llm-finetuning

14.Finetuning_Mistral_7b_Using_AutoTrain.ipynb
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            "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
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            "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
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  },
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  "cells": [
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "view-in-github",
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        "colab_type": "text"
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      },
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      "source": [
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        "<a href=\"https://colab.research.google.com/github/ashishpatel26/LLM-Finetuning/blob/main/14.Finetuning_Mistral_7b_Using_AutoTrain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "source": [
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        "## Fine-tuning Mistral 7b with AutoTrain"
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      ],
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      "metadata": {
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        "id": "7oRhTab-3Isg"
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      }
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    },
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    {
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      "cell_type": "markdown",
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      "source": [
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        "Setup Runtime\n",
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        "For fine-tuning Llama, a GPU instance is essential. Follow the directions below:\n",
1351
        "\n",
1352
        "- Go to `Runtime` (located in the top menu bar).\n",
1353
        "- Select `Change Runtime Type`.\n",
1354
        "- Choose `T4 GPU` (or a comparable option)."
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      ],
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      "metadata": {
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        "id": "yhDioAdc3ML5"
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      }
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    },
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    {
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      "cell_type": "markdown",
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      "source": [
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        "### Step 1: Setup Environment"
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      ],
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      "metadata": {
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        "id": "IJZt3QI73kWF"
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      }
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    },
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    {
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      "cell_type": "code",
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      "source": [
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        "!pip install pandas autotrain-advanced -q"
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      ],
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "UgvqeBz_3XvO",
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        "outputId": "96376e39-7fe6-4d68-fe4b-eb5d8f3a3f8f"
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      },
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      "execution_count": 1,
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      "outputs": [
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        {
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          "output_type": "stream",
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          "name": "stdout",
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          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.5/129.5 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.1/174.1 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.4/13.4 MB\u001b[0m \u001b[31m46.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m31.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.0/60.0 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m404.2/404.2 kB\u001b[0m \u001b[31m43.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m80.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m304.5/304.5 kB\u001b[0m \u001b[31m23.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m880.6/880.6 kB\u001b[0m \u001b[31m35.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1400
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
1401
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m78.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.5/242.5 kB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1405
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m32.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1406
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.1/20.1 MB\u001b[0m \u001b[31m85.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1407
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1408
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m90.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1409
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.6/85.6 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1410
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.0/118.0 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1411
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m93.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1412
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m122.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1413
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.1/258.1 kB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1414
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m70.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1415
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1416
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1417
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1418
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1419
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1420
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1421
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.3/66.3 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1422
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
1423
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.2/298.2 kB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1424
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.7/75.7 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1425
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.7/138.7 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1426
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1427
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1428
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1429
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m56.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1430
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m226.0/226.0 kB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1431
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m66.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1432
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m118.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1433
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.7/78.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1434
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1435
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1436
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1437
            "\u001b[?25h  Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
1438
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
1439
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
1440
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
1441
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.23.4 which is incompatible.\u001b[0m\u001b[31m\n",
1442
            "\u001b[0m"
1443
          ]
1444
        }
1445
      ]
1446
    },
1447
    {
1448
      "cell_type": "code",
1449
      "execution_count": 2,
