RLHF_Part_II.ipynb

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      "execution_count": 2,
      "metadata": {
        "id": "BnQ6RtzwYjgM"
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      "source": [
        "# #installing necessary libraries\n",
        "# ! pip install torch==1.13.1\n",
        "# ! pip torchdata==0.5.1 --quiet\n",
        "\n",
        "# !pip install \\\n",
        "#     transformers==4.27.2 \\\n",
        "#     datasets==2.11.0 \\\n",
        "#     evaluate==0.4.0 \\\n",
        "#     rouge_score==0.1.2 \\\n",
        "#     loralib==0.1.1 \\\n",
        "#     peft==0.3.0 --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#install necessary libraries\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer\n",
        "import torch\n",
        "import time\n",
        "import evaluate\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "aqUw4uOYZcN_"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "#loading dataset from hugging face\n",
        "dataset_name = \"knkarthick/dialogsum\"\n",
        "\n",
        "dataset = load_dataset(dataset_name)\n",
        "\n",
        "dataset"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
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          "text": [
            "Downloading and preparing dataset csv/knkarthick--dialogsum to /root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
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              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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              "Downloading data:   0%|          | 0.00/11.3M [00:00<?, ?B/s]"
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        {
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "model_id": "7f7b1109e2ee442a93bf5ba4cdf3521d"
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        {
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              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
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        {
          "output_type": "stream",
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          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
          ]
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
              "        num_rows: 12460\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
              "        num_rows: 1500\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
              "        num_rows: 500\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example_indices = [300, 500]\n",
        "\n",
        "dash_line = '-'.join('' for x in range(100))\n",
        "\n",
        "for i, index in enumerate(example_indices):\n",
        "    print(dash_line)\n",
        "    print('Example ', i + 1)\n",
        "    print(dash_line)\n",
        "    print('INPUT DIALOGUE:')\n",
        "    print(dataset['test'][index]['dialogue'])\n",
        "    print(dash_line)\n",
        "    print('BASELINE HUMAN SUMMARY:')\n",
        "    print(dataset['test'][index]['summary'])\n",
        "    print(dash_line)\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bothroG_rNE5",
        "outputId": "c822a805-4117-4683-fced-a6b7b651ffbe"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "Example  1\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT DIALOGUE:\n",
            "#Person1#: I cannot imagine if Trump were to be our President again.\n",
            "#Person2#: I am proud to say that he is our President, and I will be really happy if he could be re-elected.\n",
            "#Person1#: You voted for him, right?\n",
            "#Person2#: Did you vote for him, because I know that I did.\n",
            "#Person1#: I am not sure about this.\n",
            "#Person2#: I have nothing but faith in Trump.\n",
            "#Person1#: What?\n",
            "#Person2#: I am pretty sure he will make America great again!\n",
            "#Person1#: Well, though we do need some change in this country, I don't think he is the right person.\n",
            "#Person2#: Our country is already changing as it is.\n",
            "#Person1#: You are right about this.\n",
            "#Person2#: I trust that he will take good care of our country.\n",
            "#Person1#: Well, I don't think so. I will vote for Biden anyway.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# is crazy for Trump and voted for him. #Person2# doesn't agree with #Person1# on Trump and will vote for Biden.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Example  2\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT DIALOGUE:\n",
            "#Person1#: Are you going anywhere for your vacation?\n",
            "#Person2#: Yes, we're making plans for a tour.\n",
            "#Person1#: That'll be lovely. Where are you going?\n",
            "#Person2#: Well, we will start out from Long Island this Friday. We've planned a four day drive to Salt Lake City, where we'll join my brother and his family on his fortieth birthday.\n",
            "#Person1#: Well, you've got to prepare a lot of food and enough sleeping bags then.\n",
            "#Person2#: Oh, we'll spend the nights in hotels and enjoy local food as we pass by. How does it sound, David?\n",
            "#Person1#: It sounds good. You can do a lot of sightseeing, too.\n",
            "#Person2#: Yes, we'll take our time. And we'll go to Five Lake Strict and the Wall Street.\n",
            "#Person1#: So, you're going to have a really nice vacation.\n",
            "#Person2#: You can say that again.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person2# tells David the plan for a tour and #Person2# will celebrate #Person2#'s brother's fortieth birthday when at Salt Lake City.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loading Model from hugging face\n",
