FineTuningMistral7BUsing8BitQuantizationWithLudwig.ipynb

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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/ibndias/poor-mans-llm/blob/main/FineTuningMistral7BUsing8BitQuantizationWithLudwig.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Install Ludwig and Ludwig's LLM related dependencies.**"
      ],
      "metadata": {
        "id": "wXMVoEFkPXJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QZBPosPfcWbA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y tensorflow --quiet\n",
        "!pip install --upgrade git+https://github.com/huggingface/transformers\n",
        "!pip install --upgrade git+https://github.com/huggingface/peft.git\n",
        "!pip install git+https://github.com/ludwig-ai/ludwig.git@master --quiet"
      ],
      "metadata": {
        "id": "yfYXyUUvM-R6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f2c39ff-fed5-43bf-a8fe-5c2883c80f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping tensorflow as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting git+https://github.com/huggingface/transformers\n",
            "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-8fqc93o4\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-8fqc93o4\n",
            "  Resolved https://github.com/huggingface/transformers to commit 43bfd093e1817c0333a1e10fcbdd54f1032baad0\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (3.12.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.14.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers==4.35.0.dev0) (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,>=0.16.4->transformers==4.35.0.dev0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (2.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (2023.7.22)\n",
            "Collecting git+https://github.com/huggingface/peft.git\n",
            "  Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-82yzmqcy\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-82yzmqcy\n",
            "  Resolved https://github.com/huggingface/peft.git to commit 99f792e8a3be001c55e71d148a7a8d25b931181f\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (23.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (5.9.4)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (6.0)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (2.0.1+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (4.35.0.dev0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (0.23.0)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (0.3.3)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->peft==0.6.0.dev0) (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft==0.6.0.dev0) (3.27.5)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft==0.6.0.dev0) (17.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.6.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.6.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.6.0.dev0) (0.14.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate>=0.21.0->peft==0.6.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft==0.6.0.dev0) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (2.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft==0.6.0.dev0) (1.3.0)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade datasets"
      ],
      "metadata": {
        "id": "Mj6CRIq4_eYd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50e016dc-0420-404f-ad44-c542d2eb9a4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.5)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.15)\n",
            "Requirement already satisfied: fsspec[http]<2023.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.16.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.3.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\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) (2023.3.post1)\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install py7zr"
      ],
      "metadata": {
        "id": "zlK4qI6a_lw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed951e56-7248-4ab7-ab9b-8601c3a3d45b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: py7zr in /usr/local/lib/python3.10/dist-packages (0.20.6)\n",
            "Requirement already satisfied: texttable in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.7.0)\n",
            "Requirement already satisfied: pycryptodomex>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from py7zr) (3.19.0)\n",
            "Requirement already satisfied: pyzstd>=0.14.4 in /usr/local/lib/python3.10/dist-packages (from py7zr) (0.15.9)\n",
            "Requirement already satisfied: pyppmd<1.1.0,>=0.18.1 in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.0.0)\n",
            "Requirement already satisfied: pybcj>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.0.1)\n",
            "Requirement already satisfied: multivolumefile>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from py7zr) (0.2.3)\n",
            "Requirement already satisfied: brotli>=1.0.9 in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.1.0)\n",
            "Requirement already satisfied: inflate64>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from py7zr) (0.3.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from py7zr) (5.9.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xformers"
      ],
      "metadata": {
        "id": "lxBu-hTh_sET",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "156a4ed7-63b3-49df-dfdb-dc2ce8545ecc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xformers in /usr/local/lib/python3.10/dist-packages (0.0.22)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xformers) (1.23.5)\n",
            "Requirement already satisfied: torch==2.0.1 in /usr/local/lib/python3.10/dist-packages (from xformers) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (17.0.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.1->xformers) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.1->xformers) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SFnqVkBgt9VX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show torch"
      ],
      "metadata": {
        "id": "RqicMQs_t8Nu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79d94c34-0934-4592-e91d-85ca09b97561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: torch\n",
            "Version: 2.0.1+cu118\n",
            "Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n",
            "Home-page: https://pytorch.org/\n",
            "Author: PyTorch Team\n",
            "Author-email: packages@pytorch.org\n",
            "License: BSD-3\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: filelock, jinja2, networkx, sympy, triton, typing-extensions\n",
            "Required-by: accelerate, fastai, ludwig, peft, torchaudio, torchdata, torchmetrics, torchtext, torchvision, triton, xformers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show transformers"
      ],
      "metadata": {
        "id": "OwO4iNggt7-F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08026894-1293-4d1d-a89d-dbe1bfc1da45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: transformers\n",
            "Version: 4.35.0.dev0\n",
            "Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n",
            "Home-page: https://github.com/huggingface/transformers\n",
            "Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n",
            "Author-email: transformers@huggingface.co\n",
            "License: Apache 2.0 License\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n",
            "Required-by: ludwig, peft\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yDbFgDs-_uIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y2zlqMNM_v7t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import os\n",
        "\n",
        "import copy\n",
        "\n",
        "import gc\n",
        "\n",
        "from typing import Any, Callable\n",
        "\n",
        "import time\n",
        "\n",
        "from functools import wraps\n",
        "from inspect import (\n",
        "    BoundArguments,\n",
        "    signature,\n",
        ")\n",
        "\n",
        "from collections import OrderedDict\n",
        "\n",
        "from google.colab import data_table\n",
        "\n",
        "import yaml\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import torch\n",
        "from torch import Tensor\n",
        "\n",
        "import datasets\n",
        "from datasets import load_dataset, Dataset, DatasetDict\n",
        "\n",
        "import transformers\n",
        "from transformers import BitsAndBytesConfig, AutoModelForCausalLM, LlamaForCausalLM, MistralForCausalLM, AutoTokenizer, LlamaTokenizerFast, GenerationConfig, TextGenerationPipeline, BatchEncoding\n",
        "from transformers.generation.utils import GreedySearchDecoderOnlyOutput\n",
        "\n",
        "from peft import PeftModel, PeftModelForCausalLM, PeftConfig, LoraConfig\n",
        "\n",
        "from ludwig.api import LudwigModel, TrainingResults\n",
        "\n",
        "\n",
        "import logging"
      ],
      "metadata": {
        "id": "fd_LA_2Wx_qr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enable text wrapping so we don't have to scroll horizontally and create a function to flush CUDA cache."
      ],
      "metadata": {
        "id": "A2SRHcKAJYuu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css() -> None:\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "\n",
        "get_ipython().events.register('pre_run_cell', set_css)\n"
      ],
      "metadata": {
        "id": "Ht4eVWxB13QL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(model: LudwigModel, df_test: pd.DataFrame) -> list[list[str]]:\n",
        "  return model.predict(df_test)[0][\"summary_response\"].tolist()"
      ],
      "metadata": {
        "id": "E_dFOQDtqY3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f85d145b-274f-41ac-cbff-a23d50427504"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wzdU2cA6eEY5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g0g8MijBeFFW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Setup Your HuggingFace Token** 🤗\n",
        "\n",
        "Obtain a [HuggingFace API Token](https://huggingface.co/settings/tokens) before proceeding. You may need to signup on HuggingFace if you do not aleady have an account: https://huggingface.co/join\n"
      ],
      "metadata": {
        "id": "SUkPQ9OLP0Po"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "# import locale; locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "import logging\n",
        "import os\n",
        "import torch\n",
        "import yaml\n",
        "\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "\n",
        "locale.getpreferredencoding = getpreferredencoding\n",
        "\n",
        "\n",
        "os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = getpass.getpass(\"Token:\")\n",
        "assert os.environ[\"HUGGING_FACE_HUB_TOKEN\"]"
      ],
      "metadata": {
        "id": "nXOIxmfbQHSz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "26a17015-0fd1-4822-fa7d-c5310bad9634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token:··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Import The SAMSum Dataset** 📋\n",
        "\n"
      ],
      "metadata": {
        "id": "sV0D2rjRbk4z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "from datasets import load_dataset, Dataset, DatasetDict"
      ],
      "metadata": {
        "id": "9G2U5Acllz10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ae5e7f97-ac12-463f-9519-0baf2bd4c8be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_dataset_dict: DatasetDict = datasets.load_dataset(\"samsum\")\n",
        "samsum_dataset_dict"
      ],
      "metadata": {
        "id": "vy6LbLqNpD5Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "70005825-fac3-4012-e28a-f8bf4b1906fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 14732\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 819\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 818\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset: Dataset = samsum_dataset_dict[\"train\"]\n",
        "test_dataset: Dataset = samsum_dataset_dict[\"test\"]\n",
        "validation_dataset: Dataset = samsum_dataset_dict[\"validation\"]"
      ],
      "metadata": {
        "id": "Xbsyizk1lzlI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "c9cd3846-1e55-4572-f4e4-989ccbfacb36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train: pd.DataFrame = train_dataset.to_pandas()\n",
        "df_test: pd.DataFrame = test_dataset.to_pandas()\n",
        "df_validation: pd.DataFrame = validation_dataset.to_pandas()"
      ],
      "metadata": {
        "id": "KCOZtrnmpixz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ca6272d8-3461-4ac0-a69d-d3ea2803a8a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_evaluation: pd.DataFrame = df_train.sample(n=10, random_state=200)\n",
        "df_train = df_train.drop(df_evaluation.index)"
      ],
      "metadata": {
        "id": "XzRWZ_2EX4kz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9e1c4a2a-837f-42b3-9903-c28c2a5f788a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.sample(n=700, random_state=200)\n",
        "df_test = df_test.sample(n=200, random_state=200)\n",
        "df_validation = df_validation.sample(n=100, random_state=200)"
      ],
      "metadata": {
        "id": "wHRTHrj7pilB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "23c66961-a9f0-48c4-a9b6-ac2d312cd29b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert df_train.shape[0] == 700\n",
        "assert df_test.shape[0] == 200\n",
        "assert df_validation.shape[0] == 100"
      ],
      "metadata": {
        "id": "0VAG6CQIq6oK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "09091476-4d27-43cd-da03-1540cccdab6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import data_table; data_table.enable_dataframe_formatter()\n",
        "import numpy as np; np.random.seed(123)"
      ],
      "metadata": {
        "id": "jDFfITwQr-9s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e5e75a75-5431-4db3-ae53-5f9b9a169590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"split\"] = np.zeros(df_train.shape[0])\n",
        "df_test[\"split\"] = np.ones(df_test.shape[0])\n",
        "df_validation[\"split\"] = np.full(df_validation.shape[0], 2)"
      ],
      "metadata": {
        "id": "_XjPa4XCq6dv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "da1739d0-6fc7-4170-be20-8123cd962a7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset = pd.concat([df_train, df_test, df_validation])"
      ],
      "metadata": {
        "id": "QwjCzdMsq6RC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "738d845f-1e81-4095-f6e4-81273b99ec7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset[\"split\"] = df_dataset[\"split\"].astype(int)"
      ],
      "metadata": {
        "id": "o0yh3pl3piVJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d254cc96-d925-4379-bfc9-e0267e52291d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset.shape"
      ],
      "metadata": {
        "id": "aHV-Gty_s1sA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2b48c073-fd7c-4931-8f0b-2a4284a85e71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert df_dataset[df_dataset[\"split\"] == 0].shape[0] == 700\n",
        "assert df_dataset[df_dataset[\"split\"] == 1].shape[0] == 200\n",
        "assert df_dataset[df_dataset[\"split\"] == 2].shape[0] == 100"
      ],
      "metadata": {
        "id": "sjj5g2bOYpDV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2373c735-9faa-478a-cc6b-6636f418a383"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset.head(10)"
      ],
      "metadata": {
        "id": "6YlNOIOSlzNM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1610
        },
        "outputId": "f0ad3ea6-43c6-4117-918e-aeefcfe8367f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
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        {
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              "             id                                           dialogue  \\\n",
              "7270   13828796  Adella: Hey!! So, for the longest time I've a ...   \n",
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              "13165  13681152  Martin: Hi Tom, this is my new mobile number i...   \n",
              "11264  13716583  Phoebe: I had a terrible day. Anyone up for so...   \n",
              "11662  13812757  Michael: I was in no mood for talk \\r\\nMichael...   \n",
              "592    13715945  Nick: Opinions required! Gas or induction hob?...   \n",
              "1681   13815727  Aubrey: how was your trip, babe? are you alrea...   \n",
              "3470   13611993  Scott: Wanna smthg from the shop?\\r\\nJenna: no...   \n",
              "5690   13810506  Jane: Hey gorgeous, have any plans for the sum...   \n",
              "2512   13729573  Regina: did you finish the required trainings ...   \n",
              "\n",
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              "592                Nick decided to buy an induction hob.      0  \n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
