Courses/LangChain/Lecture2. Models/LangСhain. Lecture 2d. RLHF.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# RLHF\n",
        "RLHF (Reinforcement Learning from Human Feedback) is an important component of the current method used to train advanced language models. It helps include people’s feedback when fine-tuning the model, which ultimately makes the model more useful and secure."
      ],
      "metadata": {
        "id": "8cIqUHa9B5lS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/244.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/244.2 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.8/294.8 kB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.5.0\n",
        "!pip install -q sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "import torch\n",
        "from datasets import Dataset, load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForSequenceClassification,\n",
        "    AutoTokenizer,\n",
        "    TrainingArguments,\n",
        ")\n",
        "from trl import RewardTrainer"
      ],
      "metadata": {
        "id": "Q3fcuUCvnoS3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random.seed(42)"
      ],
      "metadata": {
        "id": "tKITp1qGpS6g"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hf_dataset = load_dataset(\n",
        "    \"argilla/dolly-curated-comparison-falcon-7b-instruct\", split=\"train\"\n",
        ")"
      ],
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        "id": "Ytg6Ws0jnsRd",
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      "execution_count": 4,
      "outputs": [
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          "output_type": "display_data",
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
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              "Generating train split:   0%|          | 0/7401 [00:00<?, ? examples/s]"
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        "df = hf_dataset.to_pandas()\n",
        "df"
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      },
      "execution_count": 5,
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              "                                                 prompt  \\\n",
              "0                                  What is Depreciation   \n",
              "1     What do you know about the city of Aberdeen in...   \n",
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              "7399                     Is Killing is Sin ? Is it ture   \n",
              "7400  Who was Otto von Bismarck?\\nOtto, Prince of Bi...   \n",
              "\n",
              "                                             response-1  \\\n",
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              "1     Aberdeen, the city in which I've lived for the...   \n",
              "2     Describe thunderstorm season in the United Sta...   \n",
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              "4     The best way to answer an interview question m...   \n",
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              "7400  Who was Otto von Bismarck?\\nOtto von Bismarck ...   \n",
              "\n",
              "                                             response-2  \\\n",
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              "1     As an AI language model, I don't have personal...   \n",
              "2     Describe thunderstorm season in the United Sta...   \n",
              "3     When did Peloton IPO?\\nPeloton IPO'd on May 26...   \n",
              "4     Some of the best ways to answer an interview q...   \n",
              "...                                                 ...   \n",
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              "7398  Without quoting directly from the text give me...   \n",
              "7399  Is Killing is Sin ? Is it ture?\\nKilling is no...   \n",
              "7400  Who was Otto von Bismarck?\\nOtto von Bismarck ...   \n",
              "\n",
              "                    category  \\\n",
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              "2     information_extraction   \n",
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              "2     Thunderstorm season in the United States and C...        10567  \n",
              "3     Peloton became a public company via an initial...        12412  \n",
              "4     The first recommended step is to ask clarifyin...         2521  \n",
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              "7396          Embrace the change and see the difference        15010  \n",
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              "7398  Brendon Small is a  stand-up comedian, Creator...        14205  \n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prompt</th>\n",
              "      <th>response-1</th>\n",
              "      <th>response-2</th>\n",
              "      <th>category</th>\n",
              "      <th>original_response</th>\n",
              "      <th>external_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What is Depreciation</td>\n",
              "      <td>What is Depreciation – 10 Important Facts to K...</td>\n",
              "      <td>What is Depreciation on a Car?\\nDepreciation i...</td>\n",
              "      <td>open_qa</td>\n",