1450
      "metadata": {
1451
        "colab": {
1452
          "base_uri": "https://localhost:8080/"
1453
        },
1454
        "id": "kwStofw4257S",
1455
        "outputId": "3c3881f0-9bd5-442f-dd3a-b363cc8d3c88"
1456
      },
1457
      "outputs": [
1458
        {
1459
          "output_type": "stream",
1460
          "name": "stdout",
1461
          "text": [
1462
            "> \u001b[1mINFO    Installing latest transformers@main\u001b[0m\n",
1463
            "> \u001b[1mINFO    Successfully installed latest transformers\u001b[0m\n",
1464
            "> \u001b[1mINFO    Installing latest peft@main\u001b[0m\n",
1465
            "> \u001b[1mINFO    Successfully installed latest peft\u001b[0m\n",
1466
            "> \u001b[1mINFO    Installing latest diffusers@main\u001b[0m\n",
1467
            "> \u001b[1mINFO    Successfully installed latest diffusers\u001b[0m\n",
1468
            "> \u001b[1mINFO    Installing latest trl@main\u001b[0m\n",
1469
            "> \u001b[1mINFO    Successfully installed latest trl\u001b[0m\n",
1470
            "> \u001b[1mINFO    Installing latest xformers\u001b[0m\n",
1471
            "> \u001b[1mINFO    Successfully installed latest xformers\u001b[0m\n",
1472
            "> \u001b[1mINFO    Installing latest PyTorch\u001b[0m\n",
1473
            "> \u001b[1mINFO    Successfully installed latest PyTorch\u001b[0m\n"
1474
          ]
1475
        }
1476
      ],
1477
      "source": [
1478
        "!autotrain setup --update-torch"
1479
      ]
1480
    },
1481
    {
1482
      "cell_type": "markdown",
1483
      "source": [
1484
        "## Step 2: Connect to HuggingFace for Model Upload\n",
1485
        "\n",
1486
        "### Logging to Hugging Face\n",
1487
        "To make sure the model can be uploaded to be used for Inference, it's necessary to log in to the Hugging Face hub.\n",
1488
        "\n",
1489
        "### Getting a Hugging Face token\n",
1490
        "Steps:\n",
1491
        "\n",
1492
        "1. Navigate to this URL: https://huggingface.co/settings/tokens\n",
1493
        "2. Create a write `token` and copy it to your clipboard\n",
1494
        "3. Run the code below and enter your `token`"
1495
      ],
1496
      "metadata": {
1497
        "id": "H-zXccJMZEx2"
1498
      }
1499
    },
1500
    {
1501
      "cell_type": "code",
1502
      "source": [
1503
        "from huggingface_hub import notebook_login\n",
1504
        "notebook_login()"
1505
      ],
1506
      "metadata": {
1507
        "colab": {
1508
          "base_uri": "https://localhost:8080/",
1509
          "height": 145,
1510
          "referenced_widgets": [
1511
            "93721b72963843db8afd2dc95b1a7e26",
1512
            "3c4c437c3be348a6beaa549a070e7d03",
1513
            "bef331d1317e4dca8423b41d9a4d5a46",
1514
            "fd8eb83e711f4efeb5392159094979ee",
1515
            "361f03f842874570ba0f5a7992ef85bb",
1516
            "da5b0da670514701962d1fb278d2c806",
1517
            "229eb3fe560b47cfaf90bd897d0356a3",
1518
            "2488b59a84c4450a87d03d1a7416131a",
1519
            "aba01680bd644852bdd01943fe6ff3a8",
1520
            "32368ce026824d4faad5f96bb523b1ef",
1521
            "6c9bfecb5c7d4218b905c5d9d1a94e67",
1522
            "cdcf8ddbe33d443ebb8e746da30ac0e7",
1523
            "0394a80f33694ddcaac86d11eb55dfba",
1524
            "2087238355ef44719f296fa797fdd1a4",
1525
            "3a25c5df13944295882f0114971dad95",
1526
            "9c9e13827ccd49c28eb7e4b5b7bce367",
1527
            "d65e536585574e1ebbe7dc596e5fff5b",
1528
            "1878c66b2eae453a864ffead4070b1b1",
1529
            "07a86a9d6045490582005ae3dc6235b4",
1530
            "26203a05198d4ceeacbf5e773709f351",
1531
            "1b54ffd23ba74eeebd9c469f440fb681",
1532
            "6b08ab9f42604d50b75ca79953cdb513",
1533
            "da126394b0454dfa92ead879aa4d05f8",
1534
            "cdad03b3eb6f4b8498d4c095ef4bd77c",
1535
            "3ea83882b0c24fc6bdc55ec477e8d966",
1536
            "9cee18be45e147d094a9f3d563e43deb",
1537
            "c7a697bfe78e4ef3ba7c086c6a7dc9a0",
1538
            "3e3a59c9cd1e4bab9a7134b3ed460a70",
1539
            "13f4a118e7ef4a148bde03a6e84e8aa3",
1540
            "da203394bbcb4a3a98ccf00c99c8397c",
1541
            "e72159dd38c04fda9b6944b21ef7ee18",
1542
            "3be1673dc1ce401f9bf74665671fe25e"
1543
          ]
1544
        },
1545
        "id": "VzMLmLP86Ub-",
1546
        "outputId": "dad1be39-2a4b-4979-d08c-6254bb496948"
1547
      },
1548
      "execution_count": 4,
1549
      "outputs": [
1550
        {
1551
          "output_type": "display_data",
1552
          "data": {
1553
            "text/plain": [
1554
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
1555
            ],
1556
            "application/vnd.jupyter.widget-view+json": {
1557
              "version_major": 2,
1558
              "version_minor": 0,
1559
              "model_id": "93721b72963843db8afd2dc95b1a7e26"
1560
            }
1561
          },
1562
          "metadata": {}
1563
        }
1564
      ]
1565
    },
1566
    {
1567
      "cell_type": "markdown",
1568
      "source": [
1569
        "## Step 3: Upload your dataset\n",
1570
        "\n",
1571
        "Add your data set to the root directory in the Colab under the name train.csv. The AutoTrain command will look for your data there under that name.\n",
1572
        "\n",
1573
        "#### Don't have a data set and want to try finetuning on an example data set?\n",
1574
        "If you don't have a dataset you can run these commands below to get an example data set and save it to train.csv"
1575
      ],
1576
      "metadata": {
1577
        "id": "qY932JBNZmtA"
1578
      }
1579
    },
1580
    {
1581
      "cell_type": "code",
1582
      "source": [
1583
        "!git clone https://github.com/joshbickett/finetune-llama-2.git\n",
1584
        "%cd finetune-llama-2\n",
1585
        "%mv train.csv ../train.csv\n",
1586
        "%cd .."
1587
      ],
1588
      "metadata": {
1589
        "id": "JxTn4r_YZdkY"
1590
      },
1591
      "execution_count": 5,
1592
      "outputs": []
1593
    },
1594
    {
1595
      "cell_type": "code",
1596
      "source": [
1597
        "import pandas as pd\n",
1598
        "df = pd.read_csv(\"train.csv\")\n",
1599
        "df"
1600
      ],
1601
      "metadata": {
1602
        "colab": {
1603
          "base_uri": "https://localhost:8080/",
1604
          "height": 1849
1605
        },
1606
        "id": "NUb-rkeoZzZ6",
1607
        "outputId": "9dad4111-a670-4801-ba9c-07da36e93884"
1608
      },
1609
      "execution_count": 6,
1610
      "outputs": [
1611
        {
1612
          "output_type": "execute_result",
1613
          "data": {
1614
            "text/plain": [
1615
              "                           Concept  \\\n",
1616
              "0       A person walks in the rain   \n",
1617
              "1            A cat chasing a mouse   \n",
1618
              "2              A dog eating a bone   \n",
1619
              "3         A bird flying in the sky   \n",
1620
              "4        A fish swimming in a tank   \n",
1621
              "5        A child playing with toys   \n",
1622
              "6        A car driving on the road   \n",
1623
              "7    A flower blooming in a garden   \n",
1624
              "8          A bee collecting pollen   \n",
1625
              "9     A sun setting over the ocean   \n",
1626
              "10        A cow grazing in a field   \n",
1627
              "11       A snail racing on a track   \n",
1628
              "12        A penguin sliding on ice   \n",
1629
              "13    A lion roaring in the jungle   \n",
1630
              "14     A monkey swinging on a tree   \n",
1631
              "15   A turtle sunbathing on a rock   \n",
1632
              "16    A rabbit hopping in a meadow   \n",
1633
              "17      A squirrel collecting nuts   \n",
1634
              "18      A wolf howling at the moon   \n",
1635
              "19     A fox sneaking in the woods   \n",
1636
              "20       A bear fishing in a river   \n",
1637
              "21       A hippo bathing in a pond   \n",
1638
              "22    A giraffe eating from a tree   \n",
1639
              "23      An elephant spraying water   \n",
1640
              "24  A kangaroo jumping in a desert   \n",
1641
              "\n",
1642
              "                             Funny Description Prompt  \\\n",
1643
              "0   A person walks in the rain, wearing a suit mad...   \n",
1644