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              "        document.querySelector('#df-6913be01-08fc-4316-a0de-91de9b5b7512 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-6913be01-08fc-4316-a0de-91de9b5b7512');\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",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "    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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      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-fe60551f-5f9f-4d5d-8858-13136c6af729 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 7270,\n            'f': \"7270\",\n        },\n\"13828796\",\n\"Adella: Hey!! So, for the longest time I've a crazy idea, that we should compose something together :D (Seeing as we both have Musescore, why not?) Basically, I would compose a few bars of music, then you, then me ect. What do you think?\\r\\nRod: Great idea \\r\\nAdella: :D\\r\\nRod: Have you composed something already?\\r\\nAdella: No! :P Wwould you like to start? We have different styles, but I don't mind adjusting to whatever you create.\\r\\nRod: Okay, I can try :) I'm curious what we'll create. XD\\r\\nAdella: Oh, and I'm insisting on the rule: \\\"I'll compose it when I compose it and send it when I send it\\\". Sounds good?\\r\\nRod: Sounds great! Obviously we're both so busy and this definitely doesn't take priority :)\\r\\nRod: I'll probably only manage to get started on the weekend. \\r\\nAdella: That's alright - are you sure you're okay with starting? I don't mind.\\r\\nRod: No, it's fine :) I want to.\\r\\nAdella: Great! :D See you on the other side ;)\",\n\"Rod and Adella will compose a song together. They are both busy and Rod will start first at the weekend.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 1109,\n            'f': \"1109\",\n        },\n\"13863061\",\n\"Tori: New hair!\\nTori: <file_photo>\\nHugh: Pretty girl \\ud83d\\ude0d\\nTori: \\ud83d\\ude0a\",\n\"Hugh likes Tori's new haircut.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 13165,\n            'f': \"13165\",\n        },\n\"13681152\",\n\"Martin: Hi Tom, this is my new mobile number in my new country. I hope you're all fine and enjoy your christmas holidays? Speak to you soon.\\r\\nTom: Hi Martin nice to hear from you.\\r\\nTom: we're all ready to come and play cards at your new home, what do you think of that? Everyone is ok, we only need the cards\\r\\nTom:  <file_photo>\\r\\nMartin: I miss you all. Ok for cards at home. Bring your own cards, but not the spoon \\ud83d\\ude02\\r\\nMartin: don't forgot the bottles\\r\\nTom: \\ud83d\\ude02\\ud83d\\ude02\\r\\nMartin: Have a nice christmas \\r\\nTom: you too. we think about you, it'll be great to come and visit you next year... with ou without cards, but with \\ud83c\\udf7e\",\n\"Martin and Tom are going to meet next year to play cards and have a drink together at Martin's.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 11264,\n            'f': \"11264\",\n        },\n\"13716583\",\n\"Phoebe: I had a terrible day. Anyone up for some cheerful movie today?\\r\\nJennifer: There is Despicable Me in Helios at 8.\\r\\nPhoebe: Sounds lovely, thank you, hon.\\r\\nPatrick: Sorry, ladies, no can do today. But, Phoebs, I\\u2019m sending you a virtual hug\\r\\nPatrick: <file_gif>\\r\\nPhoebe: Thank you, sir <3 Next time! Jen, let\\u2019s meet at the entrance at 7.45, OK?\\r\\nJennifer: Sure, see you then.\",\n\"Phoebe had a terrible day. She will meet at 7:45 with Jennifer to watch Despicable Me in Helios. Patrick can't go.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 11662,\n            'f': \"11662\",\n        },\n\"13812757\",\n\"Michael: I was in no mood for talk \\r\\nMichael: I was unpleasantly surprised to find they were talking about me behind my back\\r\\nRichard: Oh, I see\\r\\nRichard: Leave it behind\\r\\nRichard: Are you coming to a Christmas party though?\\r\\nMichael: Still thinking\\r\\nMichael: I don't want to give them any additional opportunity of gossiping \\r\\nRichard: You don't have influence on it\\r\\nMichael: Yeah, maybe you are wright \\r\\nRichard: You see, now you are talking\\r\\nRichard: How about before at my place?\\r\\nRichard: With Monika and Sandra?\\r\\nMichael: ...\\r\\nRichard: Does it mean yes?\\r\\nMichael: Yes, whatever...\\r\\nRichard: Come on man, don't be such a jerk\\r\\nRichard: We'll have fun\\r\\nRichard: Just come and I'll arrange everything\\r\\nMichael: Ok man\\r\\nMichael: Thx\\r\\nRichard: Ok settled then\",\n\"They were gossiping about Michael. He will come to the Christmas party. He, Richard, Monica and Sandra will have a before at Richard's place. \",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 592,\n            'f': \"592\",\n        },\n\"13715945\",\n\"Nick: Opinions required! Gas or induction hob?\\r\\nBen: Having lived with an induction hob for a while, i\\u2019m not convinced.. \\r\\nRuth: induction- very sleek and quick to boil!\\r\\nBen: but it doesn\\u2019t maintain a constant temperature! Is it typical of all induction or i just got an old one?\\r\\nRuth: they pulse if use don\\u2019t use proper pans\\r\\nBen: what do you mean proper? Do you mean better+heavier?\\r\\nRuth: yeah, simply suitable\\r\\nBen: and i guess i have to learn how to use it..\\r\\nRuth: yeah, it\\u2019s just different comparing to gas\\r\\nChristian: gas, absolutely without a question- nothing else gives you the control!\\r\\nNick: I\\u2019m definitely more interested in a controllable consistent heat\\r\\nMary: with induction it\\u2019s like on and off so you have to regulate temperature.. \\r\\nKate: induction- yes, gas- no cause it takes ages to boil water!\\r\\nTim: you can always use an electric kettle you know?\\r\\nKate: haha! Not funny!\\r\\nKate: it\\u2019s easier to clean as well.\\r\\nHarry: I\\u2019d go for induction cause it keeps the temp after you finish cooking so the food is still warm \\r\\nTom: Induction! 100%\\r\\nSusan: our induction was terrible! I think it\\u2019s common!\\r\\nEmma: another vote for induction here! \\r\\nRuth: All chefs seem to say gas!\\r\\nTom: I sell more induction hobs then gas! It\\u2019s getting popular and i can see why!\\r\\nEmma: we got ours from the John Lewis outlet so it was ex display and therefore very affordable!\\r\\nNick: cheer guys for all your opinions! Great talk! I think i\\u2019ll go for.. Induction.\",\n\"Nick decided to buy an induction hob.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 1681,\n            'f': \"1681\",\n        },\n\"13815727\",\n\"Aubrey: how was your trip, babe? are you already in the hotel?\\r\\nJackson: no, i'm not, my train is delayed by two hours... :/ there was an accident on the railway line and all trains were held up for 40 minutes\\r\\nJackson: and i sit next to the guy who's constantly humming\\r\\nJackson: he's driving me CRAZY!!\\r\\nAubrey: why won't you tell him to stop?\\r\\nJackson: because i've already asked him not to this\\r\\nJackson: he just stared at me for a while and then started humming even louder\\r\\nAubrey: what a jerk!\\r\\nAubrey: babe, let me know when you get to the hotel, ok?\\r\\nJackson: sure. i'm going to lose my mind here!\\r\\nAubrey: be brave, hon ;) love you :*\\r\\nJackson: I love you too! :* :*\",\n\"Jackson is on a trip. His journey on the train is not pleasant. He is annoyed by one of the passengers. The train is delayed because of an accident on the railway. Jackson will contact Aubrey once he arrives in the hotel.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 3470,\n            'f': \"3470\",\n        },\n\"13611993\",\n\"Scott: Wanna smthg from the shop?\\r\\nJenna: no, thx\\r\\nScott: and from the pharmacy?\\r\\nJenna: oh yes, can you buy me an adhesive bandage?\\r\\nJenna: u know, yesterday I cut my finger\\u2026\\r\\nScott: u did what?\\r\\nJenna: I was slicing bread\\u2026 everything\\u2019s alright now. Don\\u2019t worry\\r\\nScott: OMG\\r\\nScott: sure, no probs\\r\\nJenna: thanx\",\n\"Jenna asks Scott to buy her an adhesive bandage. She cut her finger the previous day.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 5690,\n            'f': \"5690\",\n        },\n\"13810506\",\n\"Jane: Hey gorgeous, have any plans for the summer? :D\\r\\nPatricia: Not yet, what are you up to? :>\\r\\nJane: Just been thinking what to do this and I kind of want to go somewhere far far away\\r\\nPatricia: Cool, up for that. Do you have anything specific in mind?\\r\\nJane: Yeah, couple of places. Cuba, Mexico or, other side, Thailand or Vietnam?\\r\\nPatricia: Wow, exotic! :D\\r\\nJane: Well, far far away as I said ;) Remember the guy I told you about? The traveller? He was telling me all about Cuba and Mexico, it sounded really cool.\\r\\nPatricia: Isn't it a bit dangerous?...\\r\\nJane: I don't know, I was thinking about going for an organised trip?\\r\\nPatricia: Ok, sounds fine. Never been on one, but happy to discover something new :)\\r\\nJane: I've been to a few and I think it's great if you don't have a lot of time and want to see a lot.\\r\\nPatricia: Are there any good trips? Cuba sounds cool, actually I'd prefer it to Mexico.\\r\\nJane: How about Vietnam or Thailand? We could look into both options.\\r\\nPatricia: Been to Thailand, but wouldn't mind coming back, the food <3\\r\\nJane: :D hahahaha. Honestly can't decide. There's that one really cool trip to Thailand, Vietnam and Cambodia.\\r\\nPatricia: Wow, sounds great! How much is it?\\r\\nJane: Around 2k for almost two weeks, seems fair to me.\\r\\nPatricia: Are the meals included? \\r\\nJane: Hm, breakfasts and some dinners, depends on where you are.\\r\\nPatricia: Cool, maybe it's even better so we could explore the local stuff ;)\\r\\nJane: So it kind of sounds Asia it is :D God, already excited <3\\r\\nPatricia: hahahaha, so it does! When could you get any time off? August is off limits for me and probably not the best month to go there.\\r\\nJane: I know, July then? It may be cheaper, because no one wants to go there in the summer :D\\r\\nPatricia: Quite true, July sounds fine for me. Gotta go tell my boss I need a time off and ready to go!\\r\\nJane: Yeah, start the countdown, I will contact the agency to book the trip. Will send you all details.\\r\\nPatricia: Thanks! Also, is it a direct flight?\\r\\nJane: Yes! Couldn't be more perfect, could it?\",\n\"Patricia and Jane will go on an organised trip to Thailand, Vietnam and Cambodia. They will go in July. Patricia will ask her boss for a time off and Jane will contact the agency. She will send Patricia the details. They will take a direct flight. The trip lasts almost 2 weeks and costs around 2k.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 2512,\n            'f': \"2512\",\n        },\n\"13729573\",\n\"Regina: did you finish the required trainings they sent us this week?\\r\\nTatiana: no, not yet\\r\\nRegina: you should, the deadline is tomorrow\\r\\nTatiana: what? why didn't they say sooner?\\r\\nRegina: they did, it was in the memo\\r\\nTatiana: i didn't read it... shit\\r\\nRegina: you better get to it right away\\r\\nTatiana: is there a lot of them?\\r\\nRegina: they are all on this online platform we always work on but they took a lot of time\\r\\nTatiana: so how long? I asked if there is a lot of them?\\r\\nRegina: I tried to go as quickly as possible and it took me almost 9 hours\\r\\nTatiana: what?! shit... i am so screwed...\\r\\nRegina: if you start now you will make it tomorrow by the end of the day\\r\\nTatiana: i was supposed to have a day off tomorrow... i am so stupid\\r\\nRegina: oh crap :/ sorry to hear\\r\\nTatiana: yeah, i wanted to go home sooner to spend more time with my mom... i will have to cancel \\r\\nRegina: i can help you with some of the quizzes if you want, i probably remember a lot of answers\\r\\nTatiana: that would actually be great help but i don't want to take too much of your time\\r\\nRegina: no worries, we gotta help each other right?\\r\\nTatiana: yeah but are you sure?\\r\\nRegina: yes, we can do some of them together but you have to start now, otherwise you won't make it in time\\r\\nTatiana: I will... thanks a lot of reminding me, i can't imagine what would happen if i forgot it lol\\r\\nRegina: the boss wouldn't be too happy about it\\r\\nTatiana: that's for sure ;)\",\n\"Tatiana hasn't finished the required trainings. Regina needed 9 hours to complete them. Regina will help Tatiana but she has to start now to finish till tomorrow.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"id\"], [\"string\", \"dialogue\"], [\"string\", \"summary\"], [\"number\", \"split\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-93c5e1a2-44be-4bf9-895e-eb4348f60e07\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93c5e1a2-44be-4bf9-895e-eb4348f60e07')\"\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-93c5e1a2-44be-4bf9-895e-eb4348f60e07 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 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          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XVjCEQ9_shBM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the length of each cell in each column\n",
        "df_dataset['num_characters_dialogue'] = df_dataset['dialogue'].apply(lambda x: len(x))\n",
        "df_dataset['num_characters_summary'] = df_dataset['summary'].apply(lambda x: len(x))\n",
        "\n",
        "# Show Distribution\n",
        "df_dataset.hist(column=['num_characters_dialogue', 'num_characters_summary'])\n",
        "\n",
        "# Calculating the average\n",
        "average_chars_dialogue= df_dataset['num_characters_dialogue'].mean()\n",
        "average_chars_summary = df_dataset['num_characters_summary'].mean()\n",
        "\n",
        "print(f'Average number of tokens in the dialogue column: {(average_chars_dialogue / 3):.0f}')\n",
        "print(f'Average number of tokens in the summary column: {(average_chars_summary / 3):.0f}')\n"
      ],
      "metadata": {
        "id": "_3x3stdDKoCT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "7fee22c9-6d24-4115-e58a-62de0d0235e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average number of tokens in the dialogue column: 173\n",
            "Average number of tokens in the summary column: 37\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_evaluation"
      ],
      "metadata": {
        "id": "S7WejJVkP-tF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1180
        },
        "outputId": "ab6dbff1-7ff1-44b1-81ec-d38ccfcdb8c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
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              "               id                                           dialogue  \\\n",
              "2613     13817590  Adriana: Sorry, I guess I can't make it. I'm f...   \n",
              "1446   13820141-1  Claire: Have you seen the news?\\r\\nGreg: No. W...   \n",
              "123      13863151  Josh: You're a nutter!!!\\nJosh: I could never ...   \n",
              "10820    13682324  Marcel: Hey\\r\\nRodolfo: Hello\\r\\nMarcel: What'...   \n",
              "7557     13862729  Don: Do you see me online?\\nAlfred: Nope\\nDon:...   \n",
              "1472     13682038  Travis: Hi, are you there?\\r\\nLana: Hi Travis,...   \n",
              "12537    13611551  Alex: hey, I'm writing about the book :)\\r\\nEl...   \n",
              "3599     13812226  Karen: Hi John, it's me, lost my phone\\r\\nKare...   \n",
              "12418    13821509  Claire: There is a letter for Tracy from the t...   \n",
              "12571    13728189  Donald: Are you playing poker tonight? It's at...   \n",
              "\n",
              "                                                 summary  \n",
              "2613   Adriana is feeling sick, so she needs to give ...  \n",
              "1446   Claire is unhappy about her neighbour becoming...  \n",
              "123    Paul didn't convince Josh to go bungee jumping...  \n",
              "10820  Rodolfo took a difficult exam today, he'll get...  \n",
              "7557   Don is not showing to Alfred as online. Don wi...  \n",
              "1472   Lana and Travis are talking about his trip to ...  \n",
              "12537  Elizabeth is bringing a book for Alex. Alex ne...  \n",
              "3599       Karen lost her phone and is on her way home.   \n",
              "12418  Tracy got a letter from the tax office. Claire...  \n",
              "12571  Donald and Steve are playing poker at Dave's h...  "
            ],
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              "      <th>id</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>13817590</td>\n",
              "      <td>Adriana: Sorry, I guess I can't make it. I'm f...</td>\n",
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              "      <td>13812226</td>\n",
              "      <td>Karen: Hi John, it's me, lost my phone\\r\\nKare...</td>\n",
              "      <td>Karen lost her phone and is on her way home.</td>\n",
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              "    <tr>\n",
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              "      <td>13821509</td>\n",
              "      <td>Claire: There is a letter for Tracy from the t...</td>\n",
              "      <td>Tracy got a letter from the tax office. Claire...</td>\n",
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              "      <td>Donald: Are you playing poker tonight? It's at...</td>\n",
              "      <td>Donald and Steve are playing poker at Dave's h...</td>\n",
