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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##Inferencing"
      ],
      "metadata": {
        "id": "UuWGB7B3f9EL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54mB6vFuf5h7",
        "outputId": "8ea20838-64b3-4910-825f-3dae35dc5f7c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/huggingface-cli\", line 8, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/commands/huggingface_cli.py\", line 49, in main\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/commands/user.py\", line 98, in run\n",
            "    login(token=self.args.token, add_to_git_credential=self.args.add_to_git_credential)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/_login.py\", line 112, in login\n",
            "    interpreter_login(new_session=new_session, write_permission=write_permission)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/_login.py\", line 167, in interpreter_login\n",
            "    token = getpass(\"Token: \")\n",
            "  File \"/usr/lib/python3.10/getpass.py\", line 77, in unix_getpass\n",
            "    passwd = _raw_input(prompt, stream, input=input)\n",
            "  File \"/usr/lib/python3.10/getpass.py\", line 146, in _raw_input\n",
            "    line = input.readline()\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install tensorflow[and-cuda]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzNPKFNfG7I1",
        "outputId": "e2225ff0-0a2f-4858-bbdd-c28bf5ce8d90"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m417.9/417.9 MB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cufft-cu11==10.9.0.58 (from tensorflow[and-cuda])\n",
            "  Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.4/168.4 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cudnn-cu11==8.7.0.84 (from tensorflow[and-cuda])\n",
            "  Downloading nvidia_cudnn_cu11-8.7.0.84-py3-none-manylinux1_x86_64.whl (728.5 MB)\n",
            "\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/728.5 MB\u001b[0m \u001b[31m92.2 MB/s\u001b[0m eta \u001b[36m0:00:07\u001b[0m\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.7/728.5 MB\u001b[0m \u001b[31m90.9 MB/s\u001b[0m eta \u001b[36m0:00:07\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q accelerate peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
      ],
      "metadata": {
        "id": "LQVhFU1sgoiQ"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else \"cpu\"\n",
        "model = \"deepapaikar/Llama_SCplusQA_10epochs\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "from transformers import AutoModelForCausalLM\n",
        "model = AutoModelForCausalLM.from_pretrained(model, load_in_4bit=True)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "17b7ddaf-89bd-47a0-b755-0aba37191af2"
      },
      "execution_count": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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          "name": "stderr",
          "text": [
            "Some weights of LlamaForCausalLM were not initialized from the model checkpoint at deepapaikar/Llama_SCplusQA_10epochs and are newly initialized: ['model.layers.9.self_attn.rotary_emb.inv_freq', 'model.layers.10.self_attn.rotary_emb.inv_freq', 'model.layers.24.self_attn.rotary_emb.inv_freq', 'model.layers.26.self_attn.rotary_emb.inv_freq', 'model.layers.29.self_attn.rotary_emb.inv_freq', 'model.layers.20.self_attn.rotary_emb.inv_freq', 'model.layers.5.self_attn.rotary_emb.inv_freq', 'model.layers.8.self_attn.rotary_emb.inv_freq', 'model.layers.23.self_attn.rotary_emb.inv_freq', 'model.layers.0.self_attn.rotary_emb.inv_freq', 'model.layers.16.self_attn.rotary_emb.inv_freq', 'model.layers.18.self_attn.rotary_emb.inv_freq', 'model.layers.12.self_attn.rotary_emb.inv_freq', 'model.layers.7.self_attn.rotary_emb.inv_freq', 'model.layers.2.self_attn.rotary_emb.inv_freq', 'model.layers.4.self_attn.rotary_emb.inv_freq', 'model.layers.31.self_attn.rotary_emb.inv_freq', 'model.layers.6.self_attn.rotary_emb.inv_freq', 'model.layers.22.self_attn.rotary_emb.inv_freq', 'model.layers.27.self_attn.rotary_emb.inv_freq', 'model.layers.15.self_attn.rotary_emb.inv_freq', 'model.layers.19.self_attn.rotary_emb.inv_freq', 'model.layers.17.self_attn.rotary_emb.inv_freq', 'model.layers.3.self_attn.rotary_emb.inv_freq', 'model.layers.1.self_attn.rotary_emb.inv_freq', 'model.layers.14.self_attn.rotary_emb.inv_freq', 'model.layers.30.self_attn.rotary_emb.inv_freq', 'model.layers.28.self_attn.rotary_emb.inv_freq', 'model.layers.11.self_attn.rotary_emb.inv_freq', 'model.layers.13.self_attn.rotary_emb.inv_freq', 'model.layers.25.self_attn.rotary_emb.inv_freq', 'model.layers.21.self_attn.rotary_emb.inv_freq']\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": "markdown",
      "source": [
        "### Mount file"
      ],
      "metadata": {
        "id": "EK07IJrs3-ha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "7DAREHFJ2pgX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/QA_test.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "z68vnBsR2jTR",
        "outputId": "2920814e-5066-4ce9-a1c9-0f877b5af10d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                            question  \\\n",
              "0  Which Yeshiva University school is Dr. Assa Co...   \n",
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              "2  Following his entry into Goldstein Golub Kessl...   \n",
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              "\n",
              "                                              answer  \n",