              "1   A cat, wearing detective attire and sunglasses...   \n",
1645
              "2   A dog with a chef's hat is eating a bone seaso...   \n",
1646
              "3   A bird with oversized sunglasses and a basebal...   \n",
1647
              "4   A fish in a snorkel and flippers swimming in a...   \n",
1648
              "5   A child in a superhero cape playing with alien...   \n",
1649
              "6   A car with legs instead of wheels running down...   \n",
1650
              "7   A flower wearing a top hat and bow tie bloomin...   \n",
1651
              "8   A bee with a backpack vacuum cleaner collectin...   \n",
1652
              "9   The sun wearing sunglasses and sipping on a tr...   \n",
1653
              "10  A cow with headphones on, jamming to music whi...   \n",
1654
              "11  A snail with a racing helmet and number '1' pa...   \n",
1655
              "12  A penguin in ice skates, doing figure skating ...   \n",
1656
              "13  A lion with a microphone, singing a ballad in ...   \n",
1657
              "14  A monkey in a trapeze artist outfit, swinging ...   \n",
1658
              "15  A turtle with sunglasses on, sunbathing on a r...   \n",
1659
              "16  A rabbit in basketball attire, hopping around ...   \n",
1660
              "17  A squirrel in a miner's helmet, using a drill ...   \n",
1661
              "18  A wolf in pajamas, howling at the moon, with a...   \n",
1662
              "19  A fox in ninja attire, sneaking around the woo...   \n",
1663
              "20  A bear with a fishing rod and a fisherman's ha...   \n",
1664
              "21  A hippo with a shower cap on, using a giant ru...   \n",
1665
              "22  A giraffe with a long scarf, eating from a tre...   \n",
1666
              "23  An elephant with a water gun, having a water f...   \n",
1667
              "24  A kangaroo with spring shoes, jumping around i...   \n",
1668
              "\n",
1669
              "                                                 text  \n",
1670
              "0   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1671
              "1   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1672
              "2   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1673
              "3   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1674
              "4   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1675
              "5   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1676
              "6   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1677
              "7   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1678
              "8   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1679
              "9   ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1680
              "10  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1681
              "11  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1682
              "12  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1683
              "13  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1684
              "14  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1685
              "15  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1686
              "16  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1687
              "17  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1688
              "18  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1689
              "19  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1690
              "20  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1691
              "21  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1692
              "22  ###Human:\\ngenerate a midjourney prompt for A ...  \n",
1693
              "23  ###Human:\\ngenerate a midjourney prompt for An...  \n",
1694
              "24  ###Human:\\ngenerate a midjourney prompt for A ...  "
1695
            ],
1696
            "text/html": [
1697
              "\n",
1698
              "  <div id=\"df-10103957-7c94-44ed-adb0-80fbb9e799e9\" class=\"colab-df-container\">\n",
1699
              "    <div>\n",
1700
              "<style scoped>\n",
1701
              "    .dataframe tbody tr th:only-of-type {\n",
1702
              "        vertical-align: middle;\n",
1703
              "    }\n",
1704
              "\n",
1705
              "    .dataframe tbody tr th {\n",
1706
              "        vertical-align: top;\n",
1707
              "    }\n",
1708
              "\n",
1709
              "    .dataframe thead th {\n",
1710
              "        text-align: right;\n",
1711
              "    }\n",
1712
              "</style>\n",
1713
              "<table border=\"1\" class=\"dataframe\">\n",
1714
              "  <thead>\n",
1715
              "    <tr style=\"text-align: right;\">\n",
1716
              "      <th></th>\n",
1717
              "      <th>Concept</th>\n",
1718
              "      <th>Funny Description Prompt</th>\n",
1719
              "      <th>text</th>\n",
1720
              "    </tr>\n",
1721
              "  </thead>\n",
1722
              "  <tbody>\n",
1723
              "    <tr>\n",
1724
              "      <th>0</th>\n",
1725
              "      <td>A person walks in the rain</td>\n",
1726
              "      <td>A person walks in the rain, wearing a suit mad...</td>\n",
1727
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1728
              "    </tr>\n",
1729
              "    <tr>\n",
1730
              "      <th>1</th>\n",
1731
              "      <td>A cat chasing a mouse</td>\n",
1732
              "      <td>A cat, wearing detective attire and sunglasses...</td>\n",
1733
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1734
              "    </tr>\n",
1735
              "    <tr>\n",
1736
              "      <th>2</th>\n",
1737
              "      <td>A dog eating a bone</td>\n",
1738
              "      <td>A dog with a chef's hat is eating a bone seaso...</td>\n",
1739
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1740
              "    </tr>\n",
1741
              "    <tr>\n",
1742
              "      <th>3</th>\n",
1743
              "      <td>A bird flying in the sky</td>\n",
1744
              "      <td>A bird with oversized sunglasses and a basebal...</td>\n",
1745
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1746
              "    </tr>\n",
1747
              "    <tr>\n",
1748
              "      <th>4</th>\n",
1749
              "      <td>A fish swimming in a tank</td>\n",
1750
              "      <td>A fish in a snorkel and flippers swimming in a...</td>\n",
1751
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1752
              "    </tr>\n",
1753
              "    <tr>\n",
1754
              "      <th>5</th>\n",
1755
              "      <td>A child playing with toys</td>\n",
1756
              "      <td>A child in a superhero cape playing with alien...</td>\n",
1757
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1758
              "    </tr>\n",
1759
              "    <tr>\n",
1760
              "      <th>6</th>\n",
1761
              "      <td>A car driving on the road</td>\n",
1762
              "      <td>A car with legs instead of wheels running down...</td>\n",
1763
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1764
              "    </tr>\n",
1765
              "    <tr>\n",
1766
              "      <th>7</th>\n",
1767
              "      <td>A flower blooming in a garden</td>\n",
1768
              "      <td>A flower wearing a top hat and bow tie bloomin...</td>\n",
1769
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1770
              "    </tr>\n",
1771
              "    <tr>\n",
1772
              "      <th>8</th>\n",
1773
              "      <td>A bee collecting pollen</td>\n",
1774
              "      <td>A bee with a backpack vacuum cleaner collectin...</td>\n",
1775
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1776
              "    </tr>\n",
1777
              "    <tr>\n",
1778
              "      <th>9</th>\n",
1779
              "      <td>A sun setting over the ocean</td>\n",
1780
              "      <td>The sun wearing sunglasses and sipping on a tr...</td>\n",
1781
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1782
              "    </tr>\n",
1783
              "    <tr>\n",
1784
              "      <th>10</th>\n",
1785
              "      <td>A cow grazing in a field</td>\n",
1786