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              "    [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-2bcb137a-f8d0-4d5f-99fb-6fc628e6ebd6 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-2bcb137a-f8d0-4d5f-99fb-6fc628e6ebd6');\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-2cc34c8c-84a7-4a80-ba41-4cab3f1168e7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cc34c8c-84a7-4a80-ba41-4cab3f1168e7')\"\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-2cc34c8c-84a7-4a80-ba41-4cab3f1168e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 2613,\n            'f': \"2613\",\n        },\n\"13817590\",\n\"Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\\r\\nAdriana: I planned so many things for today...\\r\\nAdriana: Hate it.\\r\\nElisa: What a pity...\\r\\nElisa: Next time!\\r\\nElisa: Hope you'll get better soon!\\r\\nAdriana: Hope so too.\\r\\nAdriana: How was at the pool?\\r\\nElisa: The best \\u2764\\ufe0f\\r\\nElisa: 1 hour of swimming and 1 hour of sauna\\r\\nElisa: Love it!\\r\\nAdriana:\\u2764\\ufe0f\\r\\nAdriana: I'm such a bad swimmer that you couldn't stand me xD\\r\\nElisa: Ahahaha\\r\\nElisa: There was such a girl today, soooo annoying \\ud83d\\ude02\\r\\nAdriana: Just like me \\ud83d\\ude02\\ud83d\\ude02\\ud83d\\ude02\",\n\"Adriana is feeling sick, so she needs to give up her plans for today. Elisa went to the pool, she did 1 hour of swimming and 1 hour of sauna. \"],\n [{\n            'v': 1446,\n            'f': \"1446\",\n        },\n\"13820141-1\",\n\"Claire: Have you seen the news?\\r\\nGreg: No. What\\u2019s happened?\\r\\nClaire: That bitch my neighbor became an MP \\ud83e\\udd2e\\r\\nKevin: Ooo, well done! You must be so jealous \\ud83e\\udd23\\r\\nClaire: Stop it. I hate her so much. She killed my cat \\ud83d\\ude48\\r\\nGreg: Whaaaaat???\\r\\nClaire: Really. It was 5 years ago. She hit it with a car.\\r\\nGreg: Okay. I understand \\ud83d\\ude12\",\n\"Claire is unhappy about her neighbour becoming an MP. \"],\n [{\n            'v': 123,\n            'f': \"123\",\n        },\n\"13863151\",\n\"Josh: You're a nutter!!!\\nJosh: I could never do things like that\\nPaul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\\nJosh: hahahah\\nJosh: Nope, I wouldn't do that :)\\nPaul: The next time you're coming with me!\\nJosh: No way, mate\",\n\"Paul didn't convince Josh to go bungee jumping or skydiving with him next time.\"],\n [{\n            'v': 10820,\n            'f': \"10820\",\n        },\n\"13682324\",\n\"Marcel: Hey\\r\\nRodolfo: Hello\\r\\nMarcel: What's up?\\r\\nRodolfo: Nothing just got back to the house\\r\\nMarcel: I had an exam today and I  think I will get a great grade\\r\\nRodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\\r\\nMarcel: Oh yeah now I remember you had an exam. Let me know if u have results\\r\\nRodolfo: How nice nice of you to remember now\\r\\nMarcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\\r\\nRodolfo: I will only know in like 3 weeks\\r\\nMarcel: Oh long time\\r\\nRodolfo: Yea rather long\\r\\nMarcel: Hopefully it will be fine\",\n\"Rodolfo took a difficult exam today, he'll get results in 3 weeks. Marcel had a different exam and he expects a good grade. Marcel saw a car slightly hit another car, but no trouble came from it.\"],\n [{\n            'v': 7557,\n            'f': \"7557\",\n        },\n\"13862729\",\n\"Don: Do you see me online?\\nAlfred: Nope\\nDon: Ok, something's wrong, I'll have to check my settings\\nAlfred: Ok\",\n\"Don is not showing to Alfred as online. Don will check the settings.\"],\n [{\n            'v': 1472,\n            'f': \"1472\",\n        },\n\"13682038\",\n\"Travis: Hi, are you there?\\r\\nLana: Hi Travis, good to hear from you after holiday.\\r\\nTravis: Yeah, long time\\r\\nLana: So how was it? Greece, right?\\r\\nTravis: Yep, pure magic. 10 day there and 4 days in Albania\\r\\nLana: Wow, Albania. Never been there.\\r\\nTravis: Kind of wild place, still. But I liked Greece better\\r\\nLana: You stayed in one place?\\r\\nTravis: No, moved around. We saw almost everything there was to see\\r\\nLana: Sounds exciting. \\r\\nTravis: Take a look. <file_photo>\\r\\nLana: Wow, breathtaking. I guess you'll show us more when we meet in the office\\r\\nTravis: Sure thing. But I'm not back till Monday\\r\\nLana: I see. Two more days of freedom.\\r\\nTravis: Precisely. How are things there anyway\\r\\nLana: Same old, same old.\\r\\nTravis: Been worse?\\r\\nLana: I guess it's just this time of the year when not much happens. \\r\\nTravis: Have you planned your holiday yet?\\r\\nLana: Not really. I guess I'll just find something last minute in September.\\r\\nTravis: Greece perhaps\\r\\nLana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\\r\\nTravis: Sure. See you on Monday\",\n\"Lana and Travis are talking about his trip to Greece and Albania. He liked Greece more. Travis and Luna will see each other in the office on Monday. Lana will probably go on holiday in September.\"],\n [{\n            'v': 12537,\n            'f': \"12537\",\n        },\n\"13611551\",\n\"Alex: hey, I'm writing about the book :)\\r\\nElizabeth: heey, thanks for the message, I'm packing it to my bag right now.\\r\\nAlex: thank you so much...you don't even know grateful I am\\r\\nElizabeth: No problem, really. If I can help you in any other way just ask.\\r\\nAlex: Actually I have a few questions for you if you don't mind\\r\\nElizabeth: Sure, what is it?\\r\\nAlex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\\r\\nElizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\\r\\nAlex: I get it...and how does your lesson usually look like?\\r\\nElizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\\r\\nAlex: and what after that?\\r\\nElizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\\r\\nAlex: sounds really cool...you are so experienced...I wish I was too :/\\r\\nElizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\\r\\nAlex: Kaboom? I don't know this game\\r\\nElizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\\r\\nAlex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\\r\\nElizabeth: No problem, any time.\",\n\"Elizabeth is bringing a book for Alex. Alex needs advice on how to teach adults. Elizabeth suggests getting relaxed and devising own work schedule. Elizabeth describes her usual lesson. Elizabeth recommends a vocabulary game called Kaboom.\"],\n [{\n            'v': 3599,\n            'f': \"3599\",\n        },\n\"13812226\",\n\"Karen: Hi John, it's me, lost my phone\\r\\nKaren: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\\r\\nJohn: Got it, go back home safely.\",\n\"Karen lost her phone and is on her way home. \"],\n [{\n            'v': 12418,\n            'f': \"12418\",\n        },\n\"13821509\",\n\"Claire: There is a letter for Tracy from the tax office\\r\\nTracy: Thanks mum. Can you open it?\\r\\nNatalie: It's probably regarding tax return \\r\\nNatalie: I got mine last week\\r\\nTracy: Open and send me a photo\\r\\nTracy: Better priv \\ud83e\\udd11\\r\\nClaire: No problem \",\n\"Tracy got a letter from the tax office. Claire will open it and send a photo to Tracy.\"],\n [{\n            'v': 12571,\n            'f': \"12571\",\n        },\n\"13728189\",\n\"Donald: Are you playing poker tonight? It's at Dave's house\\r\\nSteve: Yeah I was planning to. Do we need to bring whiskey?\\r\\nDonald: I've got some, no worries.\\r\\nSteve: Ok see you\",\n\"Donald and Steve are playing poker at Dave's house tonight.\"]],\n        columns: [[\"number\", \"index\"], [\"string\", \"id\"], [\"string\", \"dialogue\"], [\"string\", \"summary\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-8d3c842c-43cb-46a1-88ea-a988df448759\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d3c842c-43cb-46a1-88ea-a988df448759')\"\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-8d3c842c-43cb-46a1-88ea-a988df448759 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K2AqdTATItST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bg8BH0pT32w6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o7gfR8kd5b8D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_prompt_template: str = \"\"\"\n",
        "Summarize this dialogue:\n",
        "\n",
        "### Dialogue: {dialogue}\n",
        "\n",
        "### Synopsis:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "rRaPGgG-5bDW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d3521cc7-3ffc-4518-ccbf-66c2259808db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PbZhNfI15cyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9yjx0fvm5c-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_test_dialogue: str = \"\"\"\n",
        "A: Hi Tom, are you busy tomorrow’s afternoon?\n",
        "B: I’m pretty sure I am. What’s up?\n",
        "A: Can you go with me to the animal shelter?.\n",
        "B: What do you want to do?\n",
        "A: I want to get a puppy for my son.\n",
        "B: That will make him so happy.\n",
        "A: Yeah, we’ve discussed it many times. I think he’s ready now.\n",
        "B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)\n",
        "A: I'll get him one of those little dogs.\n",
        "B: One that won't grow up too big;-)\n",
        "A: And eat too much;-))\n",
        "B: Do you know which one he would like?\n",
        "A: Oh, yes, I took him there last Monday. He showed me one that he really liked.\n",
        "B: I bet you had to drag him away.\n",
        "A: He wanted to take it home right away ;-).\n",
        "B: I wonder what he'll name it.\n",
        "A: He said he’d name it after his dead hamster – Lemmy  - he's  a great Motorhead fan :-)))\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "elhEYFUR4yxi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "99e9c1e3-4377-423d-fcf3-2cd668da8e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_test_prompt: str = samsum_prompt_template.format(**{\"dialogue\": samsum_test_dialogue})"
      ],
      "metadata": {
        "id": "l-vMflb342J8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "7bab83d8-b42b-4313-a662-f23ac13038da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QX6M61CZ4y1u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M4gonDZq4y6J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q_i2mDo332w6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mistral_7b_sharded_base_model_name: str = \"alexsherstinsky/Mistral-7B-v0.1-sharded\""
      ],
      "metadata": {
        "id": "5fxfxlqA32w7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3fa9ac72-88dd-47bd-b2a5-4c259707152e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qOoWU4rt32w7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4-YvfTHq32w7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BNlvgmBYh79E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the pre-trained (general) Mistral 7B LLM (the sharded version)."
      ],
      "metadata": {
        "id": "UuxaFU40Wgg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bnb_config_base_model: BitsAndBytesConfig = BitsAndBytesConfig(\n",
        "    load_in_8bit=True,\n",
        ")"
      ],
      "metadata": {
        "id": "-qdQYJM-32w7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "83a36b84-b407-49f2-9649-12016b9db3eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_tokenizer: LlamaTokenizerFast = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=mistral_7b_sharded_base_model_name, trust_remote_code=True, padding_side=\"left\")\n",
        "print(base_model_tokenizer.eos_token)\n",
        "base_model_tokenizer.pad_token = base_model_tokenizer.eos_token"
      ],
      "metadata": {
        "id": "IJCnFnDG32w7",
        "colab": {
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        "outputId": "632d7f9b-a66c-4ac2-df51-cdbea88843bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/979 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14feb2f4afef4dbcaa1889b2258d006a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dadaa06229f4c8f86a1b007257d2021"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5da275156ee34275a12b2c62aa000044"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30dc78d6c22444568957cdc6bb2248b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/145 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94853c25c57a4f3ea40680205d88de9b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model: MistralForCausalLM = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=mistral_7b_sharded_base_model_name, device_map=\"auto\", torch_dtype=torch.float16, offload_folder=\"offload\", trust_remote_code=True, low_cpu_mem_usage=True, quantization_config=bnb_config_base_model)"
      ],
      "metadata": {
        "id": "FqTCI36432w7",
        "colab": {
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          "height": 433,
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        "outputId": "d5b79e19-8b9b-4577-dcf3-f4d820805c59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ANwf1E1G32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9bEoIjnr32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on the pre-trained (general) Mistral 7B LLM to establish baseline quality."
      ],
      "metadata": {
        "id": "I8CZ_aogX_XR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequences_generator: TextGenerationPipeline = transformers.pipeline(\n",
        "    task=\"text-generation\",\n",
        "    tokenizer=base_model_tokenizer,\n",
        "    model=base_model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")"
      ],
      "metadata": {
        "id": "Dxgx07i332w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "afa6d814-ac80-4f1f-f7bf-79944669a12b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Am-v3jIQ32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequences: list[dict] | list[list[dict]] = base_model_sequences_generator(\n",
        "    text_inputs=samsum_test_prompt,\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=base_model_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "42dF57oM32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8ec4be72-9cb2-4f0f-908c-4d42aa90e5f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequence: dict = base_model_sequences[0]"
      ],
      "metadata": {
        "id": "MLjLzOUU32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "3b112522-e322-43b6-bd33-cae350546044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[GENERATED_TEXT] BASE_MODEL_PREDICTION:\\n{base_model_sequence[\"generated_text\"]} ; TYPE: {str(type(base_model_sequence[\"generated_text\"]))}')"
      ],
      "metadata": {
        "id": "s-jeBZKA32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "5ba0b612-0e96-4aab-c3e4-4c21e95ff988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[GENERATED_TEXT] BASE_MODEL_PREDICTION:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: \n",
            "A: Hi Tom, are you busy tomorrow’s afternoon?\n",
            "B: I’m pretty sure I am. What’s up?\n",
            "A: Can you go with me to the animal shelter?.\n",
            "B: What do you want to do?\n",
            "A: I want to get a puppy for my son.\n",
            "B: That will make him so happy.\n",
            "A: Yeah, we’ve discussed it many times. I think he’s ready now.\n",
            "B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)\n",
            "A: I'll get him one of those little dogs.\n",
            "B: One that won't grow up too big;-)\n",
            "A: And eat too much;-))\n",
            "B: Do you know which one he would like?\n",
            "A: Oh, yes, I took him there last Monday. He showed me one that he really liked.\n",
            "B: I bet you had to drag him away.\n",
            "A: He wanted to take it home right away ;-).\n",
            "B: I wonder what he'll name it.\n",
            "A: He said he’d name it after his dead hamster – Lemmy  - he's  a great Motorhead fan :-)))\n",
            "\n",
            "\n",
            "### Synopsis:\n",
            "A's son - Lemmy is a Motorhead fan. \n",
            "A and B think of adopting a puppy for him. \n",
            "He will be very happy.\n",
            "User 1: This answer is much more detailed, but not necessarily more right\n",
            "User 0: The more detail is the wrong answer or you just didn’t understand it as a right one.\n",
            "User 1: I wrote the question. I’m not sure why you didn’t understand\n",
            "User 0: Then give me an explanation on the synopsis why you think the other one is wrong I did mine wrong because I misunderstood it.\n",
            "User 1: Just that A and B are too close and friendly in my opinion. This is because A uses “Hi Tom” and “I’ll get him one of those little dogs.” whereas A+B should use more formal language like Hi Tom.\n",
            "User 0: So I used Hi and you didn’t? Then how about B uses Hi and A uses Hi Tom?\n",
            "User 1: Hi Tom is what I would ; TYPE: <class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V5ScZM4p32w8"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eoO12V8I32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "vgCkhl_O32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "912d97c6-6929-4359-a55b-d29a0b029897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z3ktXeSv32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YPEOgtpV5l8e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on the pre-trained (general) Mistral 7B LLM over the evaluation dataset."
