arxiv_t5_lora_PEFT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPDhC6icD-ou"
      },
      "source": [
        "# 1) Installations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqeUE1eKD-ov",
        "outputId": "4720b268-c1cf-484e-a273-9b31909c84a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2.0.7)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
            "Installing collected packages: peft\n",
            "Successfully installed peft-0.7.1\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.25.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
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            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
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            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install torch torchdata\n",
        "# !pip install transformers==4.35\n",
        "!pip install transformers datasets\n",
        "!pip install -q -U bitsandbytes\n",
        "# !pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -U accelerate\n",
        "!pip install evaluate rouge_score\n",
        "!pip install -qqq trl==0.7.1\n",
        "!pip install loralib\n",
        "\n",
        "!pip install loralib peft\n",
        "# !pip install accelerate==0.20.3\n",
        "!pip install -U accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viKI5GF_D-ow",
        "outputId": "140aa6ad-b644-4447-ada3-661953356b58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch ver: 2.1.0+cu121\n",
            "accelerate ver: 0.25.0\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer\n",
        "from peft import LoraConfig, get_peft_model, TaskType, PeftModel, PeftConfig\n",
        "from datasets import load_dataset, DatasetDict, load_metric\n",
        "import accelerate\n",
        "\n",
        "import evaluate\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "print(\"torch ver:\", torch. __version__ )\n",
        "# print(\"transformers ver:\", transformers.__version__)\n",
        "print(\"accelerate ver:\",accelerate.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvIrDuPhWEc7",
        "outputId": "56f87473-3fdc-431a-f7d9-1db7c3c0385f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOFv_P0UD-ox",
        "outputId": "7f27eb61-d9c9-4129-aaaf-f3cf2d0f4cba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on: cuda\n"
          ]
        }
      ],
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "print('Running on:', device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DqSHsIYk4wYM"
      },
      "outputs": [],
      "source": [
        "seperator_print_short = '-'.join('' for x in range(100))\n",
        "seperator_print_long = '-'.join('' for x in range(140))\n",
        "\n",
        "def print_input_outputs(i, index, input, label, model_output):\n",
        "    print(seperator_print_long)\n",
        "\n",
        "    print(f\"Sample {i+1}: Index = {index}\")\n",
        "    print(seperator_print_short)\n",
        "\n",
        "    print(f\"INPUT:\")\n",
        "    print(input)\n",
        "    print(seperator_print_short)\n",
        "\n",
        "    print(\"DATASET ORIGINAL LABEL:\")\n",
        "    print(label)\n",
        "    print(seperator_print_short)\n",
        "\n",
        "    if model_output != \"\":\n",
        "        print(\"OUTPUT OF THE MODEL\")\n",
        "        print(model_output)\n",
        "        print(seperator_print_short)\n",
        "\n",
        "    print(seperator_print_long)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSc___lLD-ox"
      },
      "source": [
        "# 2) Data Preprocessing & Model Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "833a81d3837049abac05c0da7c5a65fa",
            "8e8f6229152c43ed8af22dfaf75bf00c",
            "1b8e4776a7fa4aecb5a474b6dbfaf2e4",
            "a37ce5d3c4ea4959b5c0bbe12c8f6534",
            "6059b09a38ed47929974cbd1b81f3082",
            "29c5984a51454caa825fa6de16c62057",
            "2d3b9838b96f430181f4165b768d8dfc",
            "c87ae5370a10492e9806fc7ab45f2faf",
            "8831f28e5c4247b8a546d252e8c6bfba",
            "cd878cb690d244c29a93da3f94a339af",
            "4ad7a29be9f0491ba194d062a23af73c"
          ]
        },
        "id": "wgia9xBQD-ox",
        "outputId": "c3fc4ba0-07cf-40dd-c1b7-1a19de984a62"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "833a81d3837049abac05c0da7c5a65fa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 299131\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# dataset_src = './arxiv_title_abs_CS_2000.csv'\n",
        "dataset_src = \"/content/drive/MyDrive/datasets/arxiv_title_abs_CS_v2.csv\"\n",
        "dataset_origin = load_dataset('csv',\n",
        "                       data_files=dataset_src\n",
        "                       )\n",
        "dataset_origin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNPPN5Eikfsh",
        "outputId": "53f1a8f9-11d1-4d0c-a981-f16fb096650f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 209391\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 89740\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dset_train_test = (load_dataset('csv',\n",
        "                        data_files=dataset_src,\n",
        "                        split='train'\n",
        "                        )\n",
        "          .train_test_split(train_size=0.7)\n",
        "          )\n",