              "      <td>A cow with headphones on, jamming to music whi...</td>\n",
1787
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1788
              "    </tr>\n",
1789
              "    <tr>\n",
1790
              "      <th>11</th>\n",
1791
              "      <td>A snail racing on a track</td>\n",
1792
              "      <td>A snail with a racing helmet and number '1' pa...</td>\n",
1793
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1794
              "    </tr>\n",
1795
              "    <tr>\n",
1796
              "      <th>12</th>\n",
1797
              "      <td>A penguin sliding on ice</td>\n",
1798
              "      <td>A penguin in ice skates, doing figure skating ...</td>\n",
1799
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1800
              "    </tr>\n",
1801
              "    <tr>\n",
1802
              "      <th>13</th>\n",
1803
              "      <td>A lion roaring in the jungle</td>\n",
1804
              "      <td>A lion with a microphone, singing a ballad in ...</td>\n",
1805
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1806
              "    </tr>\n",
1807
              "    <tr>\n",
1808
              "      <th>14</th>\n",
1809
              "      <td>A monkey swinging on a tree</td>\n",
1810
              "      <td>A monkey in a trapeze artist outfit, swinging ...</td>\n",
1811
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1812
              "    </tr>\n",
1813
              "    <tr>\n",
1814
              "      <th>15</th>\n",
1815
              "      <td>A turtle sunbathing on a rock</td>\n",
1816
              "      <td>A turtle with sunglasses on, sunbathing on a r...</td>\n",
1817
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1818
              "    </tr>\n",
1819
              "    <tr>\n",
1820
              "      <th>16</th>\n",
1821
              "      <td>A rabbit hopping in a meadow</td>\n",
1822
              "      <td>A rabbit in basketball attire, hopping around ...</td>\n",
1823
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1824
              "    </tr>\n",
1825
              "    <tr>\n",
1826
              "      <th>17</th>\n",
1827
              "      <td>A squirrel collecting nuts</td>\n",
1828
              "      <td>A squirrel in a miner's helmet, using a drill ...</td>\n",
1829
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1830
              "    </tr>\n",
1831
              "    <tr>\n",
1832
              "      <th>18</th>\n",
1833
              "      <td>A wolf howling at the moon</td>\n",
1834
              "      <td>A wolf in pajamas, howling at the moon, with a...</td>\n",
1835
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1836
              "    </tr>\n",
1837
              "    <tr>\n",
1838
              "      <th>19</th>\n",
1839
              "      <td>A fox sneaking in the woods</td>\n",
1840
              "      <td>A fox in ninja attire, sneaking around the woo...</td>\n",
1841
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1842
              "    </tr>\n",
1843
              "    <tr>\n",
1844
              "      <th>20</th>\n",
1845
              "      <td>A bear fishing in a river</td>\n",
1846
              "      <td>A bear with a fishing rod and a fisherman's ha...</td>\n",
1847
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1848
              "    </tr>\n",
1849
              "    <tr>\n",
1850
              "      <th>21</th>\n",
1851
              "      <td>A hippo bathing in a pond</td>\n",
1852
              "      <td>A hippo with a shower cap on, using a giant ru...</td>\n",
1853
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1854
              "    </tr>\n",
1855
              "    <tr>\n",
1856
              "      <th>22</th>\n",
1857
              "      <td>A giraffe eating from a tree</td>\n",
1858
              "      <td>A giraffe with a long scarf, eating from a tre...</td>\n",
1859
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1860
              "    </tr>\n",
1861
              "    <tr>\n",
1862
              "      <th>23</th>\n",
1863
              "      <td>An elephant spraying water</td>\n",
1864
              "      <td>An elephant with a water gun, having a water f...</td>\n",
1865
              "      <td>###Human:\\ngenerate a midjourney prompt for An...</td>\n",
1866
              "    </tr>\n",
1867
              "    <tr>\n",
1868
              "      <th>24</th>\n",
1869
              "      <td>A kangaroo jumping in a desert</td>\n",
1870
              "      <td>A kangaroo with spring shoes, jumping around i...</td>\n",
1871
              "      <td>###Human:\\ngenerate a midjourney prompt for A ...</td>\n",
1872
              "    </tr>\n",
1873
              "  </tbody>\n",
1874
              "</table>\n",
1875
              "</div>\n",
1876
              "    <div class=\"colab-df-buttons\">\n",
1877
              "\n",
1878
              "  <div class=\"colab-df-container\">\n",
1879
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10103957-7c94-44ed-adb0-80fbb9e799e9')\"\n",
1880
              "            title=\"Convert this dataframe to an interactive table.\"\n",
1881
              "            style=\"display:none;\">\n",
1882
              "\n",
1883
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
1884
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1885
              "  </svg>\n",
1886
              "    </button>\n",
1887
              "\n",
1888
              "  <style>\n",
1889
              "    .colab-df-container {\n",
1890
              "      display:flex;\n",
1891
              "      gap: 12px;\n",
1892
              "    }\n",
1893
              "\n",
1894
              "    .colab-df-convert {\n",
1895
              "      background-color: #E8F0FE;\n",
1896
              "      border: none;\n",
1897
              "      border-radius: 50%;\n",
1898
              "      cursor: pointer;\n",
1899
              "      display: none;\n",
1900
              "      fill: #1967D2;\n",
1901
              "      height: 32px;\n",
1902
              "      padding: 0 0 0 0;\n",
1903
              "      width: 32px;\n",
1904
              "    }\n",
1905
              "\n",
1906
              "    .colab-df-convert:hover {\n",
1907
              "      background-color: #E2EBFA;\n",
1908
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1909
              "      fill: #174EA6;\n",
1910
              "    }\n",
1911
              "\n",
1912
              "    .colab-df-buttons div {\n",
1913
              "      margin-bottom: 4px;\n",
1914
              "    }\n",
1915
              "\n",
1916
              "    [theme=dark] .colab-df-convert {\n",
1917
              "      background-color: #3B4455;\n",
1918
              "      fill: #D2E3FC;\n",
1919
              "    }\n",
1920
              "\n",
1921
              "    [theme=dark] .colab-df-convert:hover {\n",
1922
              "      background-color: #434B5C;\n",
1923
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1924
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1925
              "      fill: #FFFFFF;\n",
1926
              "    }\n",
1927
              "  </style>\n",
1928
              "\n",
1929
              "    <script>\n",
1930
              "      const buttonEl =\n",
1931
              "        document.querySelector('#df-10103957-7c94-44ed-adb0-80fbb9e799e9 button.colab-df-convert');\n",
1932
              "      buttonEl.style.display =\n",
1933
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1934
              "\n",
1935
              "      async function convertToInteractive(key) {\n",
1936
              "        const element = document.querySelector('#df-10103957-7c94-44ed-adb0-80fbb9e799e9');\n",
1937
              "        const dataTable =\n",
1938
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1939
              "                                                    [key], {});\n",
1940
              "        if (!dataTable) return;\n",
1941
              "\n",
1942
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
1943
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1944
              "          + ' to learn more about interactive tables.';\n",
1945
              "        element.innerHTML = '';\n",
1946
              "        dataTable['output_type'] = 'display_data';\n",
1947
              "        await google.colab.output.renderOutput(dataTable, element);\n",
1948