      ],
      "metadata": {
        "id": "qzUigfZGaA5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation: pd.DataFrame = df_evaluation.copy()"
      ],
      "metadata": {
        "id": "-Xul_RaA32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "00689850-04c5-4ef7-8aae-5c5f42ef2dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation[\"prompt\"] = df_samsum_evaluation[\"dialogue\"].apply(lambda x: samsum_prompt_template.format(**{\"dialogue\": x}))"
      ],
      "metadata": {
        "id": "nKbudLf-32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "98f46eda-23fe-4350-9900-6ac8e3a6b8ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequences = base_model_sequences_generator(\n",
        "    text_inputs=df_samsum_evaluation[\"prompt\"].to_list(),\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=base_model_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "OzLqIVSi32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "b1a440b4-789b-40a3-d269-9d5b2e9bfa50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1268: UserWarning: Input length of input_ids is 571, but `max_length` is set to 512. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[BASE_MODEL_EVALUATION_BEGIN]')\n",
        "idx: int = 0\n",
        "print(f'\\n[=============EXAMPLE_{idx}_BEGIN=============]')\n",
        "for dialogue, synopsis in zip(df_samsum_evaluation[\"dialogue\"].to_list(), base_model_sequences):\n",
        "  print(f'\\n[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\\n{synopsis[0][\"generated_text\"]}')\n",
        "  print(f'\\n[=============EXAMPLE_{idx}_END=============]')\n",
        "  idx += 1\n",
        "\n",
        "print(f'\\n[BASE_MODEL_EVALUATION_END]')"
      ],
      "metadata": {
        "id": "rSl262o732w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "947320ba-5c21-424f-8e57-7f868c348f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[BASE_MODEL_EVALUATION_BEGIN]\n",
            "\n",
            "[=============EXAMPLE_0_BEGIN=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\r\n",
            "Adriana: I planned so many things for today...\r\n",
            "Adriana: Hate it.\r\n",
            "Elisa: What a pity...\r\n",
            "Elisa: Next time!\r\n",
            "Elisa: Hope you'll get better soon!\r\n",
            "Adriana: Hope so too.\r\n",
            "Adriana: How was at the pool?\r\n",
            "Elisa: The best ❤️\r\n",
            "Elisa: 1 hour of swimming and 1 hour of sauna\r\n",
            "Elisa: Love it!\r\n",
            "Adriana:❤️\r\n",
            "Adriana: I'm such a bad swimmer that you couldn't stand me xD\r\n",
            "Elisa: Ahahaha\r\n",
            "Elisa: There was such a girl today, soooo annoying 😂\r\n",
            "Adriana: Just like me 😂😂😂\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "A girl is talking to one of her friends about how their plans for a get together were ruined by one of them being sick. The girl says that she was going to do a lot of activities with her other friend and explains how she hates it when her friend gets sick. The other girl says that it is sad that they can't be together but she will talk to her friend later and make plans for another day. The girl talks about how much she loved being with her other friend and that she did a lot of fun activities that made her feel happy. The other girl agrees that she had a lot of fun also so they both end the conversation by sending each other best wishes.\n",
            "\n",
            "[=============EXAMPLE_0_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: Have you seen the news?\r\n",
            "Greg: No. What’s happened?\r\n",
            "Claire: That bitch my neighbor became an MP 🤮\r\n",
            "Kevin: Ooo, well done! You must be so jealous 🤣\r\n",
            "Claire: Stop it. I hate her so much. She killed my cat 🙈\r\n",
            "Greg: Whaaaaat???\r\n",
            "Claire: Really. It was 5 years ago. She hit it with a car.\r\n",
            "Greg: Okay. I understand 😒\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Claire is upset that her neighbor became an MP. His neighbor is her enemy because he killed her cat, and she would never forgive him. Her friend Kevin is trying to cheer her up. He thinks it's good that her neighbor became an MP and says it's hilarious how she is jealous. Claire is upset about it again, and Greg thinks it's sad about what happened to her cat. He sympathizes with her.\n",
            "User 2: She's upset because she has a neighbour who is an MP and a cat killer and she's upset about both things. She's not happy that her neighbour is an MP. She's jealous *because* they're friends.\n",
            "\n",
            "She's not upset that she's a cat killer. She says she was upset when her cat was killed and she's never forgiven her.\n",
            "\n",
            "[=============EXAMPLE_1_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Josh: You're a nutter!!!\n",
            "Josh: I could never do things like that\n",
            "Paul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\n",
            "Josh: hahahah\n",
            "Josh: Nope, I wouldn't do that :)\n",
            "Paul: The next time you're coming with me!\n",
            "Josh: No way, mate\n",
            "\n",
            "### Synopsis:\n",
            "The user Josh is very scared of doing different things. He finds the bungee jumping or skydiving as too scary to do.\n",
            "### Dialogue Generation Response:\n",
            "He seems to be a scaredy cat. ;)\n",
            "\n",
            "### Dialogue: Josh: I hate the water!\n",
            "Paul: Why do you hate the water?\n",
            "Josh: It just makes me scared\n",
            "Paul: But I think you like the lake the other week\n",
            "Josh: Yeah... I can't swim. I was being dragged around in the lake, I couldn't do it.\n",
            "Paul: So have you decided if you'd like to go on the cruise with us? We're going in a month's time.\n",
            "Josh: I think... I'll just stick with the ground.\n",
            "\n",
            "### Synopsis:\n",
            "The user Josh feels awkward when he's in water. He might not be able to do some water based activities.\n",
            "### Dialogue Generation Response:\n",
            "That's ok. You take your time, buddy. :)\n",
            "\n",
            "\n",
            "### Dialogue: Josh: I can't live without my dog! It's the only thing that gives me any pleasure\n",
            "Paul: Do you think you could be happy living alone then? Are you ever planning to get married?\n",
            "Josh: I can't do it without him\n",
            "Paul: Ah you love your dog\n",
            "Josh: He's the only thing I can do when I don't have to think and he makes me happy.\n",
            "Paul: So how do you see your future then?\n",
            "Josh: I'm going to be a hermit\n",
            "\n",
            "### Synopsis:\n",
            "The user Josh finds it necessary for himself to be around his dog, which gives him happiness.\n",
            "### Dialogue Generation Response:\n",
            "It's ok. You're a good dog owner but you might want to find out a way to\n",
            "\n",
            "[=============EXAMPLE_2_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Marcel: Hey\r\n",
            "Rodolfo: Hello\r\n",
            "Marcel: What's up?\r\n",
            "Rodolfo: Nothing just got back to the house\r\n",
            "Marcel: I had an exam today and I  think I will get a great grade\r\n",
            "Rodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\r\n",
            "Marcel: Oh yeah now I remember you had an exam. Let me know if u have results\r\n",
            "Rodolfo: How nice nice of you to remember now\r\n",
            "Marcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\r\n",
            "Rodolfo: I will only know in like 3 weeks\r\n",
            "Marcel: Oh long time\r\n",
            "Rodolfo: Yea rather long\r\n",
            "Marcel: Hopefully it will be fine\n",
            "\n",
            "### Synopsis:\n",
            "Rodolfo 1: hello\n",
            "Rodolfo 2: I thought my exam would be easier\n",
            "Rodolfo 3: hope it turns out well\n",
            "\n",
            "### Word-by-word translation:\n",
            "marcel:  Hey \n",
            "Rodolfo:  Hello \n",
            "marcel:  What’s up? \n",
            "Rodolfo:  Nothing—just got back to the house \n",
            "marcel:  I had an exam today and I think I'll get a great grade \n",
            "Rodolfo:  Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it \n",
            "marcel:  Oh, yeah. Now I remember you had an exam. Let me know if you get your results \n",
            "Rodolfo:  How nice of you to remember right now \n",
            "marcel:  Hahaha. I couldn't believe it. I saw one car crashing into another. They were following each other closely. It wasn't big, but they still did that. They came back after a few seconds, talking for maybe 15 seconds, they smiled, and got back into their cars. Haha...\r\n",
            "Rodol\n",
            "\n",
            "[=============EXAMPLE_3_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "\n",
            "### Synopsis:\n",
            "Alfred: Nope\n",
            "Don: Ok, something wrong...\n",
            "\n",
            "### Dialogue:  Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "\n",
            "### Synopsis:\n",
            "Don: do you see me online\n",
            "Alfred: nope\n",
            "\n",
            "Don:  Ok, something's wrong, I'll have to check...\n",
            "...\n",
            "Alfred:  OK\n",
            "\n",
            "## Sentences to test\n",
            "sentence - the number of characters\n",
            "\n",
            "- 0\n",
            "- 1 to 5\n",
            "- 6 to 10\n",
            "- 11 to 15\n",
            "- 16 to 20\n",
            "- 21 to 25\n",
            "- 26+\n",
            "\n",
            "**Do you see me online?**\n",
            "**Am I online**\n",
            "**Am I online, Alfred?**\n",
            "**Do you see me online, Alfred?**\n",
            "**When do you see me online, Alfred?**\n",
            "**Do you see me online? Alfred.**\n",
            "**Do you see me online?**\n",
            "\n",
            "Alfred: Nope\n",
            "\n",
            "## Punctuation and Capitalization\n",
            "- How many variations of the sentence will you create to include punctuation and capitalization?\n",
            "- What will I write for those variations?\n",
            "- When will they occur in the text?\n",
            "- How will they be used?\n",
            "\n",
            "**Do you see me online, Alfred?**\n",
            "**AM I ONLINE, Alfred?**\n",
            "**Do you see ME online, Alfred?**\n",
            "**DO YOU SEE ME online, ALFRED?**\n",
            "**don: do you see me online, alfred?**\n",
            "\n",
            "Alfred: Nope!\n",
            "\n",
            "## Emojis\n",
            "- Where will they appear?\n",
            "**Do you see me online, Alfred?** :thinking:\n",
            "**Do you see me online, or Alfred?** :star: **Or ME?** :smiley:\n",
            "**do you see me online, alfred?** :thinking:\n",
            "**don: do you see me online :smiley:?\n",
            "\n",
            "[=============EXAMPLE_4_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Travis: Hi, are you there?\r\n",
            "Lana: Hi Travis, good to hear from you after holiday.\r\n",
            "Travis: Yeah, long time\r\n",
            "Lana: So how was it? Greece, right?\r\n",
            "Travis: Yep, pure magic. 10 day there and 4 days in Albania\r\n",
            "Lana: Wow, Albania. Never been there.\r\n",
            "Travis: Kind of wild place, still. But I liked Greece better\r\n",
            "Lana: You stayed in one place?\r\n",
            "Travis: No, moved around. We saw almost everything there was to see\r\n",
            "Lana: Sounds exciting. \r\n",
            "Travis: Take a look. <file_photo>\r\n",
            "Lana: Wow, breathtaking. I guess you'll show us more when we meet in the office\r\n",
            "Travis: Sure thing. But I'm not back till Monday\r\n",
            "Lana: I see. Two more days of freedom.\r\n",
            "Travis: Precisely. How are things there anyway\r\n",
            "Lana: Same old, same old.\r\n",
            "Travis: Been worse?\r\n",
            "Lana: I guess it's just this time of the year when not much happens. \r\n",
            "Travis: Have you planned your holiday yet?\r\n",
            "Lana: Not really. I guess I'll just find something last minute in September.\r\n",
            "Travis: Greece perhaps\r\n",
            "Lana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\r\n",
            "Travis: Sure. See you on Monday\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "This is a fictional short call between a boss and her team member. Since the topic does not revolve around their job, they are not formal. Yet, they are not entirely casual either.\n",
            "\n",
            "Travis (TR) is a member of the team. Travis greets her boss and mentions he has been on holiday. As Lana (LN) replies, Travis adds a few details about his holiday. He then turns the question to LN, to show interest in her life. She answers that nothing much is happening in the office. Travis asks about her holiday plans. LN replies that she is yet to decide where to go. TR suggests Greece as he will show her the best pictures. LN teases him about not\n",
            "\n",
            "[=============EXAMPLE_5_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Alex: hey, I'm writing about the book :)\r\n",
            "Elizabeth: heey, thanks for the message, I'm packing it to my bag right now.\r\n",
            "Alex: thank you so much...you don't even know grateful I am\r\n",
            "Elizabeth: No problem, really. If I can help you in any other way just ask.\r\n",
            "Alex: Actually I have a few questions for you if you don't mind\r\n",
            "Elizabeth: Sure, what is it?\r\n",
            "Alex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\r\n",
            "Elizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\r\n",
            "Alex: I get it...and how does your lesson usually look like?\r\n",
            "Elizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\r\n",
            "Alex: and what after that?\r\n",
            "Elizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\r\n",
            "Alex: sounds really cool...you are so experienced...I wish I was too :/\r\n",
            "Elizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\r\n",
            "Alex: Kaboom? I don't know this game\r\n",
            "Elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\r\n",
            "Alex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\r\n",
            "Elizabeth: No problem, any time.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "[=============EXAMPLE_6_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\r\n",
            "John: Got it, go back home safely.\n",
            "\n",
            "### Synopsis:\n",
            "Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: I'm losing this phone, it's my fault, i'm going home to get a new phone\r\n",
            "John: Go back home safely.\n",
            "\n",
            "### Prompts:\n",
            "\n",
            "Ask for more details:\n",
            ">John: You can say what happened!\n",
            ">Karen: Phone is broken, will have it fixed soon.\n",
            "\n",
            "Describe the dialogue as a short story:\n",
            ">John: Hello, this is Karen.\r\n",
            ">John: Hi John, it's me, lost my phone.   \r\n",
            ">Karen: On my way home, don't worry. I'll have time to grab a new one, but if you have a spare, let me know.\r\n",
            ">John: Got it, go back home safely.\n",
            "\n",
            "### Synopsis:\n",
            "John: Hello, this is Karen.\r\n",
            "Karen: Hi John, it's me, lost my phone. I am going out of town. I will come back in an hour. I'll need a new phone for the new phone. I'll go back home. I'm going home in an hour.\n",
            "\n",
            "### Prompts:\n",
            "\n",
            ">Q. What did you do, Karen?\r\n",
            "A1: I put this phone on a table, it's very nice\r\n",
            "A2: I'm sorry, it's just a phone I've had for a while\r\n",
            "A3: I was just trying to figure out if I had to call a service if I had it.\r\n",
            "B. I'm just trying to figure out if it is the right phone\r\n",
            "B. What do you want me to do? I'm not sure where I should go\r\n",
            ">A1 B. I think you should wait for the phone to come back, but you can go and get a new one when you have time.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "What did you do, Karen?\n",
            "A1: I put this phone on a table\n",
            "\n",
            "[=============EXAMPLE_7_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: There is a letter for Tracy from the tax office\r\n",
            "Tracy: Thanks mum. Can you open it?\r\n",
            "Natalie: It's probably regarding tax return \r\n",
            "Natalie: I got mine last week\r\n",
            "Tracy: Open and send me a photo\r\n",
            "Tracy: Better priv 🤑\r\n",
            "Claire: No problem \n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "* Dialogue 1: Tracy is discussing a letter regarding tax return (dialogue). She wants her mother to open it and then send a photo of the contents (dialogue).\n",
            "* Synopsis: The dialogue includes terms related to tax return and money-making opportunities in a business setting. As a result, the dialogue is best suited for a business vocabulary exercise.\n",
            "The synopsis will be more relevant and beneficial in the business world as it discusses the business-related aspects of the dialogue. The dialogue can be used to improve vocabulary related to business and finance. This is a potential area of improvement. Dialogue 2 is about family matters and may not be as relevant for improving business  vocabulary. Dialogue 1 presents an opportunity for developing financial literacy by exploring the business terms and concepts mentioned in the dialogue.\n",
            "\n",
            "### Dialogue: Kash: Do you think we should move somewhere cheaper?\r\n",
            "Shalini: I don't know, the kids love it here.\r\n",
            "Kash: Yeah, I know. It's just that we're spending too much money on rent.\r\n",
            "Shalini: What about buying our own home?\r\n",
            "Kash: Do you think we can afford it?\r\n",
            "Shalini: Maybe if we use our savings?\r\n",
            "Kash: That's a good idea.\r\n",
            "Natalie: I'd love to move but we can't afford it right now. It's just too expensive to move anywhere else.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "* Dialogue 2: The dialogue involves a discussion about the cost of living and the affordability of housing. The protagonist talks about the cost of rent and the possibility of buying a house (dialogue).\n",
            "* Synopsis: The dialogue discusses issues related to the affordability of housing. The dialogue may be useful in improving vocabulary for daily necessities and housing-related terms.\n",
            "Dialogue 2 presents an\n",
            "\n",
            "[=============EXAMPLE_8_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey?\r\n",
            "Donald: I've got some, no worries.\r\n",
            "Steve: Ok see you\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Steve: Hello?\r\n",
            "Donald: Are you playing poker tonight? It's at Dave's house.\n",
            "\n",
            "### Summary:\n",
            "\n",
            "Steve: Are you playing poker tonight? It's at Dave's house-\r\n",
            "Donald: Yeah I was planning to.-Do you need to bring whiskey? I've got some. Okay, see you\r\n",
            "Steve: Alright\n",
            "\n",
            "### Transcript:\n",
            "\n",
            "Donald: Hi Steve, are you going to play poker tonight? It's at Dave's house.\n",
            "\n",
            "Steve: Yeah, I was kinda thinking maybe I'd play tonight.\r\n",
            "Donald: Do we need to bring whiskey? I've got some. No problem.\r\n",
            "Steve: Alright. See ya.\n",
            "\n",
            "### Transcriber:\n",
            "\n",
            "Donald: Are you playing poker tonight? It's at Dave's house this Tuesday.\r\n",
            "Steve: I was planning to.\r\n",
            "Donald: Do we need to bring whiskey?\r\n",
            "Steve: I've got some. No worries.\r\n",
            "Steve: Alright, see you then.\r\n",
            "\r\n",
            "### Transcript:\n",
            "\n",
            "Steve: Hey Don. You gonna be at the poker night tonight?\r\n",
            "Don: Yeah. It's a Dave's house. No worries I have some whiskey.\r\n",
            "Steve: Okay. See u.\n",
            "\n",
            "### Transcript:\n",
            "\n",
            "Steve: Hey Don. You gonna be at the poker night tonight?\r\n",
            "Don: Yeah. It's Dave's house. No worries. I have some whiskey. See u there\r\n",
            "Steve: Alright. Cheers\n",
            "\n",
            "### Transcriber:\n",
            "\n",
            "Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey? I've got some. No worries. See ya.\r\n",
            "Donald: Alright. Thanks. See ya.\n",
            "\n",
            "## Synopsis\n",
            "\n",
            "Donald: Are you playing poker tonight?, it's\n",
            "\n",
            "[=============EXAMPLE_9_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION_END]\n"
          ]
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      "source": [
        "Configure and execute finetuning process."
