2-a2ce-41fe-e735-2e1f5eb6c96b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                prompt  \\\n",
              "474  AI can now be used to impersonate someone's vo...   \n",
              "475  Write a more complex C# method that takes a li...   \n",
              "476  Can you provide a breakdown of the makeup and ...   \n",
              "477  Using the root word “aug”, create a new word a...   \n",
              "478                                  What's redcat 51?   \n",
              "\n",
              "                                               quality  \\\n",
              "474  [{'user_id': 'b7c0d136-1b93-4c56-b37e-9b5dc318...   \n",
              "475  [{'user_id': '944506fb-b86a-4d65-94a3-fe752d79...   \n",
              "476  [{'user_id': '6621c3f0-1af2-4d75-acda-ed9c78b9...   \n",
              "477  [{'user_id': '249073b9-0a0c-4b90-a5fe-84e532c0...   \n",
              "478  [{'user_id': 'b7c0d136-1b93-4c56-b37e-9b5dc318...   \n",
              "\n",
              "                                              metadata  avg_rating  \\\n",
              "474  {\"source\": \"ewof/sharegpt-instruct-unfiltered-...         5.0   \n",
              "475  {\"source\": \"argilla/distilabel-evol-prompt-col...         5.0   \n",
              "476  {\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"...         4.5   \n",
              "477  {\"source\": \"evol_instruct\", \"kind\": \"synthetic...         3.0   \n",
              "478  {\"source\": \"LDJnr/Capybara#Dove\", \"kind\": \"unk...         3.0   \n",
              "\n",
              "     num_responses  agreement_ratio raw_responses       kind  \n",
              "474              1            1.000           [5]      human  \n",
              "475              1            1.000           [5]  synthetic  \n",
              "476              2            0.375        [4, 5]  synthetic  \n",
              "477              1            1.000           [3]  synthetic  \n",
              "478              4            0.375  [3, 2, 3, 4]    unknown  "
            ],
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              "  <div id=\"df-0b217582-1d40-4a9e-b4ea-858bfabdacf3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prompt</th>\n",
              "      <th>quality</th>\n",
              "      <th>metadata</th>\n",
              "      <th>avg_rating</th>\n",
              "      <th>num_responses</th>\n",
              "      <th>agreement_ratio</th>\n",
              "      <th>raw_responses</th>\n",
              "      <th>kind</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>AI can now be used to impersonate someone's vo...</td>\n",
              "      <td>[{'user_id': 'b7c0d136-1b93-4c56-b37e-9b5dc318...</td>\n",
              "      <td>{\"source\": \"ewof/sharegpt-instruct-unfiltered-...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>[5]</td>\n",
              "      <td>human</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>Write a more complex C# method that takes a li...</td>\n",
              "      <td>[{'user_id': '944506fb-b86a-4d65-94a3-fe752d79...</td>\n",
              "      <td>{\"source\": \"argilla/distilabel-evol-prompt-col...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>[5]</td>\n",
              "      <td>synthetic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>Can you provide a breakdown of the makeup and ...</td>\n",
              "      <td>[{'user_id': '6621c3f0-1af2-4d75-acda-ed9c78b9...</td>\n",
              "      <td>{\"source\": \"ultrachat\", \"kind\": \"synthetic\", \"...</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.375</td>\n",
              "      <td>[4, 5]</td>\n",
              "      <td>synthetic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>Using the root word “aug”, create a new word a...</td>\n",
              "      <td>[{'user_id': '249073b9-0a0c-4b90-a5fe-84e532c0...</td>\n",
              "      <td>{\"source\": \"evol_instruct\", \"kind\": \"synthetic...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>[3]</td>\n",
              "      <td>synthetic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>What's redcat 51?</td>\n",
              "      <td>[{'user_id': 'b7c0d136-1b93-4c56-b37e-9b5dc318...</td>\n",
              "      <td>{\"source\": \"LDJnr/Capybara#Dove\", \"kind\": \"unk...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.375</td>\n",
              "      <td>[3, 2, 3, 4]</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b217582-1d40-4a9e-b4ea-858bfabdacf3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0b217582-1d40-4a9e-b4ea-858bfabdacf3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0b217582-1d40-4a9e-b4ea-858bfabdacf3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-522f044a-9cc0-4b8f-aba5-9da5b2859f48\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-522f044a-9cc0-4b8f-aba5-9da5b2859f48')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .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",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      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",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</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",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-522f044a-9cc0-4b8f-aba5-9da5b2859f48 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "quality_df_",