              "        const docLink = document.createElement('div');\n",
1949
              "        docLink.innerHTML = docLinkHtml;\n",
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              "        element.appendChild(docLink);\n",
1951
              "      }\n",
1952
              "    </script>\n",
1953
              "  </div>\n",
1954
              "\n",
1955
              "\n",
1956
              "<div id=\"df-24b1b524-f6d0-40f7-b38a-584fc18ab9d2\">\n",
1957
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24b1b524-f6d0-40f7-b38a-584fc18ab9d2')\"\n",
1958
              "            title=\"Suggest charts.\"\n",
1959
              "            style=\"display:none;\">\n",
1960
              "\n",
1961
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "     width=\"24px\">\n",
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              "    <g>\n",
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1965
              "    </g>\n",
1966
              "</svg>\n",
1967
              "  </button>\n",
1968
              "\n",
1969
              "<style>\n",
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              "  .colab-df-quickchart {\n",
1971
              "      --bg-color: #E8F0FE;\n",
1972
              "      --fill-color: #1967D2;\n",
1973
              "      --hover-bg-color: #E2EBFA;\n",
1974
              "      --hover-fill-color: #174EA6;\n",
1975
              "      --disabled-fill-color: #AAA;\n",
1976
              "      --disabled-bg-color: #DDD;\n",
1977
              "  }\n",
1978
              "\n",
1979
              "  [theme=dark] .colab-df-quickchart {\n",
1980
              "      --bg-color: #3B4455;\n",
1981
              "      --fill-color: #D2E3FC;\n",
1982
              "      --hover-bg-color: #434B5C;\n",
1983
              "      --hover-fill-color: #FFFFFF;\n",
1984
              "      --disabled-bg-color: #3B4455;\n",
1985
              "      --disabled-fill-color: #666;\n",
1986
              "  }\n",
1987
              "\n",
1988
              "  .colab-df-quickchart {\n",
1989
              "    background-color: var(--bg-color);\n",
1990
              "    border: none;\n",
1991
              "    border-radius: 50%;\n",
1992
              "    cursor: pointer;\n",
1993
              "    display: none;\n",
1994
              "    fill: var(--fill-color);\n",
1995
              "    height: 32px;\n",
1996
              "    padding: 0;\n",
1997
              "    width: 32px;\n",
1998
              "  }\n",
1999
              "\n",
2000
              "  .colab-df-quickchart:hover {\n",
2001
              "    background-color: var(--hover-bg-color);\n",
2002
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
2003
              "    fill: var(--button-hover-fill-color);\n",
2004
              "  }\n",
2005
              "\n",
2006
              "  .colab-df-quickchart-complete:disabled,\n",
2007
              "  .colab-df-quickchart-complete:disabled:hover {\n",
2008
              "    background-color: var(--disabled-bg-color);\n",
2009
              "    fill: var(--disabled-fill-color);\n",
2010
              "    box-shadow: none;\n",
2011
              "  }\n",
2012
              "\n",
2013
              "  .colab-df-spinner {\n",
2014
              "    border: 2px solid var(--fill-color);\n",
2015
              "    border-color: transparent;\n",
2016
              "    border-bottom-color: var(--fill-color);\n",
2017
              "    animation:\n",
2018
              "      spin 1s steps(1) infinite;\n",
2019
              "  }\n",
2020
              "\n",
2021
              "  @keyframes spin {\n",
2022
              "    0% {\n",
2023
              "      border-color: transparent;\n",
2024
              "      border-bottom-color: var(--fill-color);\n",
2025
              "      border-left-color: var(--fill-color);\n",
2026
              "    }\n",
2027
              "    20% {\n",
2028
              "      border-color: transparent;\n",
2029
              "      border-left-color: var(--fill-color);\n",
2030
              "      border-top-color: var(--fill-color);\n",
2031
              "    }\n",
2032
              "    30% {\n",
2033
              "      border-color: transparent;\n",
2034
              "      border-left-color: var(--fill-color);\n",
2035
              "      border-top-color: var(--fill-color);\n",
2036
              "      border-right-color: var(--fill-color);\n",
2037
              "    }\n",
2038
              "    40% {\n",
2039
              "      border-color: transparent;\n",
2040
              "      border-right-color: var(--fill-color);\n",
2041
              "      border-top-color: var(--fill-color);\n",
2042
              "    }\n",
2043
              "    60% {\n",
2044
              "      border-color: transparent;\n",
2045
              "      border-right-color: var(--fill-color);\n",
2046
              "    }\n",
2047
              "    80% {\n",
2048
              "      border-color: transparent;\n",
2049
              "      border-right-color: var(--fill-color);\n",
2050
              "      border-bottom-color: var(--fill-color);\n",
2051
              "    }\n",
2052
              "    90% {\n",
2053
              "      border-color: transparent;\n",
2054
              "      border-bottom-color: var(--fill-color);\n",
2055
              "    }\n",
2056
              "  }\n",
2057
              "</style>\n",
2058
              "\n",
2059
              "  <script>\n",
2060
              "    async function quickchart(key) {\n",
2061
              "      const quickchartButtonEl =\n",
2062
              "        document.querySelector('#' + key + ' button');\n",
2063
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
2064
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
2065
              "      try {\n",
2066
              "        const charts = await google.colab.kernel.invokeFunction(\n",
2067
              "            'suggestCharts', [key], {});\n",
2068
              "      } catch (error) {\n",
2069
              "        console.error('Error during call to suggestCharts:', error);\n",
2070
              "      }\n",
2071
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
2072
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
2073
              "    }\n",
2074
              "    (() => {\n",
2075
              "      let quickchartButtonEl =\n",
2076
              "        document.querySelector('#df-24b1b524-f6d0-40f7-b38a-584fc18ab9d2 button');\n",
2077
              "      quickchartButtonEl.style.display =\n",
2078
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
2079
              "    })();\n",
2080
              "  </script>\n",
2081
              "</div>\n",
2082
              "    </div>\n",
2083
              "  </div>\n"
2084
            ]
2085
          },
2086
          "metadata": {},
2087
          "execution_count": 6
2088
        },
2089
        {
2090
          "output_type": "display_data",
2091
          "data": {
2092
            "text/plain": [
2093
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cdbda5c8eb0>"
2094
            ],
2095
            "text/html": [
2096
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
2097
              "<style>\n",
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              "  .colab-quickchart-section-title {\n",
2099
              "      clear: both;\n",
2100
              "  }\n",
2101
              "</style>"
2102
            ]
2103
          },
2104
          "metadata": {}
2105
        },
2106
        {
2107
          "output_type": "display_data",
2108
          "data": {
2109
            "text/plain": [
2110
              "import numpy as np\n",
2111
              "from google.colab import autoviz\n",
2112
              "\n",
2113
              "def value_plot(df, y, figscale=1):\n",
2114
              "  from matplotlib import pyplot as plt\n",
2115
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
2116
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
2117
              "  plt.tight_layout()\n",
2118
              "  return autoviz.MplChart.from_current_mpl_state()\n",
2119
              "\n",
2120
              "chart = value_plot(_df_0, *['index'], **{})\n",
2121
              "chart"
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2123
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              "\">\n",
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              "        \n",