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              "        const element = document.querySelector('#df-20c203e9-d3e6-41bb-a179-0725d78b812d');\n",
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              "          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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              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\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",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      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",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predictions"
      ],
      "metadata": {
        "id": "u4rWaxiH5B4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "for i in range(10):\n",
        "    torch.cuda.empty_cache()\n",
        "    gc.collect()"
      ],
      "metadata": {
        "id": "TjTofJ_Tm6XQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Where is Yeshiva University? \"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yce7kEmSPdfY",
        "outputId": "1149e9a4-cae8-4999-a84c-31bb58cefb75"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Where is Yeshiva University?  [/INST] New York City</s> Yeshiva University is a top 100 university in the United States.</s> It is located in New York and known for its business and technology programs.</s> It is also known for its central campus, which is in New York City.</s> The university is divided into five schools: Sackstein Center for Dispute Resolution, Stern College for Women, Sy Syms School of Business, Undergraduate Studies, and Wurzweiler School of Social Work.</s> Yeshiva University is a top 100 university in the United States, known for its business and technology programs, located in New York City, and divided into five schools.</s> The five schools include Belfer Center for Policy Studies, Cardozo School of Law, The Ferkauf Graduate School of Psychology, The George Washington University, and Yes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming your DataFrame is named df\n",
        "df['predicted'] = df.apply(lambda row: pipeline(\n",
        "    task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)(\n",
        "    f\"<s>[INST] {row['question']} [/INST]\"\n",
        ")[0]['generated_text'], axis=1)\n",
        "\n",
        "# Save the DataFrame to a new CSV file with the 'predicted' column\n",
        "df.to_csv('/content/drive/MyDrive/Fall 2023/Model building/Llama/SC+QA Training/Dataset/Test Dataset/output_file.csv', index=False)"
      ],
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        "colab": {
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        "id": "7qlcbH234vCj",
        "outputId": "cfcb5659-4a32-4a85-b5d8-7a0798f8ede7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
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              "model_id": "b880a75e5e824e6eb1cb51a51d248243"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rogue"
      ],
      "metadata": {
        "id": "ECs6oHiP63Rv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rouge"
      ],
      "metadata": {
        "id": "vVUlRI5c64Vz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b3900a5-5326-4dd6-d7b4-2bc3e09134af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rouge\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from rouge) (1.16.0)\n",
            "Installing collected packages: rouge\n",
            "Successfully installed rouge-1.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = pd.read_csv('/content/drive/MyDrive/Fall 2023/Model building/Llama/SC+QA Training/Dataset/Test Dataset/output_file.csv')"
      ],
      "metadata": {
        "id": "N_nUOHHB65HZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "f532aca3-69e2-4b8b-acca-19c810ed82b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-927853f58ad5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Fall 2023/Model building/Llama/SC+QA Training/Dataset/Test Dataset/output_file.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds"
      ],
      "metadata": {
        "id": "x7hpkK_567Oe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from rouge import Rouge"
      ],
      "metadata": {
        "id": "38_h5TrX68u7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "act_ans = []\n",
        "pred_ans = []\n",
        "\n",
        "for index, row in preds.iterrows():\n",
        "\n",
        "  act_ans.append(row['answer'])\n",
        "  pred_ans.append(row['predicted'])"
      ],
      "metadata": {
        "id": "uJeU-VAh6_C2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hyps, refs = map(list, (pred_ans,act_ans))\n",
        "rouge = Rouge()\n",
        "\n",
        "scores = rouge.get_scores(hyps, refs, avg=True)\n",
        "scores"
      ],
      "metadata": {
        "id": "txQqDfpG7A1M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate"
      ],
      "metadata": {
        "id": "bDTVwYGp7Clr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of dictionaries for tabulating the scores\n",
        "score_table = [{'Metric': metric, 'Precision': score['p'], 'Recall': score['r'], 'F1-Score': score['f']} for metric, score in scores.items()]\n",
        "\n",
        "# Print the scores in a tabular format\n",
        "print(tabulate(score_table, headers='keys', tablefmt='grid'))"
      ],
      "metadata": {
        "id": "LiS_DuY67EEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rcjIiD1h7FqG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}