        "model_name='google/flan-t5-base'\n",
        "original_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
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          "height": 241,
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          ]
        },
        "id": "AR6Prum5eqTT",
        "outputId": "0129b1b1-0e07-4281-ac17-e6659cdcdf13"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
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          "output_type": "display_data",
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_trainable_model_parameters(model):\n",
        "    trainable_model_params = 0\n",
        "    all_model_params = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_model_params += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_model_params += param.numel()\n",
        "    return f\"trainable model parameters: {trainable_model_params}\\nall model parameters: {all_model_params}\\npercentage of trainable model parameters: {100 * trainable_model_params / all_model_params:.2f}%\"\n",
        "\n",
        "print(get_trainable_model_parameters(original_model))"
      ],
      "metadata": {
        "id": "8f_bA2qnfotk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d86bd89-9d5d-401e-efce-033d7d8cf559"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable model parameters: 247577856\n",
            "all model parameters: 247577856\n",
            "percentage of trainable model parameters: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model, TaskType\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=32, # Rank\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q\", \"v\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=TaskType.SEQ_2_SEQ_LM # FLAN-T5\n",
        ")"
      ],
      "metadata": {
        "id": "xFCHiU76mU9b"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model = get_peft_model(original_model,\n",
        "                            lora_config)\n",
        "print(get_trainable_model_parameters(peft_model))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDD3sLc4oeVC",
        "outputId": "594c0a02-ceef-489d-e001-45c65ea65e1d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable model parameters: 3538944\n",
            "all model parameters: 251116800\n",
            "percentage of trainable model parameters: 1.41%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset(example):\n",
        "    start_prompt = 'Summarize the following conversation.\\n\\n'\n",
        "    end_prompt = '\\n\\nSummary: '\n",
        "    prompt = [start_prompt + dialogue + end_prompt for dialogue in example[\"dialogue\"]]\n",
        "    example['input_ids'] = tokenizer(prompt, padding=\"max_length\", truncation=True, return_tensors=\"pt\").input_ids\n",
        "    example['labels'] = tokenizer(example[\"summary\"], padding=\"max_length\", truncation=True, return_tensors=\"pt\").input_ids\n",
        "\n",
        "    return example\n",
        "\n",
        "tokenized_datasets = dataset.map(prepare_dataset, batched=True)\n",
        "tokenized_datasets = tokenized_datasets.remove_columns(['id', 'topic', 'dialogue', 'summary',])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "ed2d62b499524ba2ad9bc4bbe4406379",
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            "cfbf4c513253436db1ec51da928b810a"
          ]
        },
        "id": "zVtpARvMpyBg",
        "outputId": "2afc464f-1e8a-4027-a2b7-48fca5c4e411"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/12460 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed2d62b499524ba2ad9bc4bbe4406379"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee273effdd3b4c83b8f822171560e1bc"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_TnFJkhs3ZW",
        "outputId": "6b4f6b15-fce5-43d3-d990-73d91a0ce06b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': (12460, 2), 'test': (1500, 2), 'validation': (500, 2)}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "QHrUXB3jp-GT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output_dir = f'./peft-training-{str(int(time.time()))}'\n",
        "\n",
        "peft_training_args = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    auto_find_batch_size=True,\n",
        "    learning_rate=1e-3, # Higher learning rate than full fine-tuning.\n",
        "    num_train_epochs=10,\n",
        "    logging_steps=1,\n",
        "    max_steps=100\n",
        ")\n",
        "\n",
        "peft_trainer = Trainer(\n",
        "    model=peft_model,\n",
        "    args=peft_training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        ")"
      ],
      "metadata": {
        "id": "yRJWTRfoonW8"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peft_trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pcyaTC6Vpbot",