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              "      </div>\n",
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              "      <script></script>\n",
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              "        (() => {\n",
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              "          chartElement.onclick = getCodeForChartHandler;\n",
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              "        })();\n",
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              "      </script>\n",
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              "      <style>\n",
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              "        .colab-quickchart-chart-with-code  {\n",
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              "            display: block;\n",
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              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
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              "  .colab-quickchart-section-title {\n",
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        },
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          "output_type": "display_data",
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          "data": {
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            "text/plain": [
2331
              "import numpy as np\n",
2332
              "from google.colab import autoviz\n",
2333
              "\n",
2334
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
2335
              "  from matplotlib import pyplot as plt\n",
2336
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
2337
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
2338
              "  plt.tight_layout()\n",
2339
              "  return autoviz.MplChart.from_current_mpl_state()\n",
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              "\">\n",
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              "      </div>\n",
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              "                'getCodeForChart', [\"chart-f1d9ad68-95de-4e16-aa2c-ecd0ed077302\"], {});\n",
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              "            const responseJson = chartCodeResponse.data['application/json'];\n",
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2460
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2483
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            "text/html": [
2485
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
2486
              "<style>\n",
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              "  .colab-quickchart-section-title {\n",
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              "      clear: both;\n",
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              "  }\n",
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          "output_type": "display_data",
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          "data": {
2498
            "text/plain": [
2499
              "import numpy as np\n",
2500
              "from google.colab import autoviz\n",
2501
              "\n",
2502
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
2503
              "  from matplotlib import pyplot as plt\n",
2504
              "  import seaborn as sns\n",
2505
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
2506
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
2507
              "  def _plot_series(series, series_name, series_index=0):\n",
2508
              "    if value_colname == 'count()':\n",
2509
              "      counted = (series[timelike_colname]\n",
2510
              "                 .value_counts()\n",
2511
              "                 .reset_index(name='counts')\n",
2512
              "                 .rename({'index': timelike_colname}, axis=1)\n",
2513
              "                 .sort_values(timelike_colname, ascending=True))\n",
2514
              "      xs = counted[timelike_colname]\n",
2515
              "      ys = counted['counts']\n",
2516
              "    else:\n",
2517
              "      xs = series[timelike_colname]\n",
2518
              "      ys = series[value_colname]\n",
2519
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
2520
              "\n",
2521
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
2522
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
2523
              "  if series_colname:\n",
2524
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
2525
              "      _plot_series(series, series_name, i)\n",
2526
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
2527
              "  else:\n",
2528
              "    _plot_series(df, '')\n",
2529
              "  sns.despine(fig=fig, ax=ax)\n",
2530
              "  plt.xlabel(timelike_colname)\n",
2531
              "  plt.ylabel(value_colname)\n",
2532
              "  return autoviz.MplChart.from_current_mpl_state()\n",
2533
              "\n",
2534
              "chart = time_series_multiline(_df_2, *['index', 'count()', None], **{})\n",
2535
              "chart"
2536
            ],
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              "MGGC/v73v+vDDz/UunXrFBLCf5KAR4HT/gDq5JVXXpEktWnTRtHR0Tp79qxatGihvLw8paamSpLi\n",
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              "4uI0dOhQ77/55JNPtH//fmVmZkqSbt68qdDQUEnSt7/9bf3tb3/TyJEjtWPHDkVGRj7aFwQYjPgD\n",
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              "qJMmTZp4/3doaKj3OwS+7IuvH5Vuf7/A5s2bFRsbe8d61dXV+s9//qMnnnjCexYBwKPBOTYADyw8\n",
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              "PFx9+/bV2rVrJUn5+fnas2eP9/ExY8bo3Xff9f6gcPXqVRUUFEi6/XWk3bp1U05OjubNm+e9H4D9\n",
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              "iD+Ah7J27VqtWrVKvXr10htvvKFhw4Z5H1u+fLmaNm2q+Ph49e7dW88++6wKCwu1bds2/elPf9KH\n",
2652
              "H36omJgYZWZmasKECaqoqAjiKwHMwWf7AwBgGN75AwBgGOIPAIBhiD8AAIYh/gAAGIb4AwBgGOIP\n",
2653
              "AIBhiD8AAIYh/gAAGIb4AwBgGOIPAIBh/gdT0TI3fq3c8gAAAABJRU5ErkJggg==\n",
2654
              "\">\n",
2655
              "        \n",
2656
              "      </div>\n",
2657
              "      <script></script>\n",
2658
              "      <script type=\"text/javascript\">\n",
2659
              "        (() => {\n",
2660
              "          const chartElement = document.getElementById(\"chart-6f27e1e2-13ed-4911-84dd-fd64d306e6d4\");\n",
2661
              "          async function getCodeForChartHandler(event) {\n",
2662
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
2663
              "                'getCodeForChart', [\"chart-6f27e1e2-13ed-4911-84dd-fd64d306e6d4\"], {});\n",
2664
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
2665
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
2666
              "          }\n",
2667
              "          chartElement.onclick = getCodeForChartHandler;\n",
2668
              "        })();\n",
2669
              "      </script>\n",
2670
              "      <style>\n",
2671
              "        .colab-quickchart-chart-with-code  {\n",
2672
              "            display: block;\n",
2673
              "            float: left;\n",
2674
              "            border: 1px solid transparent;\n",
2675
              "        }\n",
2676
              "\n",
2677
              "        .colab-quickchart-chart-with-code:hover {\n",
2678
              "            cursor: pointer;\n",
2679
              "            border: 1px solid #aaa;\n",
2680
              "        }\n",
2681
              "      </style>"
2682
            ]
2683
          },
2684
          "metadata": {}
2685
        }
2686
      ]
2687
    },
2688
    {
2689
      "cell_type": "code",
2690
      "source": [
2691
        "df['text'][15]"
2692
      ],
2693
      "metadata": {
2694
        "colab": {
2695
          "base_uri": "https://localhost:8080/",
2696
          "height": 35
2697
        },
2698
        "id": "3mr4WrwHZ0pv",
2699
        "outputId": "6934e624-7111-4fe1-cdf2-74e0d4bff778"
2700
      },
2701
      "execution_count": 7,
2702
      "outputs": [
2703
        {
2704
          "output_type": "execute_result",
2705
          "data": {
2706
            "text/plain": [
2707
              "'###Human:\\ngenerate a midjourney prompt for A turtle sunbathing on a rock\\n\\n###Assistant:\\nA turtle with sunglasses on, sunbathing on a rock, with a mini fan and a drink beside it.'"