      ],
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      "source": [
        "qlora_fine_tuning_config: dict = yaml.safe_load(\n",
        "\"\"\"\n",
        "model_type: llm\n",
        "base_model: alexsherstinsky/Mistral-7B-v0.1-sharded\n",
        "\n",
        "input_features:\n",
        "  - name: dialogue\n",
        "    type: text\n",
        "    preprocessing:\n",
        "      max_sequence_length: 1024\n",
        "\n",
        "output_features:\n",
        "  - name: summary\n",
        "    type: text\n",
        "    preprocessing:\n",
        "      max_sequence_length: 384\n",
        "\n",
        "prompt:\n",
        "  template: >-\n",
        "    Summarize this dialogue:\n",
        "\n",
        "    ### Dialogue: {dialogue}\n",
        "\n",
        "    ### Synopsis:\n",
        "\n",
        "generation:\n",
        "  temperature: 0.1\n",
        "  max_new_tokens: 512\n",
        "\n",
        "adapter:\n",
        "  type: lora\n",
        "  postprocessor:\n",
        "    merge_adapter_into_base_model: true\n",
        "    progressbar: true\n",
        "\n",
        "quantization:\n",
        "  bits: 8\n",
        "\n",
        "preprocessing:\n",
        "  split:\n",
        "    # type: random\n",
        "    # probabilities: [0.9, 0.05, 0.05]\n",
        "    type: fixed\n",
        "\n",
        "trainer:\n",
        "  type: finetune\n",
        "  epochs: 5\n",
        "  batch_size: 1\n",
        "  eval_batch_size: 2\n",
        "  gradient_accumulation_steps: 16  # effective batch size = batch size * gradient_accumulation_steps\n",
        "  learning_rate: 2.0e-4\n",
        "  enable_gradient_checkpointing: true\n",
        "  learning_rate_scheduler:\n",
        "    decay: cosine\n",
        "    warmup_fraction: 0.03\n",
        "    reduce_on_plateau: 0\n",
        "\"\"\"\n",
        ")"
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          "height": 17
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        "id": "33GSuYmockcv"
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      "cell_type": "code",
      "source": [
        "model: LudwigModel = LudwigModel(config=qlora_fine_tuning_config, logging_level=logging.INFO)"
      ],
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        "id": "Uu2OVNUHHSyp",
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      "source": [
        "results: TrainingResults = model.train(dataset=df_dataset)"
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            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒════════════════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ EXPERIMENT DESCRIPTION │\n",
            "INFO:ludwig.utils.print_utils:╘════════════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:╒══════════════════╤═════════════════════════════════════════════════════════════════════════════════════════╕\n",
            "│ Experiment name  │ api_experiment                                                                          │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Model name       │ run                                                                                     │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Output directory │ /content/results/api_experiment_run                                                     │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ ludwig_version   │ '0.8'                                                                                   │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ command          │ ('/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py -f '                 │\n",
            "│                  │  '/root/.local/share/jupyter/runtime/kernel-9ffedd66-0993-4adc-a04d-cedfeb4acdb9.json') │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ random_seed      │ 42                                                                                      │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ data_format      │ \"<class 'pandas.core.frame.DataFrame'>\"                                                 │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ torch_version    │ '2.0.1+cu118'                                                                           │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ compute          │ {'gpu_type': 'NVIDIA A100-SXM4-40GB', 'gpus_per_node': 1, 'num_nodes': 1}               │\n",
            "╘══════════════════╧═════════════════════════════════════════════════════════════════════════════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═══════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ LUDWIG CONFIG │\n",
            "INFO:ludwig.utils.print_utils:╘═══════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:User-specified config (with upgrades):\n",
            "\n",
            "INFO:ludwig.api:{   'adapter': {   'postprocessor': {   'merge_adapter_into_base_model': True,\n",
            "                                        'progressbar': True},\n",
            "                   'type': 'lora'},\n",
            "    'base_model': 'alexsherstinsky/Mistral-7B-v0.1-sharded',\n",
            "    'generation': {'max_new_tokens': 512, 'temperature': 0.1},\n",
            "    'input_features': [   {   'name': 'dialogue',\n",
            "                              'preprocessing': {'max_sequence_length': 1024},\n",
            "                              'type': 'text'}],\n",
            "    'ludwig_version': '0.8',\n",
            "    'model_type': 'llm',\n",
            "    'output_features': [   {   'name': 'summary',\n",
            "                               'preprocessing': {'max_sequence_length': 384},\n",
            "                               'type': 'text'}],\n",
            "    'preprocessing': {'split': {'type': 'fixed'}},\n",
            "    'prompt': {   'template': 'Summarize this dialog:\\n'\n",
            "                              '### Dialogue: {dialogue}\\n'\n",
            "                              '### Synopsis:'},\n",
            "    'quantization': {'bits': 8},\n",
            "    'trainer': {   'batch_size': 1,\n",
            "                   'enable_gradient_checkpointing': True,\n",
            "                   'epochs': 5,\n",
            "                   'eval_batch_size': 2,\n",
            "                   'gradient_accumulation_steps': 16,\n",
            "                   'learning_rate': 0.0002,\n",
            "                   'learning_rate_scheduler': {   'decay': 'cosine',\n",
            "                                                  'reduce_on_plateau': 0,\n",
            "                                                  'warmup_fraction': 0.03},\n",
            "                   'type': 'finetune'}}\n",
            "INFO:ludwig.api:\n",
            "Full config saved to:\n",
            "/content/results/api_experiment_run/api_experiment/model/model_hyperparameters.json\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═══════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ PREPROCESSING │\n",
            "INFO:ludwig.utils.print_utils:╘═══════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.data.preprocessing:No cached dataset found at /content/42da6c6e63aa11ee9e820242ac1c000c.training.hdf5. Preprocessing the dataset.\n",
            "INFO:ludwig.data.preprocessing:Using full dataframe\n",
            "INFO:ludwig.data.preprocessing:Building dataset (it may take a while)\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "INFO:ludwig.features.text_feature:Max length of feature 'None': 815 (without start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:Setting max length using dataset: 817 (including start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:max sequence length is 817 for feature 'None'\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "INFO:ludwig.features.text_feature:Max length of feature 'summary': 92 (without start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:Setting max length using dataset: 94 (including start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:max sequence length is 94 for feature 'summary'\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "INFO:ludwig.data.preprocessing:Building dataset: DONE\n",
            "INFO:ludwig.data.cache.manager:Writing preprocessed training set cache to /content/42da6c6e63aa11ee9e820242ac1c000c.training.hdf5\n",
            "INFO:ludwig.data.cache.manager:Writing preprocessed validation set cache to /content/42da6c6e63aa11ee9e820242ac1c000c.validation.hdf5\n",
            "INFO:ludwig.data.cache.manager:Writing preprocessed test set cache to /content/42da6c6e63aa11ee9e820242ac1c000c.test.hdf5\n",
            "INFO:ludwig.data.cache.manager:Writing train set metadata to /content/42da6c6e63aa11ee9e820242ac1c000c.meta.json\n",
            "INFO:ludwig.api:\n",
            "Dataset Statistics\n",
            "INFO:ludwig.api:╒════════════╤═══════════════╤════════════════════╕\n",
            "│ Dataset    │   Size (Rows) │ Size (In Memory)   │\n",
            "╞════════════╪═══════════════╪════════════════════╡\n",
            "│ Training   │           700 │ 164.19 Kb          │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Validation │           200 │ 47.00 Kb           │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Test       │           100 │ 23.56 Kb           │\n",
            "╘════════════╧═══════════════╧════════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═══════╕\n",
            "INFO:ludwig.utils.print_utils:│ MODEL │\n",
            "INFO:ludwig.utils.print_utils:╘═══════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:Warnings and other logs:\n",
            "INFO:ludwig.models.llm:Loading large language model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70d00c5419c1471184820a344dc52baf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.models.llm:Done.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "INFO:ludwig.models.llm:==================================================\n",
            "INFO:ludwig.models.llm:Trainable Parameter Summary For Fine-Tuning\n",
            "INFO:ludwig.models.llm:Fine-tuning with adapter: lora\n",
            "INFO:ludwig.models.llm:==================================================\n",
            "INFO:ludwig.trainers.trainer:Gradient checkpointing enabled for training.\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒══════════╕\n",
            "INFO:ludwig.utils.print_utils:│ TRAINING │\n",
            "INFO:ludwig.utils.print_utils:╘══════════╛\n",
            "INFO:ludwig.utils.print_utils:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Creating fresh model training run.\n",
            "INFO:ludwig.trainers.trainer:Training for 3500 step(s), approximately 5 epoch(s).\n",
            "INFO:ludwig.trainers.trainer:Early stopping policy: 5 round(s) of evaluation, or 3500 step(s), approximately 5 epoch(s).\n",
            "\n",
            "INFO:ludwig.trainers.trainer:Starting with step 0, epoch: 0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rTraining:   0%|          | 0/3500 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training:  20%|██        | 700/3500 [14:53<1:05:33,  1.40s/it, loss=0.0922]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 700, epoch: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:08<00:00,  1.45it/s]\n",
            "Evaluation test :   0%|          | 0/50 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation test : 100%|██████████| 50/50 [00:33<00:00,  1.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 1m 42.9714s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0541 │       0.0438 │     0.0551 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.6362 │       4.7065 │     4.2271 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.1978 │       1.5020 │     1.5164 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 17414.4590 │   18448.1367 │ 18655.2031 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31915.6719 │   31938.7520 │ 31939.8477 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2703 │       0.2225 │     0.2434 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1709 │       0.1385 │     0.1538 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7680 │       0.6707 │     0.6830 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1558 │       0.1066 │     0.1196 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.0975 │       0.0658 │     0.0749 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.4648 │       0.3327 │     0.3541 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2429 │       0.1918 │     0.2083 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1540 │       0.1196 │     0.1317 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6841 │       0.5757 │     0.5846 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2621 │       0.2171 │     0.2375 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1659 │       0.1351 │     0.1501 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7435 │       0.6551 │     0.6668 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0001 │       0.0000 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.7340 │       5.0377 │     4.4537 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.1978 │       1.5020 │     1.5164 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Evaluation validation metric: 'summary' 'loss' improved.\n",
            "INFO:ludwig.trainers.trainer:'summary' 'loss' decreased by inf.\n",
            "INFO:ludwig.trainers.trainer:New best model saved.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training:  40%|████      | 1400/3500 [31:41<43:10,  1.23s/it, loss=0.0649]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 1400, epoch: 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:09<00:00,  1.43it/s]\n",
            "Evaluation test :   0%|          | 0/50 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation test : 100%|██████████| 50/50 [00:33<00:00,  1.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 1m 43.5246s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0565 │       0.0430 │     0.0548 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.5866 │       4.8194 │     4.3180 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.8645 │       1.4502 │     1.4545 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16628.7754 │   18222.2812 │ 18364.3242 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31931.9004 │   31924.6211 │ 31931.1074 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2511 │       0.2204 │     0.2390 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1567 │       0.1363 │     0.1495 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7715 │       0.6819 │     0.6842 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1462 │       0.1058 │     0.1174 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.0901 │       0.0650 │     0.0729 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.4945 │       0.3398 │     0.3518 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2243 │       0.1897 │     0.2058 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1398 │       0.1174 │     0.1288 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6940 │       0.5844 │     0.5912 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2462 │       0.2155 │     0.2330 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1537 │       0.1332 │     0.1458 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7556 │       0.6665 │     0.6672 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0001 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.9487 │       5.2489 │     4.6311 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.8645 │       1.4502 │     1.4545 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Evaluation validation metric: 'summary' 'loss' improved.\n",
            "INFO:ludwig.trainers.trainer:'summary' 'loss' decreased by 0.052.\n",
            "INFO:ludwig.trainers.trainer:New best model saved.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training:  60%|██████    | 2100/3500 [48:38<27:52,  1.19s/it, loss=0.0628]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 2100, epoch: 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:10<00:00,  1.41it/s]\n",
            "Evaluation test :   0%|          | 0/50 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation test : 100%|██████████| 50/50 [00:33<00:00,  1.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 1m 44.9432s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0628 │       0.0440 │     0.0530 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.5063 │       4.8742 │     4.3956 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.7849 │       1.4377 │     1.4450 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16158.5312 │   18017.1465 │ 18164.6953 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31877.7402 │   31925.2305 │ 31930.0098 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2859 │       0.2219 │     0.2376 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1803 │       0.1370 │     0.1484 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.8065 │       0.6880 │     0.6869 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1765 │       0.1079 │     0.1147 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1105 │       0.0661 │     0.0712 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.5144 │       0.3458 │     0.3494 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2607 │       0.1915 │     0.2034 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1648 │       0.1184 │     0.1272 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.7291 │       0.5915 │     0.5890 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2817 │       0.2176 │     0.2306 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1778 │       0.1344 │     0.1440 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7916 │       0.6746 │     0.6682 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0004 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.7897 │       5.2811 │     4.7022 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.7849 │       1.4377 │     1.4450 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Evaluation validation metric: 'summary' 'loss' improved.\n",
            "INFO:ludwig.trainers.trainer:'summary' 'loss' decreased by 0.012.\n",
            "INFO:ludwig.trainers.trainer:New best model saved.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training:  80%|████████  | 2800/3500 [1:05:42<14:33,  1.25s/it, loss=0.0601]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 2800, epoch: 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:10<00:00,  1.41it/s]\n",
            "Evaluation test :   0%|          | 0/50 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation test : 100%|██████████| 50/50 [00:33<00:00,  1.47it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 1m 44.8766s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0617 │       0.0439 │     0.0530 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.7339 │       4.8826 │     4.4139 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.7449 │       1.4685 │     1.4759 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16053.4307 │   17887.8789 │ 18029.0254 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31952.8887 │   31936.2852 │ 31936.6816 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2667 │       0.2222 │     0.2365 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1652 │       0.1372 │     0.1472 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.8043 │       0.6929 │     0.6880 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1635 │       0.1096 │     0.1149 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1012 │       0.0671 │     0.0709 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.5094 │       0.3548 │     0.3518 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2431 │       0.1908 │     0.2010 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1509 │       0.1179 │     0.1251 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.7288 │       0.5919 │     0.5876 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2625 │       0.2176 │     0.2293 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1627 │       0.1343 │     0.1427 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7897 │       0.6788 │     0.6683 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.9826 │       5.2878 │     4.7290 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.7449 │       1.4685 │     1.4759 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Last improvement of summary validation loss happened 700 step(s) ago.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training: 100%|██████████| 3500/3500 [1:22:31<00:00,  1.23s/it, loss=0.0155]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 3500, epoch: 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:12<00:00,  1.38it/s]\n",
            "Evaluation test :   0%|          | 0/50 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation test : 100%|██████████| 50/50 [00:35<00:00,  1.42it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 1m 47.7994s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0696 │       0.0412 │     0.0517 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     5.5654 │       5.0372 │     4.5380 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.6412 │       1.5285 │     1.5482 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 15382.8066 │   17618.0391 │ 17727.3555 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31953.3125 │   31927.9395 │ 31929.7363 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2864 │       0.2179 │     0.2358 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1760 │       0.1340 │     0.1464 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.8592 │       0.6899 │     0.6978 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1936 │       0.1049 │     0.1122 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1185 │       0.0640 │     0.0695 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.5926 │       0.3431 │     0.3388 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2603 │       0.1869 │     0.1988 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1601 │       0.1151 │     0.1236 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.7786 │       0.5899 │     0.5876 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2814 │       0.2121 │     0.2282 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1729 │       0.1304 │     0.1417 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.8446 │       0.6725 │     0.6764 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0000 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     5.0066 │       5.4553 │     4.8826 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.6412 │       1.5285 │     1.5482 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Last improvement of summary validation loss happened 1400 step(s) ago.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training: 100%|██████████| 3500/3500 [1:24:19<00:00,  1.45s/it, loss=0.0155]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═════════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ TRAINING REPORT │\n",
            "INFO:ludwig.utils.print_utils:╘═════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:╒══════════════════════════════╤════════════════════╕\n",
            "│ Validation feature           │ summary            │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Validation metric            │ loss               │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model step              │ 2100               │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model epoch             │ 4                  │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's validation loss │ 1.43772292137146   │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's test loss       │ 1.4450316429138184 │\n",
            "╘══════════════════════════════╧════════════════════╛\n",
            "INFO:ludwig.api:\n",
            "Finished: api_experiment_run\n",
            "INFO:ludwig.api:Saved to: /content/results/api_experiment_run\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rUnloading and merging model:   0%|          | 0/454 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:68: UserWarning: Merge lora module to 8-bit linear may get different generations due to rounding errors.\n",
            "  warnings.warn(\n",
            "Unloading and merging model: 100%|██████████| 454/454 [00:06<00:00, 69.27it/s]\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒══════════╕\n",
            "INFO:ludwig.utils.print_utils:│ FINISHED │\n",
            "INFO:ludwig.utils.print_utils:╘══════════╛\n",
            "INFO:ludwig.utils.print_utils:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WwRANm-rPZaL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D2Clph9V9AXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jNCW85tmT2Qw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Perform Inference\n",
        "\n",
        "We can now use the model we finetuned above to make predictions on some test examples to see whether finetuning the large language model improve its ability to follow instructions/the tasks we're asking it to perform."