              "      <td>Depreciation is the drop in value of an asset ...</td>\n",
              "      <td>518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>What do you know about the city of Aberdeen in...</td>\n",
              "      <td>Aberdeen, the city in which I've lived for the...</td>\n",
              "      <td>As an AI language model, I don't have personal...</td>\n",
              "      <td>open_qa</td>\n",
              "      <td>Aberdeen is a city located in the North East o...</td>\n",
              "      <td>351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Describe thunderstorm season in the United Sta...</td>\n",
              "      <td>Describe thunderstorm season in the United Sta...</td>\n",
              "      <td>Describe thunderstorm season in the United Sta...</td>\n",
              "      <td>information_extraction</td>\n",
              "      <td>Thunderstorm season in the United States and C...</td>\n",
              "      <td>10567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>When did Peloton IPO?\\nOn September 26, 2019, ...</td>\n",
              "      <td>When did Peloton IPO?\\nPeloton launched its in...</td>\n",
              "      <td>When did Peloton IPO?\\nPeloton IPO'd on May 26...</td>\n",
              "      <td>closed_qa</td>\n",
              "      <td>Peloton became a public company via an initial...</td>\n",
              "      <td>12412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What is the best way to answer an interview qu...</td>\n",
              "      <td>The best way to answer an interview question m...</td>\n",
              "      <td>Some of the best ways to answer an interview q...</td>\n",
              "      <td>creative_writing</td>\n",
              "      <td>The first recommended step is to ask clarifyin...</td>\n",
              "      <td>2521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7396</th>\n",
              "      <td>How do i accept the change</td>\n",
              "      <td>How do i accept the change in my life\\nAccepti...</td>\n",
              "      <td>I's a great opportunity to improve. The only t...</td>\n",
              "      <td>brainstorming</td>\n",
              "      <td>Embrace the change and see the difference</td>\n",
              "      <td>15010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7397</th>\n",
              "      <td>Extract the teams that the footballer Sócrates...</td>\n",
              "      <td>Extract the teams that the footballer Sócrates...</td>\n",
              "      <td>Extract the teams that the footballer Sócrates...</td>\n",
              "      <td>information_extraction</td>\n",
              "      <td>Brazil, Botafogo-SP, Corinthians, Fiorentina</td>\n",
              "      <td>9970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7398</th>\n",
              "      <td>Without quoting directly from the text give me...</td>\n",
              "      <td>Without quoting directly from the text give me...</td>\n",
              "      <td>Without quoting directly from the text give me...</td>\n",
              "      <td>summarization</td>\n",
              "      <td>Brendon Small is a  stand-up comedian, Creator...</td>\n",
              "      <td>14205</td>\n",
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              "    <tr>\n",
              "      <th>7399</th>\n",
              "      <td>Is Killing is Sin ? Is it ture</td>\n",
              "      <td>Is Killing is Sin ? Is it ture?\\nKilling can b...</td>\n",
              "      <td>Is Killing is Sin ? Is it ture?\\nKilling is no...</td>\n",
              "      <td>brainstorming</td>\n",
              "      <td>Killing a human being should not be sin becaus...</td>\n",
              "      <td>11253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7400</th>\n",
              "      <td>Who was Otto von Bismarck?\\nOtto, Prince of Bi...</td>\n",
              "      <td>Who was Otto von Bismarck?\\nOtto von Bismarck ...</td>\n",
              "      <td>Who was Otto von Bismarck?\\nOtto von Bismarck ...</td>\n",
              "      <td>information_extraction</td>\n",
              "      <td>Otto von Bismarck was a Prussian and German so...</td>\n",
              "      <td>12872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7401 rows × 6 columns</p>\n",
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              "        document.querySelector('#df-5db7b2bf-60f8-4368-81dd-cbd86a004481 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-5db7b2bf-60f8-4368-81dd-cbd86a004481');\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",
              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of response options\n",
        "responses = [\"response-1\", \"response-2\"]\n",
        "\n",
        "\n",
        "def get_chosen_and_not_chosen(responses):\n",
        "    \"\"\"\n",
        "    Given a list of responses, randomly selects one and returns it along with the non-selected response.\n",
        "\n",
        "    Args:\n",
        "        responses (list): List of response options.\n",
        "\n",
        "    Returns:\n",
        "        tuple: A tuple containing the chosen response, non-chosen response, and the index of the chosen response.\n",
        "    \"\"\"\n",