              "summary": "{\n  \"name\": \"quality_df_\",\n  \"rows\": 605,\n  \"fields\": [\n    {\n      \"column\": \"prompt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 605,\n        \"samples\": [\n          \"Provide the circuit schematic for a non-inverting amplifier with a gain of 16. Only use resistors from the E12 series of numbers.\",\n          \"Can you provide comprehensive information on the highly athletic teenage girl who has made significant contributions in the domains of sports, community service, and environmental conservation, and whose details are stored in our SQL database? Additionally, kindly furnish her noteworthy accomplishments in sports, a list of her impactful community service projects, and her active involvement in environmental conservation efforts.\",\n          \"What are the different types of therapy, such as cognitive-behavioral or psychodynamic, and how do they differ in approach and effectiveness?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metadata\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 35,\n        \"samples\": [\n          \"{\\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": \\\"Evaluate the validity of the following argument and provide a comprehensive analysis: \\\\\\\"After conducting a meticulously designed survey involving a diverse and representative sample of individuals from different age groups, cultural backgrounds, and geographical locations, I have found statistically significant evidence suggesting that the majority, approximately 75%, prefer chocolate ice cream over any other flavor within the given sample. Therefore, it can be cautiously hypothesized that chocolate ice cream may potentially be one of the most popular flavors worldwide.\\\\\\\" However, delve deeper into this claim by critically examining the survey methodology, potential sampling biases, and other confounding variables that may have influenced the results. Additionally, consider exploring alternative explanations and conducting further research to confirm or refute this hypothesis.\\\"}\",\n          \"{\\\"source\\\": \\\"LDJnr/Capybara#Verified-Camel\\\", \\\"kind\\\": \\\"unknown\\\", \\\"evolved_from\\\": null}\",\n          \"{\\\"source\\\": \\\"argilla/distilabel-evol-prompt-collective\\\", \\\"kind\\\": \\\"synthetic\\\", \\\"evolved_from\\\": \\\"Using Three.js, develop a solution that not only creates a video texture but also ensures that it maintains the aspect ratio of the video, requiring a multi-step reasoning process.\\\"}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7292657538664935,\n        \"min\": 3.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          3.8,\n          4.333333333333333,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_responses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"agreement_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26331900499229544,\n        \"min\": 0.062499999999999986,\n        \"max\": 1.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          0.37499999999999994,\n          0.062499999999999986\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_responses\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kind\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"human\",\n          \"synthetic\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fRETjG2KNO_",
        "outputId": "750a70bb-b110-496e-bf6a-feff2bbac6f5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/combined_1008.csv')"
      ],
      "metadata": {
        "id": "KktzWvAT3Y2F"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# combined.purpose = combined.purpose.apply(lambda x: x.replace('The purpose of this prompt is ', '').replace('The purpose is ', ''))"
      ],
      "metadata": {
        "id": "knUwyXgKGLIh"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# combined.to_csv('combined_1008.csv')"
      ],
      "metadata": {
        "id": "ni08GPbmGfqq"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "model_id = \"google/gemma-2b-it\"\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476,
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        },
        "id": "2VPtimw31BS3",
        "outputId": "002f8d1b-aa9e-47a2-b7d2-ff39348e0491"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
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          "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": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
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        {