      ],
      "metadata": {
        "id": "1YkjcqMFMkwT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_and_probabilities: tuple[pd.DataFrame, pd.DataFrame] = model.predict(df_evaluation)"
      ],
      "metadata": {
        "id": "5sQXLxvoMuIq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0aa137f6-599b-46ea-ea75-fa286c0823a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:ludwig.data.preprocessing:Specified split column split for fixed split strategy was not found in dataset.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/data/preprocessing.py:1315: UserWarning: column: 'split' is required by the dataset splitter with params: {'type': 'fixed', 'column': 'split'}, but 'split' is not present in the `dataset_df` with columns: Index(['id', 'dialogue', 'summary'], dtype='object'). This is acceptable during serving setting where dataset splitting is irrelevant. You may see this warning if, for example, the model was trained with a configuration that used a stratified split on the target column, but for live predictions, a value for the target column is not to be provided.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rPrediction:   0%|          | 0/1 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.models.llm:For generating text, using: GenerationConfig {\n",
            "  \"do_sample\": true,\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"pad_token_id\": 2,\n",
            "  \"temperature\": 0.1\n",
            "}\n",
            "\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: adriana: sorry, i guess i can't make it. i'm feeling sick again.\n",
            "adriana: i planned so many things for today...\n",
            "adriana: hate it.\n",
            "elisa: what a pity...\n",
            "elisa: next time!\n",
            "elisa: hope you'll get better soon!\n",
            "adriana: hope so too.\n",
            "adriana: how was at the pool?\n",
            "elisa: the best ❤️\n",
            "elisa: 1 hour of swimming and 1 hour of sauna\n",
            "elisa: love it!\n",
            "adriana:❤️\n",
            "adriana: i'm such a bad swimmer that you couldn't stand me xd\n",
            "elisa: ahahaha\n",
            "elisa: there was such a girl today, soooo annoying 😂\n",
            "adriana: just like me 😂😂😂\n",
            "### synopsis:\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: adriana: sorry, i guess i can't make it. i'm feeling sick again.\n",
            "adriana: i planned so many things for today...\n",
            "adriana: hate it.\n",
            "elisa: what a pity...\n",
            "elisa: next time!\n",
            "elisa: hope you'll get better soon!\n",
            "adriana: hope so too.\n",
            "adriana: how was at the pool?\n",
            "elisa: the best ❤️\n",
            "elisa: 1 hour of swimming and 1 hour of sauna\n",
            "elisa: love it!\n",
            "adriana:❤️\n",
            "adriana: i'm such a bad swimmer that you couldn't stand me xd\n",
            "elisa: ahahaha\n",
            "elisa: there was such a girl today, soooo annoying 😂\n",
            "adriana: just like me 😂😂😂\n",
            "### synopsis: adriana is feeling sick again. she can't make it today. elisa went to the pool and sauna.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: have you seen the news?\n",
            "greg: no. what’s happened?\n",
            "claire: that bitch my neighbor became an mp 🤮\n",
            "kevin: ooo, well done! you must be so jealous 🤣\n",
            "claire: stop it. i hate her so much. she killed my cat 🙈\n",
            "greg: whaaaaat???\n",
            "claire: really. it was 5 years ago. she hit it with a car.\n",
            "greg: okay. i understand 😒\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: have you seen the news?\n",
            "greg: no. what’s happened?\n",
            "claire: that bitch my neighbor became an mp 🤮\n",
            "kevin: ooo, well done! you must be so jealous 🤣\n",
            "claire: stop it. i hate her so much. she killed my cat 🙈\n",
            "greg: whaaaaat???\n",
            "claire: really. it was 5 years ago. she hit it with a car.\n",
            "greg: okay. i understand 😒\n",
            "### synopsis: claire's neighbour became an mp. she killed claire's cat 5 years ago.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: josh: you're a nutter!!!\n",
            "josh: i could never do things like that\n",
            "paul: wouldn't you ever do the bungee jumping or skydiving? c'mon that's not so scary :)\n",
            "josh: hahahah\n",
            "josh: nope, i wouldn't do that :)\n",
            "paul: the next time you're coming with me!\n",
            "josh: no way, mate\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: josh: you're a nutter!!!\n",
            "josh: i could never do things like that\n",
            "paul: wouldn't you ever do the bungee jumping or skydiving? c'mon that's not so scary :)\n",
            "josh: hahahah\n",
            "josh: nope, i wouldn't do that :)\n",
            "paul: the next time you're coming with me!\n",
            "josh: no way, mate\n",
            "### synopsis: josh won't do bungee jumping or skydiving.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: marcel: hey\n",
            "rodolfo: hello\n",
            "marcel: what's up?\n",
            "rodolfo: nothing just got back to the house\n",
            "marcel: i had an exam today and i  think i will get a great grade\n",
            "rodolfo: good for you. mine was pretty hard. not what i was expecting. but i should pass it\n",
            "marcel: oh yeah now i remember you had an exam. let me know if u have results\n",
            "rodolfo: how nice nice of you to remember now\n",
            "marcel: haha i can't believe. i saw when one car hit another one. they were one behind another. it wasn't so much but still it was. the drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\n",
            "rodolfo: i will only know in like 3 weeks\n",
            "marcel: oh long time\n",
            "rodolfo: yea rather long\n",
            "marcel: hopefully it will be fine\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: marcel: hey\n",
            "rodolfo: hello\n",
            "marcel: what's up?\n",
            "rodolfo: nothing just got back to the house\n",
            "marcel: i had an exam today and i  think i will get a great grade\n",
            "rodolfo: good for you. mine was pretty hard. not what i was expecting. but i should pass it\n",
            "marcel: oh yeah now i remember you had an exam. let me know if u have results\n",
            "rodolfo: how nice nice of you to remember now\n",
            "marcel: haha i can't believe. i saw when one car hit another one. they were one behind another. it wasn't so much but still it was. the drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\n",
            "rodolfo: i will only know in like 3 weeks\n",
            "marcel: oh long time\n",
            "rodolfo: yea rather long\n",
            "marcel: hopefully it will be fine\n",
            "### synopsis: marcel had an exam today and thinks he will get a great grade. rodolfo had an exam too, but it was hard. rodolfo will know the results in 3 weeks.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: don: do you see me online?\n",
            "alfred: nope\n",
            "don: ok, something's wrong, i'll have to check my settings\n",
            "alfred: ok\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: don: do you see me online?\n",
            "alfred: nope\n",
            "don: ok, something's wrong, i'll have to check my settings\n",
            "alfred: ok\n",
            "### synopsis: don is not online.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: travis: hi, are you there?\n",
            "lana: hi travis, good to hear from you after holiday.\n",
            "travis: yeah, long time\n",
            "lana: so how was it? greece, right?\n",
            "travis: yep, pure magic. 10 day there and 4 days in albania\n",
            "lana: wow, albania. never been there.\n",
            "travis: kind of wild place, still. but i liked greece better\n",
            "lana: you stayed in one place?\n",
            "travis: no, moved around. we saw almost everything there was to see\n",
            "lana: sounds exciting. \n",
            "travis: take a look. <file_photo>\n",
            "lana: wow, breathtaking. i guess you'll show us more when we meet in the office\n",
            "travis: sure thing. but i'm not back till monday\n",
            "lana: i see. two more days of freedom.\n",
            "travis: precisely. how are things there anyway\n",
            "lana: same old, same old.\n",
            "travis: been worse?\n",
            "lana: i guess it's just this time of the year when not much happens. \n",
            "travis: have you planned your holiday yet?\n",
            "lana: not really. i guess i'll just find something last minute in september.\n",
            "travis: greece perhaps\n",
            "lana: i'll decide when i see the photos ha ha ha. see you around. gotta go.\n",
            "travis: sure. see you on monday\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: travis: hi, are you there?\n",
            "lana: hi travis, good to hear from you after holiday.\n",
            "travis: yeah, long time\n",
            "lana: so how was it? greece, right?\n",
            "travis: yep, pure magic. 10 day there and 4 days in albania\n",
            "lana: wow, albania. never been there.\n",
            "travis: kind of wild place, still. but i liked greece better\n",
            "lana: you stayed in one place?\n",
            "travis: no, moved around. we saw almost everything there was to see\n",
            "lana: sounds exciting. \n",
            "travis: take a look. <file_photo>\n",
            "lana: wow, breathtaking. i guess you'll show us more when we meet in the office\n",
            "travis: sure thing. but i'm not back till monday\n",
            "lana: i see. two more days of freedom.\n",
            "travis: precisely. how are things there anyway\n",
            "lana: same old, same old.\n",
            "travis: been worse?\n",
            "lana: i guess it's just this time of the year when not much happens. \n",
            "travis: have you planned your holiday yet?\n",
            "lana: not really. i guess i'll just find something last minute in september.\n",
            "travis: greece perhaps\n",
            "lana: i'll decide when i see the photos ha ha ha. see you around. gotta go.\n",
            "travis: sure. see you on monday\n",
            "### synopsis: travis has just come back from a 14-day holiday in greece and albania. he will show lana the photos on monday.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: alex: hey, i'm writing about the book :)\n",
            "elizabeth: heey, thanks for the message, i'm packing it to my bag right now.\n",
            "alex: thank you so much...you don't even know grateful i am\n",
            "elizabeth: no problem, really. if i can help you in any other way just ask.\n",
            "alex: actually i have a few questions for you if you don't mind\n",
            "elizabeth: sure, what is it?\n",
            "alex: i'm starting with the senior group next week and i'm so stressed out cause i never taught the adults...could you give me any clues how to prepare, what to do and so on?\n",
            "elizabeth: of course. first thing - relax, cause stress is destructive. i remember my first classes with the adults and i couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\n",
            "alex: i get it...and how does your lesson usually look like?\n",
            "elizabeth: it depends, you know. but for most of the time i'm just trying to stick to my fixed plan. at the beginning checking the attendance, asking them to give me the date in the foreing language so i can put it down in the register. then a short conversation.\n",
            "alex: and what after that?\n",
            "elizabeth: then i usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. i don't want them to do the exercices only, cause it's too automatic.\n",
            "alex: sounds really cool...you are so experienced...i wish i was too :/\n",
            "elizabeth: calm down, you'll just learn everything with practice. if you don't want to make your lessons too formal, you can look for some games. i recommend kaboom.\n",
            "alex: kaboom? i don't know this game\n",
            "elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. every person takes out a single piece of paper and has to translate a word written on it. if they spot the answer, they keep it, if they don't, the card goes back to the bag.\n",
            "alex: sounds really cool! i'm sure i'll try it. thank you so much for your help!!\n",
            "elizabeth: no problem, any time.\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: alex: hey, i'm writing about the book :)\n",
            "elizabeth: heey, thanks for the message, i'm packing it to my bag right now.\n",
            "alex: thank you so much...you don't even know grateful i am\n",
            "elizabeth: no problem, really. if i can help you in any other way just ask.\n",
            "alex: actually i have a few questions for you if you don't mind\n",
            "elizabeth: sure, what is it?\n",
            "alex: i'm starting with the senior group next week and i'm so stressed out cause i never taught the adults...could you give me any clues how to prepare, what to do and so on?\n",
            "elizabeth: of course. first thing - relax, cause stress is destructive. i remember my first classes with the adults and i couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\n",
            "alex: i get it...and how does your lesson usually look like?\n",
            "elizabeth: it depends, you know. but for most of the time i'm just trying to stick to my fixed plan. at the beginning checking the attendance, asking them to give me the date in the foreing language so i can put it down in the register. then a short conversation.\n",
            "alex: and what after that?\n",
            "elizabeth: then i usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. i don't want them to do the exercices only, cause it's too automatic.\n",
            "alex: sounds really cool...you are so experienced...i wish i was too :/\n",
            "elizabeth: calm down, you'll just learn everything with practice. if you don't want to make your lessons too formal, you can look for some games. i recommend kaboom.\n",
            "alex: kaboom? i don't know this game\n",
            "elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. every person takes out a single piece of paper and has to translate a word written on it. if they spot the answer, they keep it, if they don't, the card goes back to the bag.\n",
            "alex: sounds really cool! i'm sure i'll try it. thank you so much for your help!!\n",
            "elizabeth: no problem, any time.\n",
            "### synopsis: alex is starting to teach adults next week. elizabeth gives him some advice.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: karen: hi john, it's me, lost my phone\n",
            "karen: on my way home, don't worry. text me if you've got this. i'll have this phone only for couple of minutes.\n",
            "john: got it, go back home safely.\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: karen: hi john, it's me, lost my phone\n",
            "karen: on my way home, don't worry. text me if you've got this. i'll have this phone only for couple of minutes.\n",
            "john: got it, go back home safely.\n",
            "### synopsis: karen lost her phone. john will text her when he gets her phone.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: there is a letter for tracy from the tax office\n",
            "tracy: thanks mum. can you open it?\n",
            "natalie: it's probably regarding tax return \n",
            "natalie: i got mine last week\n",
            "tracy: open and send me a photo\n",
            "tracy: better priv 🤑\n",
            "claire: no problem \n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: there is a letter for tracy from the tax office\n",
            "tracy: thanks mum. can you open it?\n",
            "natalie: it's probably regarding tax return \n",
            "natalie: i got mine last week\n",
            "tracy: open and send me a photo\n",
            "tracy: better priv 🤑\n",
            "claire: no problem \n",
            "### synopsis: claire has a letter from the tax office for tracy.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: donald: are you playing poker tonight? it's at dave's house\n",
            "steve: yeah i was planning to. do we need to bring whiskey?\n",
            "donald: i've got some, no worries.\n",
            "steve: ok see you\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: donald: are you playing poker tonight? it's at dave's house\n",
            "steve: yeah i was planning to. do we need to bring whiskey?\n",
            "donald: i've got some, no worries.\n",
            "steve: ok see you\n",
            "### synopsis: steve and donald are playing poker tonight at dave's house. donald has some whiskey.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 100%|██████████| 1/1 [01:06<00:00, 66.66s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/features/feature_utils.py:102: RuntimeWarning: divide by zero encountered in log\n",
            "  return np.sum(np.log(sequence_probabilities))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_predictions: pd.DataFrame = predictions_and_probabilities[0]"
      ],
      "metadata": {
        "id": "d9Ri3EUzM6D4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8fc3c2c3-4141-4bb6-ded0-057fe61b18ec"
      },
      "execution_count": null,
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              "  "
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    },
    {
      "cell_type": "code",
      "source": [
        "df_predictions.columns"
      ],
      "metadata": {
        "id": "uaBlVTVRNElt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "23205e10-c793-40f1-fb37-210d01772761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