        "    chosen_id = random.randint(0, len(responses) - 1)\n",
        "    not_chosen_id = 1 - chosen_id\n",
        "\n",
        "    return responses[chosen_id], responses[not_chosen_id], chosen_id\n",
        "\n",
        "\n",
        "# List to store rows of data\n",
        "rows = []\n",
        "\n",
        "# Iterate through the hf_dataset\n",
        "for record in hf_dataset:\n",
        "    chosen, not_chosen, chosen_id = get_chosen_and_not_chosen(responses)\n",
        "    chosen_from_falcon, _, _ = get_chosen_and_not_chosen(responses)\n",
        "\n",
        "    # Append a new row to the 'rows' list\n",
        "    rows.append(\n",
        "        {\n",
        "            \"instruction\": record[\"prompt\"],\n",
        "            \"chosen_response\": record[chosen],\n",
        "            \"rejected_response\": record[not_chosen],\n",
        "        }\n",
        "    )"
      ],
      "metadata": {
        "id": "utzMtMABn5TS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_dataset = Dataset.from_list(rows)\n",
        "prepared_dataset.to_pandas()"
      ],
      "metadata": {
        "id": "TDtUvcWroFwg",
        "outputId": "afabebc5-c2c3-49ef-8f81-8d99740caea5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            instruction  \\\n",
              "0                                  What is Depreciation   \n",
              "1     What do you know about the city of Aberdeen in...   \n",
              "2     Describe thunderstorm season in the United Sta...   \n",
              "3     When did Peloton IPO?\\nOn September 26, 2019, ...   \n",
              "4     What is the best way to answer an interview qu...   \n",
              "...                                                 ...   \n",
              "7396                         How do i accept the change   \n",
              "7397  Extract the teams that the footballer Sócrates...   \n",
              "7398  Without quoting directly from the text give me...   \n",
              "7399                     Is Killing is Sin ? Is it ture   \n",
              "7400  Who was Otto von Bismarck?\\nOtto, Prince of Bi...   \n",
              "\n",
              "                                        chosen_response  \\\n",
              "0     What is Depreciation – 10 Important Facts to K...   \n",
              "1     As an AI language model, I don't have personal...   \n",
              "2     Describe thunderstorm season in the United Sta...   \n",
              "3     When did Peloton IPO?\\nPeloton launched its in...   \n",
              "4     Some of the best ways to answer an interview q...   \n",
              "...                                                 ...   \n",
              "7396  I's a great opportunity to improve. The only t...   \n",
              "7397  Extract the teams that the footballer Sócrates...   \n",
              "7398  Without quoting directly from the text give me...   \n",
              "7399  Is Killing is Sin ? Is it ture?\\nKilling can b...   \n",
              "7400  Who was Otto von Bismarck?\\nOtto von Bismarck ...   \n",
              "\n",
              "                                      rejected_response  \n",
              "0     What is Depreciation on a Car?\\nDepreciation i...  \n",
              "1     Aberdeen, the city in which I've lived for the...  \n",
              "2     Describe thunderstorm season in the United Sta...  \n",
              "3     When did Peloton IPO?\\nPeloton IPO'd on May 26...  \n",
              "4     The best way to answer an interview question m...  \n",
              "...                                                 ...  \n",
              "7396  How do i accept the change in my life\\nAccepti...  \n",
              "7397  Extract the teams that the footballer Sócrates...  \n",
              "7398  Without quoting directly from the text give me...  \n",
              "7399  Is Killing is Sin ? Is it ture?\\nKilling is no...  \n",
              "7400  Who was Otto von Bismarck?\\nOtto von Bismarck ...  \n",
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              "      <td>When did Peloton IPO?\\nPeloton IPO'd on May 26...</td>\n",
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              "      <td>How do i accept the change in my life\\nAccepti...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7397</th>\n",
              "      <td>Extract the teams that the footballer Sócrates...</td>\n",
              "      <td>Extract the teams that the footballer Sócrates...</td>\n",
              "      <td>Extract the teams that the footballer Sócrates...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7398</th>\n",
              "      <td>Without quoting directly from the text give me...</td>\n",
              "      <td>Without quoting directly from the text give me...</td>\n",
              "      <td>Without quoting directly from the text give me...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7399</th>\n",
              "      <td>Is Killing is Sin ? Is it ture</td>\n",
              "      <td>Is Killing is Sin ? Is it ture?\\nKilling can b...</td>\n",
              "      <td>Is Killing is Sin ? Is it ture?\\nKilling is no...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7400</th>\n",