        "outputId": "7d9f799e-22ec-4ea4-9bb3-ac1c598decd5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 03:58, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>14.687500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>10.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>8.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>5.156200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>6.093800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>5.718800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>4.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>4.406200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>4.343800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>4.281200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>4.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>4.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>3.968800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>3.734400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>3.734400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>3.328100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>3.218800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>3.015600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>2.906200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>3.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>2.515600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>2.453100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>2.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>2.093800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.992200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>2.312500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.828100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.828100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.578100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.546900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.515600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.437500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.226600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.226600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.117200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.437500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.023400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.921900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.910200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.839800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.886700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.839800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.968800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.765600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.746100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.683600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.695300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.800800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.695300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.687500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.710900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.828100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.550800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.558600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.550800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.546900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.515600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.546900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.488300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.462900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.539100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.421900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.404300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.515600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.410200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.474600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.402300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.418000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.392600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.404300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.441400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.406200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.539100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.414100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.380900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.388700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.392600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.418000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.341800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.406200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.343800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.388700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.382800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.332000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.373000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.353500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.355500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.343800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.326200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.334000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.369100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.382800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.337900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.304700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.353500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=1.91345703125, metrics={'train_runtime': 240.7524, 'train_samples_per_second': 3.323, 'train_steps_per_second': 0.415, 'total_flos': 556503190732800.0, 'train_loss': 1.91345703125, 'epoch': 0.06})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model_path=\"/content/peft-tuned-output_final\"\n",
        "peft_trainer.model.save_pretrained(peft_model_path)\n",
        "tokenizer.save_pretrained(peft_model_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWRqjO4ksH9N",
        "outputId": "ac3d16a4-f38e-441b-c42f-a6ffb36b5304"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/peft-tuned-output_final/tokenizer_config.json',\n",
              " '/content/peft-tuned-output_final/special_tokens_map.json',\n",