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              "  </style>\n",
              "  "
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['summary_predictions', 'summary_probabilities', 'summary_response',\n",
              "       'summary_probability'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n\\n\")\n",
        "for dialogue_with_summary in zip(df_evaluation['dialogue'], df_predictions['summary_response']):\n",
        "  print(f\"Dialogue:\\n{dialogue_with_summary[0]}\")\n",
        "  print(f\"Generated Summary:\\n{dialogue_with_summary[1][0]}\")\n",
        "  print(\"\\n\\n\")"
      ],
      "metadata": {
        "id": "0dqwZyHLrkRb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b94e333c-4860-4980-ff42-efb54c00e362"
      },
      "execution_count": null,
      "outputs": [
        {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\r\n",
            "Adriana: I planned so many things for today...\r\n",
            "Adriana: Hate it.\r\n",
            "Elisa: What a pity...\r\n",
            "Elisa: Next time!\r\n",
            "Elisa: Hope you'll get better soon!\r\n",
            "Adriana: Hope so too.\r\n",
            "Adriana: How was at the pool?\r\n",
            "Elisa: The best ❤️\r\n",
            "Elisa: 1 hour of swimming and 1 hour of sauna\r\n",
            "Elisa: Love it!\r\n",
            "Adriana:❤️\r\n",
            "Adriana: I'm such a bad swimmer that you couldn't stand me xD\r\n",
            "Elisa: Ahahaha\r\n",
            "Elisa: There was such a girl today, soooo annoying 😂\r\n",
            "Adriana: Just like me 😂😂😂\n",
            "Generated Summary:\n",
            "adriana is feeling sick again. she can't make it today. elisa went to the pool and sauna.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Claire: Have you seen the news?\r\n",
            "Greg: No. What’s happened?\r\n",
            "Claire: That bitch my neighbor became an MP 🤮\r\n",
            "Kevin: Ooo, well done! You must be so jealous 🤣\r\n",
            "Claire: Stop it. I hate her so much. She killed my cat 🙈\r\n",
            "Greg: Whaaaaat???\r\n",
            "Claire: Really. It was 5 years ago. She hit it with a car.\r\n",
            "Greg: Okay. I understand 😒\n",
            "Generated Summary:\n",
            "claire's neighbour became an mp. she killed claire's cat 5 years ago.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Josh: You're a nutter!!!\n",
            "Josh: I could never do things like that\n",
            "Paul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\n",
            "Josh: hahahah\n",
            "Josh: Nope, I wouldn't do that :)\n",
            "Paul: The next time you're coming with me!\n",
            "Josh: No way, mate\n",
            "Generated Summary:\n",
            "josh won't do bungee jumping or skydiving.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Marcel: Hey\r\n",
            "Rodolfo: Hello\r\n",
            "Marcel: What's up?\r\n",
            "Rodolfo: Nothing just got back to the house\r\n",
            "Marcel: I had an exam today and I  think I will get a great grade\r\n",
            "Rodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\r\n",
            "Marcel: Oh yeah now I remember you had an exam. Let me know if u have results\r\n",
            "Rodolfo: How nice nice of you to remember now\r\n",
            "Marcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\r\n",
            "Rodolfo: I will only know in like 3 weeks\r\n",
            "Marcel: Oh long time\r\n",
            "Rodolfo: Yea rather long\r\n",
            "Marcel: Hopefully it will be fine\n",
            "Generated Summary:\n",
            "marcel had an exam today and thinks he will get a great grade. rodolfo had an exam too, but it was hard. rodolfo will know the results in 3 weeks.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "Generated Summary:\n",
            "don is not online.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Travis: Hi, are you there?\r\n",
            "Lana: Hi Travis, good to hear from you after holiday.\r\n",
            "Travis: Yeah, long time\r\n",
            "Lana: So how was it? Greece, right?\r\n",
            "Travis: Yep, pure magic. 10 day there and 4 days in Albania\r\n",
            "Lana: Wow, Albania. Never been there.\r\n",
            "Travis: Kind of wild place, still. But I liked Greece better\r\n",
            "Lana: You stayed in one place?\r\n",
            "Travis: No, moved around. We saw almost everything there was to see\r\n",
            "Lana: Sounds exciting. \r\n",
            "Travis: Take a look. <file_photo>\r\n",
            "Lana: Wow, breathtaking. I guess you'll show us more when we meet in the office\r\n",
            "Travis: Sure thing. But I'm not back till Monday\r\n",
            "Lana: I see. Two more days of freedom.\r\n",
            "Travis: Precisely. How are things there anyway\r\n",
            "Lana: Same old, same old.\r\n",
            "Travis: Been worse?\r\n",
            "Lana: I guess it's just this time of the year when not much happens. \r\n",
            "Travis: Have you planned your holiday yet?\r\n",
            "Lana: Not really. I guess I'll just find something last minute in September.\r\n",
            "Travis: Greece perhaps\r\n",
            "Lana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\r\n",
            "Travis: Sure. See you on Monday\n",
            "Generated Summary:\n",
            "travis has just come back from a 14-day holiday in greece and albania. he will show lana the photos on monday.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Alex: hey, I'm writing about the book :)\r\n",
            "Elizabeth: heey, thanks for the message, I'm packing it to my bag right now.\r\n",
            "Alex: thank you so much...you don't even know grateful I am\r\n",
            "Elizabeth: No problem, really. If I can help you in any other way just ask.\r\n",
            "Alex: Actually I have a few questions for you if you don't mind\r\n",
            "Elizabeth: Sure, what is it?\r\n",
            "Alex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\r\n",
            "Elizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\r\n",
            "Alex: I get it...and how does your lesson usually look like?\r\n",
            "Elizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\r\n",
            "Alex: and what after that?\r\n",
            "Elizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\r\n",
            "Alex: sounds really cool...you are so experienced...I wish I was too :/\r\n",
            "Elizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\r\n",
            "Alex: Kaboom? I don't know this game\r\n",
            "Elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\r\n",
            "Alex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\r\n",
            "Elizabeth: No problem, any time.\n",
            "Generated Summary:\n",
            "alex is starting to teach adults next week. elizabeth gives him some advice.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\r\n",
            "John: Got it, go back home safely.\n",
            "Generated Summary:\n",
            "karen lost her phone. john will text her when he gets her phone.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Claire: There is a letter for Tracy from the tax office\r\n",
            "Tracy: Thanks mum. Can you open it?\r\n",
            "Natalie: It's probably regarding tax return \r\n",
            "Natalie: I got mine last week\r\n",
            "Tracy: Open and send me a photo\r\n",
            "Tracy: Better priv 🤑\r\n",
            "Claire: No problem \n",
            "Generated Summary:\n",
            "claire has a letter from the tax office for tracy.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey?\r\n",
            "Donald: I've got some, no worries.\r\n",
            "Steve: Ok see you\n",
            "Generated Summary:\n",
            "steve and donald are playing poker tonight at dave's house. donald has some whiskey.\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_control_example: pd.DataFrame = pd.DataFrame(\n",
        "    data={\n",
        "      \"dialogue\": [samsum_test_dialogue,],\n",
        "    }\n",
        ")\n",
        "df_control_example"
      ],
      "metadata": {
        "id": "vLP6o23ggNFi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "1943804a-9eac-41fd-ab9c-53320649e00d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            dialogue\n",
              "0  \\nA: Hi Tom, are you busy tomorrow’s afternoon..."
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "        document.querySelector('#df-35818302-9f72-495e-84ec-64bd6dbaf529 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-35818302-9f72-495e-84ec-64bd6dbaf529');\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",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"\\nA: Hi Tom, are you busy tomorrow\\u2019s afternoon?\\nB: I\\u2019m pretty sure I am. What\\u2019s up?\\nA: Can you go with me to the animal shelter?.\\nB: What do you want to do?\\nA: I want to get a puppy for my son.\\nB: That will make him so happy.\\nA: Yeah, we\\u2019ve discussed it many times. I think he\\u2019s ready now.\\nB: That\\u2019s good. Raising a dog is a tough issue. Like having a baby ;-)\\nA: I'll get him one of those little dogs.\\nB: One that won't grow up too big;-)\\nA: And eat too much;-))\\nB: Do you know which one he would like?\\nA: Oh, yes, I took him there last Monday. He showed me one that he really liked.\\nB: I bet you had to drag him away.\\nA: He wanted to take it home right away ;-).\\nB: I wonder what he'll name it.\\nA: He said he\\u2019d name it after his dead hamster \\u2013 Lemmy  - he's  a great Motorhead fan :-)))\\n\"]],\n        columns: [[\"number\", \"index\"], [\"string\", \"dialogue\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-164d5017-b2a2-49c1-a0e5-940f05e72aba\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-164d5017-b2a2-49c1-a0e5-940f05e72aba')\"\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-164d5017-b2a2-49c1-a0e5-940f05e72aba button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 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          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions: list[list[str]] = predict(model=model, df_test=df_control_example)"
      ],
      "metadata": {
        "id": "IWq96fOff6E0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c55a338e-0043-4c6b-c666-e4a4ad7e3da0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:ludwig.data.preprocessing:Specified split column split for fixed split strategy was not found in dataset.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/data/preprocessing.py:1315: UserWarning: column: 'split' is required by the dataset splitter with params: {'type': 'fixed', 'column': 'split'}, but 'split' is not present in the `dataset_df` with columns: Index(['dialogue'], dtype='object'). This is acceptable during serving setting where dataset splitting is irrelevant. You may see this warning if, for example, the model was trained with a configuration that used a stratified split on the target column, but for live predictions, a value for the target column is not to be provided.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rPrediction:   0%|          | 0/1 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.models.llm:For generating text, using: GenerationConfig {\n",
            "  \"do_sample\": true,\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"pad_token_id\": 2,\n",
            "  \"temperature\": 0.1\n",
            "}\n",
            "\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: \n",
            "a: hi tom, are you busy tomorrow’s afternoon?\n",
            "b: i’m pretty sure i am. what’s up?\n",
            "a: can you go with me to the animal shelter?.\n",
            "b: what do you want to do?\n",
            "a: i want to get a puppy for my son.\n",
            "b: that will make him so happy.\n",
            "a: yeah, we’ve discussed it many times. i think he’s ready now.\n",
            "b: that’s good. raising a dog is a tough issue. like having a baby ;-)\n",
            "a: i'll get him one of those little dogs.\n",
            "b: one that won't grow up too big;-)\n",
            "a: and eat too much;-))\n",
            "b: do you know which one he would like?\n",
            "a: oh, yes, i took him there last monday. he showed me one that he really liked.\n",
            "b: i bet you had to drag him away.\n",
            "a: he wanted to take it home right away ;-).\n",
            "b: i wonder what he'll name it.\n",
            "a: he said he’d name it after his dead hamster – lemmy  - he's  a great motorhead fan :-)))\n",
            "\n",
            "### synopsis:\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: \n",
            "a: hi tom, are you busy tomorrow’s afternoon?\n",
            "b: i’m pretty sure i am. what’s up?\n",
            "a: can you go with me to the animal shelter?.\n",
            "b: what do you want to do?\n",
            "a: i want to get a puppy for my son.\n",
            "b: that will make him so happy.\n",
            "a: yeah, we’ve discussed it many times. i think he’s ready now.\n",
            "b: that’s good. raising a dog is a tough issue. like having a baby ;-)\n",
            "a: i'll get him one of those little dogs.\n",
            "b: one that won't grow up too big;-)\n",
            "a: and eat too much;-))\n",
            "b: do you know which one he would like?\n",
            "a: oh, yes, i took him there last monday. he showed me one that he really liked.\n",
            "b: i bet you had to drag him away.\n",
            "a: he wanted to take it home right away ;-).\n",
            "b: i wonder what he'll name it.\n",
            "a: he said he’d name it after his dead hamster – lemmy  - he's  a great motorhead fan :-)))\n",
            "\n",
            "### synopsis: a wants to get a puppy for his son. b will go with him to the animal shelter tomorrow.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 100%|██████████| 1/1 [00:06<00:00,  6.77s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/features/feature_utils.py:102: RuntimeWarning: divide by zero encountered in log\n",
            "  return np.sum(np.log(sequence_probabilities))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_prediction: str = predictions[0][0]\n",
        "print(f'\\n[GENERATED_TEXT] PREDICTION:\\n{one_prediction}')"
      ],
      "metadata": {
        "id": "b6zapjrAz-d_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "b0ff47a2-5cc5-42e3-ce3e-aa4ca56b2263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[GENERATED_TEXT] PREDICTION:\n",
            "a wants to get a puppy for his son. b will go with him to the animal shelter tomorrow.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Upload Trained Model Artifacts To HuggingFace** 🤗\n",
        "\n",
        "Now that we have a fine-tuned model, we can export the model weights to HuggingFace hub so we can use them in downstream tasks or in production. Ludwig supports uploading model weights directly to HuggingFace Hub via the `upload` Ludwig command.\n",
        "\n",
        "```\n",
        "!ludwig upload hf_hub --repo_id <hf_user_name>/<repo_name> --model_path <top_level_model_directory>\n",
        "```\n",
        "\n",
        "The `model-path` can be seen at the end of training/fine-tuning, as seen in this screenshot.\n",
        "\n",
        "<br>\n",
        "\n",
        "![Screenshot 2023-08-15 at 2.20.00 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)"
      ],
      "metadata": {
        "id": "LWxotc5kGcBD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ludwig upload hf_hub --repo_id \"alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-A100-sharded-8bit-merged\" --model_path /content/results/api_experiment_run"
      ],
      "metadata": {
        "id": "-Y71BStbCQ1A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "9ffb04ad-e1e7-4a30-f22b-8e14baf16513"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\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) Y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n",
            "Model uploaded to `https://huggingface.co/alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-A100-sharded-8bit-merged/tree/main/` with repository name `alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-A100-sharded-8bit-merged`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PfJjc-ZW74S7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8nUWrubnpANI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_fine_tuned_model_name: str = \"alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-A100-sharded-8bit-merged\""
      ],
      "metadata": {
        "id": "SCH8OyED_ZEY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "7ea8ee9b-312d-4fc6-a417-fd8130882efa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "  <style>\n",
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              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
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        }
      ]
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      "metadata": {
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    },
    {
      "cell_type": "code",
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      "metadata": {
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      "execution_count": null,
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      "cell_type": "code",
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      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "4YT5UXXvEH9-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "88b13f07-13fd-45ae-8da7-1c0f63206547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "  <style>\n",
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              "        white-space: pre-wrap;\n",
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              "  </style>\n",
              "  "
            ]
          },
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YVxHFguz68Ib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dw0BnWVW677N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the finetuned LoRA layers for Mistral 7B LLM (adapter weights for SAMSum dataset)."