              "      <td>Who was Otto von Bismarck?\\nOtto, Prince of Bi...</td>\n",
              "      <td>Who was Otto von Bismarck?\\nOtto von Bismarck ...</td>\n",
              "      <td>Who was Otto von Bismarck?\\nOtto von Bismarck ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7401 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2dbcd35-bc96-4473-8a40-8d702b7eb489')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b2dbcd35-bc96-4473-8a40-8d702b7eb489 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-b2dbcd35-bc96-4473-8a40-8d702b7eb489');\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-b9e1bc45-ef35-44fd-90be-140c36bda9dd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b9e1bc45-ef35-44fd-90be-140c36bda9dd')\"\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-b9e1bc45-ef35-44fd-90be-140c36bda9dd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_dataset"
      ],
      "metadata": {
        "id": "Xnz8nfVxi_4A",
        "outputId": "21e5c17d-ab0a-4927-f220-4a0910863498",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['instruction', 'chosen_response', 'rejected_response'],\n",
              "    num_rows: 7401\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_dataset_mini = prepared_dataset.select(range(1000))"
      ],
      "metadata": {
        "id": "i41OBTF-sueL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_dataset_mini"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hhVBCs5tp6s",
        "outputId": "14aa0ddb-7335-4816-86da-2d884c505fd0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['instruction', 'chosen_response', 'rejected_response'],\n",
              "    num_rows: 1000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import (\n",
        "    AutoModelForSequenceClassification,\n",
        "    BitsAndBytesConfig,\n",
        "    AutoTokenizer,\n",
        ")\n",
        "\n",
        "# Load tokenizer for the \"facebook/opt-350m\" model\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-350m\")\n",
        "\n",
        "# Prepare quantization parameters\n",
        "quantization_config = BitsAndBytesConfig(load_in_8bit=False, load_in_4bit=True)\n",
        "\n",
        "# Initialize the sequence classification model\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"facebook/opt-350m\",\n",
        "    quantization_config=quantization_config,  # Apply the quantization configuration\n",
        "    device_map={\"\": 0},  # Assign the model to device 0\n",
        "    trust_remote_code=True,  # Trust remote code\n",
        "    num_labels=1,  # Set the number of labels for classification (in this case, 1)\n",
        ")\n",
        "\n",
        "# Disable cache in model configuration\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "id": "-LIu6Sg-pjZ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244,
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        "outputId": "9107c975-1505-4539-d2f1-ee0f538dc60c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/685 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/644 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "52fb5029f8f7486280ae380ea68e1dec"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/441 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/663M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cac81cc55c294f5e88338d564a4d40fe"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of OPTForSequenceClassification were not initialized from the model checkpoint at facebook/opt-350m and are newly initialized: ['score.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If the tokenizer's pad_token is not set, use eos_token as pad_token and update model's pad_token_id\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    model.config.pad_token_id = model.config.eos_token_id\n",
        "\n",
        "\n",
        "# Define a formatting function for processing examples\n",
        "def formatting_func(examples):\n",
        "    kwargs = {\n",
        "        \"padding\": \"max_length\",\n",
        "        \"truncation\": True,\n",
        "        \"max_length\": 512,\n",
        "        \"return_tensors\": \"pt\",\n",
        "    }\n",
        "\n",
        "    # Prepend the instruction and a line break to the chosen_response and rejected_response fields.\n",
        "    prompt_plus_chosen_response = (\n",
        "        examples[\"instruction\"] + \"\\n\" + examples[\"chosen_response\"]\n",
        "    )\n",
        "    prompt_plus_rejected_response = (\n",
        "        examples[\"instruction\"] + \"\\n\" + examples[\"rejected_response\"]\n",
        "    )\n",
        "\n",
        "    # Tokenize the modified fields.\n",
        "    tokens_chosen = tokenizer.encode_plus(prompt_plus_chosen_response, **kwargs)\n",
        "    tokens_rejected = tokenizer.encode_plus(prompt_plus_rejected_response, **kwargs)\n",
        "\n",
        "    return {\n",
        "        \"input_ids_chosen\": tokens_chosen[\"input_ids\"][0],\n",
        "        \"attention_mask_chosen\": tokens_chosen[\"attention_mask\"][0],\n",
        "        \"input_ids_rejected\": tokens_rejected[\"input_ids\"][0],\n",
        "        \"attention_mask_rejected\": tokens_rejected[\"attention_mask\"][0],\n",
        "    }\n",
        "\n",
        "\n",
        "# Apply the formatting function to the prepared dataset\n",