2708
            ],
2709
            "application/vnd.google.colaboratory.intrinsic+json": {
2710
              "type": "string"
2711
            }
2712
          },
2713
          "metadata": {},
2714
          "execution_count": 7
2715
        }
2716
      ]
2717
    },
2718
    {
2719
      "cell_type": "markdown",
2720
      "source": [
2721
        "## Step 4: Overview of AutoTrain command\n",
2722
        "\n",
2723
        "#### Short overview of what the command flags do.\n",
2724
        "\n",
2725
        "- `!autotrain`: Command executed in environments like a Jupyter notebook to run shell commands directly. `autotrain` is an automatic training utility.\n",
2726
        "\n",
2727
        "- `llm`: A sub-command or argument specifying the type of task\n",
2728
        "\n",
2729
        "- `--train`: Initiates the training process.\n",
2730
        "\n",
2731
        "- `--project_name`: Sets the name of the project\n",
2732
        "\n",
2733
        "- `--model abhishek/llama-2-7b-hf-small-shards`: Specifies original model that is hosted on Hugging Face named \"llama-2-7b-hf-small-shards\" under the \"abhishek\".\n",
2734
        "\n",
2735
        "- `--data_path .`: The path to the dataset for training. The \".\" refers to the current directory. The `train.csv` file needs to be located in this directory.\n",
2736
        "\n",
2737
        "- `--use_int4`: Use of INT4 quantization to reduce model size and speed up inference times at the cost of some precision.\n",
2738
        "\n",
2739
        "- `--learning_rate 2e-4`: Sets the learning rate for training to 0.0002.\n",
2740
        "\n",
2741
        "- `--train_batch_size 12`: Sets the batch size for training to 12.\n",
2742
        "\n",
2743
        "- `--num_train_epochs 3`: The training process will iterate over the dataset 3 times.\n",
2744
        "\n",
2745
        "### Steps needed before running\n",
2746
        "Go to the `!autotrain` code cell below and update it by following the steps below:\n",
2747
        "\n",
2748
        "1. After `--project_name` replace `*enter-a-project-name*` with the name that you'd like to call the project\n",
2749
        "2. After `--repo_id` replace `*username*/*repository*`. Replace `*username*` with your Hugging Face username and `*repository*` with the repository name you'd like it to be created under. You don't need to create this repository before hand, it will automatically be created and uploaded once the training is completed.\n",
2750
        "3. Confirm that `train.csv` is in the root directory in the Colab. The `--data_path .` flag will make it so that AutoTrain looks for your data there.\n",
2751
        "4. Make sure to add the LoRA Target Modules to be trained `--target-modules q_proj, v_proj`\n",
2752
        "5. Once you've made these changes you're all set, run the command below!"
2753
      ],
2754
      "metadata": {
2755
        "id": "LEFbHxoPaDE_"
2756
      }
2757
    },
2758
    {
2759
      "cell_type": "code",
2760
      "source": [
2761
        "!autotrain llm --train --project_name mistral-7b-mj-finetuned --model bn22/Mistral-7B-Instruct-v0.1-sharded --data_path . --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 12 --num_train_epochs 3 --trainer sft --target_modules q_proj,v_proj --push_to_hub --repo_id ashishpatel26/mistral-7b-mj-finetuned"
2762
      ],
2763
      "metadata": {
2764
        "id": "wFS31VJsZ-pa"
2765
      },
2766
      "execution_count": 8,
2767
      "outputs": []
2768
    },
2769
    {
2770
      "cell_type": "markdown",
2771
      "source": [
2772
        "## Step 5: Completed 🎉\n",
2773
        "After the command above is completed your Model will be uploaded to Hugging Face.\n",
2774
        "\n",
2775
        "#### Learn more about AutoTrain (optional)\n",
2776
        "If you want to learn more about what command-line flags are available"
2777
      ],
2778
      "metadata": {
2779
        "id": "gEf6G0iPc0Nr"
2780
      }
2781
    },
2782
    {
2783
      "cell_type": "markdown",
2784
      "source": [
2785
        "## Step 6: Inference Engine"
2786
      ],
2787
      "metadata": {
2788
        "id": "FIoxuAEAfJ4z"
2789
      }
2790
    },
2791
    {
2792
      "cell_type": "code",
2793
      "source": [
2794
        "!autotrain llm -h"
2795
      ],
2796
      "metadata": {
2797
        "id": "aYsYyXmrc0xu"
2798
      },
2799
      "execution_count": 9,
2800
      "outputs": []
2801
    },
2802
    {
2803
      "cell_type": "code",
2804
      "source": [
2805
        "!pip install -q peft  accelerate bitsandbytes safetensors"
2806
      ],
2807
      "metadata": {
2808
        "id": "5m1ouhWhc2fr"
2809
      },
2810
      "execution_count": 1,
2811
      "outputs": []
2812
    },
2813
    {
2814
      "cell_type": "code",
2815
      "source": [
2816
        "import torch\n",
2817
        "from peft import PeftModel\n",
2818
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
2819
        "import transformers\n",
2820
        "adapters_name = \"ashishpatel26/mistral-7b-mj-finetuned\"\n",
2821
        "model_name = \"bn22/Mistral-7B-Instruct-v0.1-sharded\" #\"mistralai/Mistral-7B-Instruct-v0.1\"\n",
2822
        "\n",
2823
        "\n",
2824
        "device = \"cuda\" # the device to load the model onto"
2825
      ],
2826
      "metadata": {
2827
        "id": "8s-nDnnPc--U"
2828
      },
2829
      "execution_count": 2,
2830
      "outputs": []
2831
    },
2832
    {
2833
      "cell_type": "code",
2834
      "source": [
2835
        "bnb_config = transformers.BitsAndBytesConfig(\n",
2836
        "    load_in_4bit=True,\n",
2837
        "    bnb_4bit_use_double_quant=True,\n",
2838
        "    bnb_4bit_quant_type=\"nf4\",\n",
2839
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