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          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03842d4e370f46b697a1db0f102807a6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"user: Give me an example prompt to make an LLM tell me a story. model:\"\n",
        "device = \"cuda:0\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens=80)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQA430us1nnB",
        "outputId": "81efcb78-1283-4656-9f52-7c1eba62cb0f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: Give me an example prompt to make an LLM tell me a story. model:\n",
            "\n",
            "Sure, here's an example prompt to make an LLM tell you a story:\n",
            "\n",
            "**Prompt:**\n",
            "\n",
            "Write a story about a young woman named Anya who is struggling to find her place in the world. She is a college student who has always been ambitious but has also faced challenges due to her family's financial situation. Despite her struggles, Anya is determined to succeed and find her\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_data = df['prompt'].apply(lambda x: tokenizer(x))\n",
        "purpose_data = df['purpose'].apply(lambda x: tokenizer(x))"
      ],
      "metadata": {
        "id": "7xnhq-BB30mF"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_data.tolist()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-NTLXTC4XG6",
        "outputId": "7680db86-bdf6-4884-a6f4-08b8c83ae31e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [2, 70029, 4065, 235290, 1547, 235290, 8847, 12027, 611, 1368, 577, 1501, 476, 5985, 578, 7017, 41174, 832, 235290, 38197, 31240, 774, 3818, 13356, 15555, 235265, 714, 5608, 1412, 3707, 14548, 235269, 9697, 604, 43973, 573, 31240, 235269, 578, 5942, 21448, 689, 117043, 674, 798, 614, 4192, 235265, 32788, 235269, 573, 5608, 1412, 614, 5952, 575, 3110, 578, 66004, 5255, 235269, 675, 10055, 79356, 689, 25340, 577, 10395, 575, 573, 2185, 235265], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.DataFrame({'prompt': prompt_data, 'purpose': purpose_data})"
      ],
      "metadata": {
        "id": "habT-V2T5PSN"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "hf_dataset = Dataset.from_dict({'prompt': prompt_data.to_list(), 'purpose': purpose_data.to_list()})"
      ],
      "metadata": {
        "id": "q5_NNKSl7xyz"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=8,\n",
        "    target_modules=[\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n"
      ],
      "metadata": {
        "id": "sjiNemD35tK5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from trl import SFTTrainer\n",
        "\n",
        "def formatting_func(example):\n",
        "    # text = f\"user: Give me a prompt {example['purpose'][0]}\\nmodel: Prompt: {example['prompt'][0]}\"\n",
        "    text = f\"Purpose: {example['purpose'][0]}\\nPrompt: {example['prompt'][0]}\"\n",
        "    return [text]\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=hf_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=10,\n",
        "        max_steps=60,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "    ),\n",
        "    peft_config=lora_config,\n",
        "    formatting_func=formatting_func,\n",
        ")\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "id": "WcAJW_uM4lin"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"user: Give me a prompt to for assessing the connectivity of a graph that has 10 vertices and 15 edges, considering that no vertex is connected to more than 3 other vertices. model:\"\n",
        "device = \"cuda:0\"\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens=120, temperature=0)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMP8qdaC5utI",
        "outputId": "55d23328-650e-4ca0-fec1-fa6530781530"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user: Give me a prompt to for assessing the connectivity of a graph that has 10 vertices and 15 edges, considering that no vertex is connected to more than 3 other vertices. model:\n",
            "\n",
            "**Prompt:**\n",
            "\n",
            "Assess the connectivity of the following graph with 10 vertices and 15 edges, considering that no vertex is connected to more than 3 other vertices. Use the following metrics:\n",
            "\n",
            "- **Degree** of each vertex\n",
            "- **Number of connected components**\n",
            "- **Total edge count**\n",
            "- **Minimum degree**\n",
            "- **Maximum degree**\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model('/content/drive/MyDrive/Colab Notebooks/gemma_1008')\n"
      ],
      "metadata": {
        "id": "HgWph1o08p52"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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      "execution_count": 21,
      "outputs": [
        {
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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    {
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  ]
}