        "formatted_dataset = prepared_dataset_mini.map(formatting_func)\n",
        "\n",
        "# Split the formatted dataset into training and testing sets\n",
        "formatted_dataset = formatted_dataset.train_test_split()"
      ],
      "metadata": {
        "id": "zBiHuNgcpurf",
        "outputId": "4f2dd5b3-85a2-4d13-b013-71a1a9c17c27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b0ff25797eb94389971dd46aa7afb81b",
            "73fcbca90db74cf886d8badd42839e7b",
            "45d126d2f5e24516b37d94d6488619a9",
            "cc02f5a3fc66400cbc187575b8d49f1d",
            "507eea333b4b4f939a60016035596b57",
            "7ee6d7a4084842659e6a7767fcec8a74",
            "26db46f3e7bf4fa18ab81b31dc9c116c",
            "707e2f487843442daf0f39871531d765",
            "3040211758f34116895dd3ce44059712",
            "d03583cee64541469904fb52c70a15a8",
            "49a1788111c14c5589b4b908bf2fcbcc"
          ]
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0ff25797eb94389971dd46aa7afb81b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "from peft import LoraConfig\n",
        "from trl import RewardTrainer\n",
        "\n",
        "# Prepare training parameters\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./train_logs\",  # Output folder\n",
        "    max_steps=100,  # Maximum number of training steps\n",
        "    per_device_train_batch_size=4,  # Batch size per GPU for training\n",
        "    gradient_accumulation_steps=1,  # Number of steps to accumulate gradients\n",
        "    learning_rate=1.0e-4,  # Learning rate\n",
        "    optim=\"adamw_torch\",  # Optimizer\n",
        "    save_steps=50,  # How often to save checkpoints\n",
        "    logging_steps=10,  # How often to log training information\n",
        "    report_to=\"tensorboard\",  # Reporting method (in this case, TensorBoard)\n",
        "    remove_unused_columns=False,  # Whether to remove unused columns\n",
        "    evaluation_strategy=\"steps\",  # Evaluation strategy\n",
        "    num_train_epochs=5,  # Number of training epochs\n",
        ")\n",
        "\n",
        "# Prepare PEFT parameters\n",
        "peft_config = LoraConfig(\n",
        "    r=16,  # Value of r\n",
        "    lora_alpha=16,  # Value of lora_alpha\n",
        "    bias=\"none\",  # Bias setting\n",
        "    task_type=\"SEQ_CLS\",  # Task type (Sequence Classification)\n",
        "    modules_to_save=[\"scores\"],  # Modules to save\n",
        ")\n",
        "\n",
        "# Prepare RewardTrainer\n",
        "trainer = RewardTrainer(\n",
        "    model=model,  # The model for reinforcement learning\n",
        "    tokenizer=tokenizer,  # The tokenizer for processing input data\n",
        "    args=training_args,  # Training arguments\n",
        "    train_dataset=formatted_dataset[\"train\"],  # Training dataset\n",
        "    eval_dataset=formatted_dataset[\"test\"],  # Evaluation dataset\n",
        "    peft_config=peft_config,  # PEFT configuration\n",
        "    max_length=512,  # Maximum length of input\n",
        ")\n",
        "\n",
        "# Execute training\n",
        "trainer.train()\n",
        "\n",
        "# Save the pretrained reward model\n",
        "trainer.model.save_pretrained(\"./reward_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "DyFr_ad5vpZU",
        "outputId": "17a3d34a-4de1-403a-db22-a6596ee550bd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2411: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  warnings.warn(\n",
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\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 13:05, 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",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.739600</td>\n",
              "      <td>0.792111</td>\n",
              "      <td>0.528000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.841500</td>\n",
              "      <td>0.793017</td>\n",
              "      <td>0.528000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.916200</td>\n",
              "      <td>0.788118</td>\n",
              "      <td>0.528000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.741200</td>\n",
              "      <td>0.782025</td>\n",
              "      <td>0.544000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.786500</td>\n",
              "      <td>0.781935</td>\n",
              "      <td>0.556000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.690000</td>\n",
              "      <td>0.780752</td>\n",
              "      <td>0.556000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.857800</td>\n",
              "      <td>0.779521</td>\n",
              "      <td>0.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.756600</td>\n",
              "      <td>0.778371</td>\n",
              "      <td>0.536000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.774400</td>\n",
              "      <td>0.777437</td>\n",
              "      <td>0.544000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.774000</td>\n",
              "      <td>0.777306</td>\n",
              "      <td>0.540000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2411: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "\n",
        "def get_score(model, tokenizer, prompt, response):\n",
        "    \"\"\"\n",