              " '/content/peft-tuned-output_final/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "9c12ffcd5c7241f8bd0be71e4d35b731",
            "089720ec7c59477a8d584ce823fce7bf",
            "8e329bea4f1b427397240052157f94dc",
            "e3c4e6489dc8426d91f001950dda589c",
            "922864b2a05c4713b3d71d77b233d435",
            "676354a811cf4ea08030b4ea69451884",
            "66c67b1bdced40a4901292ada29cff16",
            "b174d03f678b4d03b5e8513505f72e5e",
            "f9e40a1ff6a34ab88e9be3085c2cf5d0",
            "fe80f090e3b949d9aefa5ba85d781116",
            "431b4fa4387140fb81afb4a1e75f90d9",
            "a5481e9a36a54fc89709510b4aa8c5c4",
            "ef7a07716d9f495d9158704d02abbf16",
            "859216309d2c414684620cfc56cb3daa",
            "d019f3a925964434b95f9c2e45268b7e",
            "a9ff1c55cd6a4e4b8e6057134f2cc9d9",
            "461ab81bacfc49e599e456ce669659a1",
            "2c05150439424a71996e753c094f3e78",
            "0fc5ec8c93a24a68baa0751c5dfe2b9f",
            "6769044b0a3b4f169f5e1ea4ed72ed0e",
            "51dcb7fb429740898ff50c07a744df12",
            "8f47d1f052f14794a884914f4a4da575",
            "b7928ae4c6974e35b4c205bde1a89b2b",
            "54f36a38a9a243a1b6904e1fd0c5b50f",
            "d45a60cd70a34b6f9366a23b75a0cbd4",
            "6f31b55f6f2b4bb78ebcc16487c7944d",
            "51b9098e8d6844b8936bab9ff4241bf7",
            "0a5d8c0079874c989200d8f21100c690",
            "1f7b72157ebf4a2d9e3d1cdb21cc6d05",
            "29beba7074cf4b3e86206847d402b6d0",
            "ac0671e5132840a2a23588a95f5266b7",
            "71295fc0c3d74d09925a264f06174f7c"
          ]
        },
        "id": "9-3HrjiNuDNh",
        "outputId": "41a5b063-c5c9-4b19-e406-cd9fb88f6c80"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c12ffcd5c7241f8bd0be71e4d35b731"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel, PeftConfig\n",
        "\n",
        "peft_model_base = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-base\", torch_dtype=torch.bfloat16)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-base\")\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(peft_model_base,\n",
        "                                       '/content/peft-tuned-output_final/',\n",
        "                                       torch_dtype=torch.bfloat16,\n",
        "                                       is_trainable=False)"
      ],
      "metadata": {
        "id": "8-VKhah_wLTa"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = 300\n",
        "dialogue = dataset['test'][index]['dialogue']\n",
        "baseline_human_summary = dataset['test'][index]['summary']\n",
        "\n",
        "prompt = f\"\"\"\n",
        "Summarize the following conversation.\n",
        "\n",
        "{dialogue}\n",
        "\n",
        "Summary: \"\"\"\n",
        "\n",
        "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
        "\n",
        "peft_model_outputs = peft_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n",
        "peft_model_text_output = tokenizer.decode(peft_model_outputs[0], skip_special_tokens=True)\n",
        "\n",
        "print(dash_line)\n",
        "print(f'BASELINE HUMAN SUMMARY:\\n{baseline_human_summary}')\n",
        "print(dash_line)\n",
        "print(dash_line)\n",
        "print(f'PEFT MODEL: {peft_model_text_output}')\n",
        "print(dash_line)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NKiqVHOw0aP",
        "outputId": "4ac23cb2-8eff-47a1-9ea3-df2788fe614e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# is crazy for Trump and voted for him. #Person2# doesn't agree with #Person1# on Trump and will vote for Biden.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "---------------------------------------------------------------------------------------------------\n",
            "PEFT MODEL: #Person1# is proud to say that he is our President, and I will be really happy if he could be re-elected. #Person1# is proud to say that he is our President, and I will be really happy if he can be re-elected. #Person1# is proud to say that he is our President, and I will be really happy if he can be re-elected. #Person2# is proud to say that he is our President, and I will be really happy if he can be re-elected. #Person1#: Did you vote for him, because I know that I did. #Person1#: Did you vote for him, because I know that I did. #Person1#: Did you vote for him, because I know that I did. #Person2#: Did you vote for him\n",
            "---------------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dP_gR7wAw0rO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model.push_to_hub(\"flan-t5_fine_tuned_summarization\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "91a83e7345f940099ef933a62e055889",
            "cbb924b6285a427d8b4c959188fb66e4",
            "398eb9da4c304fb982582b1a96ffcb18",
            "aa001eaf59f64f3aa7e69d74b700c1d1",
            "1f7d95639eb746379852d6d4876202e9",
            "dddf74ca05cb4d5c87762442cc8f1170",
            "eac1ebdcc20f4e9aa0138f8275c2c665",
            "401252a9e31d4216a2facdd3489cd349",
            "a73559b655714bbdb55c948dfa336ec8",
            "06b944e9525d4f4b8ad3db0ab4f4cc47",
            "7380f3caa2ac48eaa4e7e1aa1b56a18e"
          ]
        },
        "id": "RAl7kTysu4qt",
        "outputId": "061c7197-19fc-442b-f926-ac4ece3f5247"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91a83e7345f940099ef933a62e055889"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Sakil/flan-t5_fine_tuned_summarization/commit/e2c8a18a1821ba52d2be0d36aad1b87294d1f00e', commit_message='Upload model', commit_description='', oid='e2c8a18a1821ba52d2be0d36aad1b87294d1f00e', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wVTFZ_buvw5a"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}