2840
        ")"
2841
      ],
2842
      "metadata": {
2843
        "id": "HosPywN_dEpl"
2844
      },
2845
      "execution_count": 3,
2846
      "outputs": []
2847
    },
2848
    {
2849
      "cell_type": "code",
2850
      "source": [
2851
        "model = AutoModelForCausalLM.from_pretrained(\n",
2852
        "    model_name,\n",
2853
        "    load_in_4bit=True,\n",
2854
        "    torch_dtype=torch.bfloat16,\n",
2855
        "    quantization_config=bnb_config,\n",
2856
        "    device_map='auto'\n",
2857
        ")"
2858
      ],
2859
      "metadata": {
2860
        "colab": {
2861
          "base_uri": "https://localhost:8080/",
2862
          "height": 49,
2863
          "referenced_widgets": [
2864
            "fcb820b4909e413e98603c195818e0d4",
2865
            "6b0a6739adbe41e8a5c34f8a8868b977",
2866
            "0b9514defba84991b4f36485b7e630fb",
2867
            "6a7973751e4d4ca08ef4c53c97103868",
2868
            "0deb9610aaff49c488b6e89139fe31df",
2869
            "89f149a2080f4721a483ff535b6e6602",
2870
            "4f62c475347944d6b18ce79d125386fc",
2871
            "996db8f083904106913a3e4b4d6627c9",
2872
            "f6b2ea40822a41899aae6768c5a34c73",
2873
            "4523834103534e2b9fb804bdb5265a1e",
2874
            "3912010e0694457f9f777c1bbb996967"
2875
          ]
2876
        },
2877
        "id": "GtZx4CZUdt1f",
2878
        "outputId": "c01df71d-a70e-48d3-d651-0061856f1b57"
2879
      },
2880
      "execution_count": 4,
2881
      "outputs": [
2882
        {
2883
          "output_type": "display_data",
2884
          "data": {
2885
            "text/plain": [
2886
              "Loading checkpoint shards:   0%|          | 0/11 [00:00<?, ?it/s]"
2887
            ],
2888
            "application/vnd.jupyter.widget-view+json": {
2889
              "version_major": 2,
2890
              "version_minor": 0,
2891
              "model_id": "fcb820b4909e413e98603c195818e0d4"
2892
            }
2893
          },
2894
          "metadata": {}
2895
        }
2896
      ]
2897
    },
2898
    {
2899
      "cell_type": "markdown",
2900
      "source": [
2901
        "## Step 7: Peft Model Loading with upload model"
2902
      ],
2903
      "metadata": {
2904
        "id": "Uh5Xc0clfQkZ"
2905
      }
2906
    },
2907
    {
2908
      "cell_type": "code",
2909
      "source": [
2910
        "model = PeftModel.from_pretrained(model, adapters_name)"
2911
      ],
2912
      "metadata": {
2913
        "id": "Rt6sOPFVdvWX"
2914
      },
2915
      "execution_count": 5,
2916
      "outputs": []
2917
    },
2918
    {
2919
      "cell_type": "code",
2920
      "source": [
2921
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
2922
        "tokenizer.bos_token_id = 1\n",
2923
        "\n",
2924
        "stop_token_ids = [0]\n",
2925
        "\n",
2926
        "print(f\"Successfully loaded the model {model_name} into memory\")"
2927
      ],
2928
      "metadata": {
2929
        "colab": {
2930
          "base_uri": "https://localhost:8080/"
2931
        },
2932
        "id": "q3OArVILeoZH",
2933
        "outputId": "af68bc96-c9a8-4801-f8d6-5f2095101988"
2934
      },
2935
      "execution_count": 6,
2936
      "outputs": [
2937
        {
2938
          "output_type": "stream",
2939
          "name": "stderr",
2940
          "text": [
2941
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
2942
          ]
2943
        },
2944
        {
2945
          "output_type": "stream",
2946
          "name": "stdout",
2947
          "text": [
2948
            "Successfully loaded the model bn22/Mistral-7B-Instruct-v0.1-sharded into memory\n"
2949
          ]
2950
        }
2951
      ]
2952
    },
2953
    {
2954
      "cell_type": "code",
2955
      "source": [
2956
        "text = \"[INST] generate a midjourney prompt for A person walks in the rain [/INST]\"\n",
2957
        "\n",
2958
        "encoded = tokenizer(text, return_tensors=\"pt\", add_special_tokens=False)\n",
2959
        "model_input = encoded\n",
2960
        "model.to(device)\n",
2961
        "generated_ids = model.generate(**model_input, max_new_tokens=200, do_sample=True)\n",
2962
        "decoded = tokenizer.batch_decode(generated_ids)\n",
2963
        "print(decoded[0])"
2964
      ],
2965
      "metadata": {
2966
        "colab": {
2967
          "base_uri": "https://localhost:8080/"
2968
        },
2969
        "id": "ZbOOX8cve0lR",
2970
        "outputId": "3052b329-7bf5-4bb4-bec5-b71e881bbc21"
2971
      },
2972
      "execution_count": 7,
2973
      "outputs": [
2974
        {
2975
          "output_type": "stream",
2976
          "name": "stderr",
2977
          "text": [
2978
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
2979
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1539: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
2980
            "  warnings.warn(\n"
2981
          ]
2982
        },
2983
        {
2984
          "output_type": "stream",
2985
          "name": "stdout",
2986
          "text": [
2987
            "[INST] generate a midjourney prompt for A person walks in the rain [/INST] \"As you wander through the pouring rain, you can't help but wonder what the world would be like if things were different. What if the rain was a symbol of the turmoil in your life, and the sunshine promised a brighter future? What if you suddenly found yourself lost in a small town where time stood still, and the people were trapped in a time loop? As you struggle to find your way back to reality, you discover a mysterious stranger who seems to hold the key to unlocking the secrets of the town and your own past.\"</s>\n"
2988
          ]
2989
        }
2990
      ]
2991
    }
2992
  ]
2993
}
llm-finetuning

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