        "    Computes a score for a given prompt and response using a provided model and tokenizer.\n",
        "\n",
        "    Args:\n",
        "        model (nn.Module): The model for scoring.\n",
        "        tokenizer: The tokenizer for processing input data.\n",
        "        prompt (str): The prompt text.\n",
        "        response (str): The response text.\n",
        "\n",
        "    Returns:\n",
        "        float: The computed score.\n",
        "    \"\"\"\n",
        "    # Tokenize the input sequences\n",
        "    inputs = tokenizer.encode_plus(\n",
        "        prompt,\n",
        "        response,\n",
        "        truncation=True,\n",
        "        padding=\"max_length\",\n",
        "        max_length=512,\n",
        "        return_tensors=\"pt\",\n",
        "    ).to(\"cuda:0\")\n",
        "\n",
        "    # Perform forward pass\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "\n",
        "    # Extract the logits\n",
        "    logits = outputs.logits\n",
        "\n",
        "    return logits.item()"
      ],
      "metadata": {
        "id": "plzrdqmV1diK"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = 4242\n",
        "prepared_dataset[x]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIVkBwoK1fAL",
        "outputId": "62183184-ee58-4763-c9f6-939876575ff7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'instruction': 'Given this paragraph about WWII, how many fatalities happened?\\n\"World War II or the Second World War, often abbreviated as WWII or WW2, was a global conflict that lasted from 1939 to 1945. The vast majority of the world\\'s countries, including all of the great powers, fought as part of two opposing military alliances: the Allies and the Axis. Many participants threw their economic, industrial, and scientific capabilities behind this total war, blurring the distinction between civilian and military resources. Aircraft played a major role, enabling the strategic bombing of population centres and the delivery of the only two nuclear weapons ever used in war.\\n\\nWorld War II was by far the deadliest conflict in history; it resulted in an estimated 70 to 85 million fatalities, mostly among civilians. Tens of millions died due to genocides (including the Holocaust), starvation, massacres, and disease. In the wake of the Axis defeat, Germany and Japan were occupied, and war crimes tribunals were conducted against German and Japanese leaders.\\n\\nThe causes of World War II are debated, but contributing factors included the Second Italo-Ethiopian War, Spanish Civil War, Second Sino-Japanese War, Soviet–Japanese border conflicts, the rise of fascism in Europe, and European tensions in the aftermath of World War I. World War II is generally considered to have begun on 1 September 1939, when Nazi Germany, under Adolf Hitler, invaded Poland. The United Kingdom and France subsequently declared war on Germany on 3 September. Under the Molotov–Ribbentrop Pact of August 1939, Germany and the Soviet Union had partitioned Poland and marked out their \"\"spheres of influence\"\" across Finland, Estonia, Latvia, Lithuania and Romania. From late 1939 to early 1941, in a series of campaigns and treaties, Germany conquered or controlled much of continental Europe, in a military alliance with Italy, Japan and other countries called the Axis. Following the onset of campaigns in North Africa and East Africa, and the fall of France in mid-1940, the war continued primarily between the European Axis powers and the British Empire, with war in the Balkans, the aerial Battle of Britain, the Blitz of the United Kingdom, and the Battle of the Atlantic. On 22 June 1941, Germany led the European Axis powers in an invasion of the Soviet Union, opening the Eastern Front, the largest land theatre of war in history.\"',\n",
              " 'chosen_response': 'Given this paragraph about WWII, how many fatalities happened?\\nApproximately 75 million fatalities happened during World War II.',\n",
              " 'rejected_response': 'Given this paragraph about WWII, how many fatalities happened?\\nApproximately 6.6 million fatalities happened during World War II.'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the prompt and responses for the example\n",
        "prompt = prepared_dataset[x][\"instruction\"]\n",
        "rejected_response = prepared_dataset[x][\"rejected_response\"]\n",
        "chosen_response = prepared_dataset[x][\"chosen_response\"]\n",
        "\n",
        "# Get the score for the example with the less preferred response\n",
        "score_less_pref = get_score(model, tokenizer, prompt, rejected_response)\n",
        "print(f\"Score for less preferred response: {score_less_pref}\")\n",
        "\n",
        "# Get the score for the example with the preferred response\n",
        "score_pref = get_score(model, tokenizer, prompt, chosen_response)\n",
        "print(f\"Score for preferred response: {score_pref}\")"
      ],
      "metadata": {
        "id": "UPpSY-waqSJ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88a5c71d-3b3f-4c13-84b8-a9bf7a8afb91"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score for less preferred response: 0.539810061454773\n",
            "Score for preferred response: 0.5915793776512146\n"
          ]
        }
      ]
    }
  ]
}