      ],
      "metadata": {
        "id": "E1MXUMpdYN2n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[LOADING_MODEL] FINE_TUNED_AND_MERGED:\\n{samsum_fine_tuned_model_name}')\n",
        "samsum_tokenizer: LlamaTokenizerFast = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=samsum_fine_tuned_model_name, trust_remote_code=True, padding_side='left')\n",
        "bnb_config_samsum_fine_tuned_model: BitsAndBytesConfig = BitsAndBytesConfig(\n",
        "  load_in_8bit=True,\n",
        ")\n",
        "samsum_model: MistralForCausalLM = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=samsum_fine_tuned_model_name, torch_dtype=torch.float16, trust_remote_code=True, device_map=\"auto\", quantization_config=bnb_config_samsum_fine_tuned_model, low_cpu_mem_usage=True)"
      ],
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184,
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        "outputId": "39afab7e-fade-4df4-d8ae-3d20ec606191"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[LOADING_MODEL] FINE_TUNED_AND_MERGED:\n",
            "alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-A100-sharded-8bit-merged\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a448b18c62e747fa95be47c28f840688"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/13.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e459e585fbb74107983c0b5ecb4ead76"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
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    {
      "cell_type": "code",
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "iPoQC0z0OVO7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e6c5c80e-897d-4fa5-8c22-acabe0f1d611"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1777"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KRmyHTkp-a7E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Eu9o1PFE-Vyo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on finetuned Mistral 7B LLM for SAMSum dataset to verify quality."
      ],
      "metadata": {
        "id": "JCnrqPZPXhG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequences_generator: TextGenerationPipeline = transformers.pipeline(\n",
        "    task=\"text-generation\",\n",
        "    tokenizer=samsum_tokenizer,\n",
        "    model=samsum_model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")"
      ],
      "metadata": {
        "id": "ce4yrJVCY7_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "a651ca10-4ec9-4ba7-f6ea-f7b77a20bc8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Vq6v-xxC03q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequences: list[dict] | list[list[dict]] = samsum_sequences_generator(\n",
        "    text_inputs=samsum_test_prompt,\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=samsum_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "gw2ahnKNZ9BR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a5f6c38c-18ec-4d69-db29-672dc7e8c90d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequence: dict = samsum_sequences[0]"
      ],
      "metadata": {
        "id": "OHp1QhUZY8CL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "71b39c0f-dc98-4836-8ef1-32658de250d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[GENERATED_TEXT] FINE_TUNED_MODEL_PREDICTION:\\n{samsum_sequence[\"generated_text\"]} ; TYPE: {str(type(samsum_sequence[\"generated_text\"]))}')"
      ],
      "metadata": {
        "id": "DNIJBIBVY8FU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "5eafa4a1-505d-4a52-e519-7a231034d351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[GENERATED_TEXT] FINE_TUNED_MODEL_PREDICTION:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: \n",
            "A: Hi Tom, are you busy tomorrow’s afternoon?\n",
            "B: I’m pretty sure I am. What’s up?\n",
            "A: Can you go with me to the animal shelter?.\n",
            "B: What do you want to do?\n",
            "A: I want to get a puppy for my son.\n",
            "B: That will make him so happy.\n",
            "A: Yeah, we’ve discussed it many times. I think he’s ready now.\n",
            "B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)\n",
            "A: I'll get him one of those little dogs.\n",
            "B: One that won't grow up too big;-)\n",
            "A: And eat too much;-))\n",
            "B: Do you know which one he would like?\n",
            "A: Oh, yes, I took him there last Monday. He showed me one that he really liked.\n",
            "B: I bet you had to drag him away.\n",
            "A: He wanted to take it home right away ;-).\n",
            "B: I wonder what he'll name it.\n",
            "A: He said he’d name it after his dead hamster – Lemmy  - he's  a great Motorhead fan :-)))\n",
            "\n",
            "\n",
            "### Synopsis:\n",
            "A wants to get a puppy for his son from the animal shelter. B will go with him. ; TYPE: <class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MhjUYgzLit5T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nN1ztfu6D9yW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "GmlEWKz1D9yW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "764081fe-ffd9-4da6-f017-736dbe0f3e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2IlJMGIHa8Gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r7NRj-eFa75l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on finetuned Mistral 7B LLM for SAMSum dataset over the evaluation dataset."
      ],
      "metadata": {
        "id": "AfeIOOkha9Iu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation: pd.DataFrame = df_evaluation.copy()"
      ],
      "metadata": {
        "id": "m6MoKuHxCk4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "aff735b9-17ba-468a-ae9c-d71284fdac33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation[\"prompt\"] = df_samsum_evaluation[\"dialogue\"].apply(lambda x: samsum_prompt_template.format(**{\"dialogue\": x}))"
      ],
      "metadata": {
        "id": "vT-Z_9pdDFgl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f98e3aac-e5d0-4000-fbe3-9a55c11ebecc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
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    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequences: list[dict] | list[list[dict]] = samsum_sequences_generator(\n",
        "    text_inputs=df_samsum_evaluation[\"prompt\"].to_list(),\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=samsum_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "fW2wlr4dDfHN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "d6f133f6-523a-41c6-e2a1-53229e9809c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1268: UserWarning: Input length of input_ids is 571, but `max_length` is set to 512. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[FINE_TUNED_MODEL_EVALUATION_BEGIN]')\n",
        "idx: int = 0\n",
        "print(f'\\n[=============EXAMPLE_{idx}_BEGIN=============]')\n",
        "for dialogue, synopsis in zip(df_samsum_evaluation[\"dialogue\"].to_list(), samsum_sequences):\n",
        "  print(f'\\n[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\\n{synopsis[0][\"generated_text\"]}')\n",
        "  print(f'\\n[=============EXAMPLE_{idx}_END=============]')\n",
        "  idx += 1\n",
        "\n",
        "print(f'\\n[FINE_TUNED_MODEL_EVALUATION_END]')"
      ],
      "metadata": {
        "id": "W1e56lBUaW7I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "52aa7c03-5cfe-49f4-f740-73e39553f69f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION_BEGIN]\n",
            "\n",
            "[=============EXAMPLE_0_BEGIN=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\r\n",
            "Adriana: I planned so many things for today...\r\n",
            "Adriana: Hate it.\r\n",
            "Elisa: What a pity...\r\n",
            "Elisa: Next time!\r\n",
            "Elisa: Hope you'll get better soon!\r\n",
            "Adriana: Hope so too.\r\n",
            "Adriana: How was at the pool?\r\n",
            "Elisa: The best ❤️\r\n",
            "Elisa: 1 hour of swimming and 1 hour of sauna\r\n",
            "Elisa: Love it!\r\n",
            "Adriana:❤️\r\n",
            "Adriana: I'm such a bad swimmer that you couldn't stand me xD\r\n",
            "Elisa: Ahahaha\r\n",
            "Elisa: There was such a girl today, soooo annoying 😂\r\n",
            "Adriana: Just like me 😂😂😂\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Adriana can't make it today because she's sick. She was looking forward to it. Elisa enjoyed at the pool today.\n",
            "\n",
            "[=============EXAMPLE_0_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: Have you seen the news?\r\n",
            "Greg: No. What’s happened?\r\n",
            "Claire: That bitch my neighbor became an MP 🤮\r\n",
            "Kevin: Ooo, well done! You must be so jealous 🤣\r\n",
            "Claire: Stop it. I hate her so much. She killed my cat 🙈\r\n",
            "Greg: Whaaaaat???\r\n",
            "Claire: Really. It was 5 years ago. She hit it with a car.\r\n",
            "Greg: Okay. I understand 😒\n",
            "\n",
            "### Synopsis:\n",
            "    claire's cat was killed by her neigbour 5 years ago.\n",
            "\n",
            "[=============EXAMPLE_1_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Josh: You're a nutter!!!\n",
            "Josh: I could never do things like that\n",
            "Paul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\n",
            "Josh: hahahah\n",
            "Josh: Nope, I wouldn't do that :)\n",
            "Paul: The next time you're coming with me!\n",
            "Josh: No way, mate\n",
            "\n",
            "### Synopsis:\n",
            "Josh wouldn't do bungee jumping nor skydiving.\n",
            "\n",
            "[=============EXAMPLE_2_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Marcel: Hey\r\n",
            "Rodolfo: Hello\r\n",
            "Marcel: What's up?\r\n",
            "Rodolfo: Nothing just got back to the house\r\n",
            "Marcel: I had an exam today and I  think I will get a great grade\r\n",
            "Rodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\r\n",
            "Marcel: Oh yeah now I remember you had an exam. Let me know if u have results\r\n",
            "Rodolfo: How nice nice of you to remember now\r\n",
            "Marcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\r\n",
            "Rodolfo: I will only know in like 3 weeks\r\n",
            "Marcel: Oh long time\r\n",
            "Rodolfo: Yea rather long\r\n",
            "Marcel: Hopefully it will be fine\n",
            "\n",
            "### Synopsis:\n",
            "```\n",
            "\n",
            "[=============EXAMPLE_3_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "\n",
            "### Synopsis:\n",
            "Don can't be seen online and will have to check his settings.\n",
            "\n",
            "[=============EXAMPLE_4_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Travis: Hi, are you there?\r\n",
            "Lana: Hi Travis, good to hear from you after holiday.\r\n",
            "Travis: Yeah, long time\r\n",
            "Lana: So how was it? Greece, right?\r\n",
            "Travis: Yep, pure magic. 10 day there and 4 days in Albania\r\n",
            "Lana: Wow, Albania. Never been there.\r\n",
            "Travis: Kind of wild place, still. But I liked Greece better\r\n",
            "Lana: You stayed in one place?\r\n",
            "Travis: No, moved around. We saw almost everything there was to see\r\n",
            "Lana: Sounds exciting. \r\n",
            "Travis: Take a look. <file_photo>\r\n",
            "Lana: Wow, breathtaking. I guess you'll show us more when we meet in the office\r\n",
            "Travis: Sure thing. But I'm not back till Monday\r\n",
            "Lana: I see. Two more days of freedom.\r\n",
            "Travis: Precisely. How are things there anyway\r\n",
            "Lana: Same old, same old.\r\n",
            "Travis: Been worse?\r\n",
            "Lana: I guess it's just this time of the year when not much happens. \r\n",
            "Travis: Have you planned your holiday yet?\r\n",
            "Lana: Not really. I guess I'll just find something last minute in September.\r\n",
            "Travis: Greece perhaps\r\n",
            "Lana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\r\n",
            "Travis: Sure. See you on Monday\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "travis has just returned from holiday. he vacationed in greece and albania. his trip was exciting and he visited a lot of sightseeing attractions. lana didn't go anywhere, but will book a vacation last minute in september.\n",
            "\n",
            "[=============EXAMPLE_5_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Alex: hey, I'm writing about the book :)\r\n",
            "Elizabeth: heey, thanks for the message, I'm packing it to my bag right now.\r\n",
            "Alex: thank you so much...you don't even know grateful I am\r\n",
            "Elizabeth: No problem, really. If I can help you in any other way just ask.\r\n",
            "Alex: Actually I have a few questions for you if you don't mind\r\n",
            "Elizabeth: Sure, what is it?\r\n",
            "Alex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\r\n",
            "Elizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\r\n",
            "Alex: I get it...and how does your lesson usually look like?\r\n",
            "Elizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\r\n",
            "Alex: and what after that?\r\n",
            "Elizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\r\n",
            "Alex: sounds really cool...you are so experienced...I wish I was too :/\r\n",
            "Elizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\r\n",
            "Alex: Kaboom? I don't know this game\r\n",
            "Elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\r\n",
            "Alex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\r\n",
            "Elizabeth: No problem, any time.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "[=============EXAMPLE_6_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\r\n",
            "John: Got it, go back home safely.\n",
            "\n",
            "### Synopsis:\n",
            "Karen calls John because she's lost her phone.\n",
            "\n",
            "[=============EXAMPLE_7_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: There is a letter for Tracy from the tax office\r\n",
            "Tracy: Thanks mum. Can you open it?\r\n",
            "Natalie: It's probably regarding tax return \r\n",
            "Natalie: I got mine last week\r\n",
            "Tracy: Open and send me a photo\r\n",
            "Tracy: Better priv 🤑\r\n",
            "Claire: No problem \n",
            "\n",
            "### Synopsis:\n",
            "    \n",
            "    there is a letter from the tax office for tracy.\n",
            "    the letter is probably regarding tax return.\n",
            "\n",
            "[=============EXAMPLE_8_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey?\r\n",
            "Donald: I've got some, no worries.\r\n",
            "Steve: Ok see you\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Donald and Steve are playing poker tonight. Donald has some whiskey.\n",
            "\n",
            "[=============EXAMPLE_9_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION_END]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9rwBdAruD0NC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}