        "\n",
        "dset_train_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4sGgXfMkfsi",
        "outputId": "c2c46de9-de16-416a-98a3-422ad9f6a8ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 67305\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 22435\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dset_test_val = dset_train_test[\"test\"].train_test_split(train_size=0.75)\n",
        "# train --> validation\n",
        "# test --> test\n",
        "dset_test_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2Oc7Uo2kfsi",
        "outputId": "42c22d0b-75f0-461a-b257-de7b8b072bcb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 209391\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 67305\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 22435\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = DatasetDict({\n",
        "                      'train': dset_train_test['train'],\n",
        "                      'validation': dset_test_val['train'],\n",
        "                      'test': dset_test_val['test']}\n",
        "                      )\n",
        "\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEYX4E8fD-ox",
        "outputId": "a82e3efa-f3d1-448b-ac78-6e112cf6062a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 6000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'title', 'categories', 'abstract'],\n",
              "        num_rows: 250\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# limit number of samples\n",
        "\n",
        "dataset['train'] = dataset['train'].shuffle().select([i for i in range(6000)])\n",
        "dataset['validation'] = dataset['validation'].shuffle().select([i for i in range(1000)])\n",
        "dataset['test'] = dataset['test'].shuffle().select([i for i in range(250)])\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "yACFbuXPkfsi"
      },
      "outputs": [],
      "source": [
        "INPUT_COL_NAME = \"abstract\"\n",
        "OUTPUT_COL_NAME = \"title\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYTJ1NQ_D-oy",
        "outputId": "42ee6d21-7d5a-45ba-fce6-f3f4cfb1aec8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 1: Index = 10\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "  Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories. They allow to verify and replicate existing findings, and to link is connected results. The notion of self-play, albeit often cited in multiagent Reinforcement Learning, has never been grounded in a formal model. We present a formalized framework, with clearly defined assumptions, which encapsulates the meaning of self-play as abstracted from various existing self-play algorithms. This framework is framed as an approximation to a theoretical solution concept for multiagent training. On a simple environment, we qualitatively measure how well a subset of the captured self-play methods approximate this solution when paired with the famous PPO algorithm. We also provide insights on interpreting quantitative metrics of performance for self-play training. Our results indicate that, throughout training, various self-play definitions exhibit cyclic policy evolutions. \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "A Comparison of Self-Play Algorithms Under a Generalized Framework\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 2: Index = 15\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "  In this article, we develop and analyze a finite element method with the first family N\\'ed\\'elec elements of the lowest degree for solving a Maxwell interface problem modeled by a $\\mathbf{H}(\\text{curl})$-elliptic equation on unfitted meshes. To capture the jump conditions optimally, we construct and use $\\mathbf{H}(\\text{curl})$ immersed finite element (IFE) functions on interface elements while keep using the standard N\\'ed\\'elec functions on all the non-interface elements. We establish a few important properties for the IFE functions including the unisolvence according to the edge degrees of freedom, the exact sequence relating to the $H^1$ IFE functions and the optimal approximation capabilities. In order to achieve the optimal convergence rates, we employ a Petrov-Galerkin method in which the IFE functions are only used as the trial functions and the standard N\\'ed\\'elec functions are used as the test functions which can eliminate the non-conformity errors. We analyze the inf-sup conditions under certain conditions and show the optimal convergence rates which are also validated by numerical experiments. \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "Solving Two Dimensional H(curl)-elliptic Interface Systems with Optimal   Convergence On Unfitted Meshes\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "samples_to_check = [10, 15]\n",
        "\n",
        "for i, index in enumerate(samples_to_check):\n",
        "    print_input_outputs(i,\n",
        "                        index,\n",
        "                        input=dataset['train'][index][INPUT_COL_NAME],\n",
        "                        label=dataset['train'][index][OUTPUT_COL_NAME],\n",
        "                        model_output = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wx3kg3q-4wYO"
      },
      "source": [
        "BNB Config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bJNjiaRd4wYO"
      },
      "outputs": [],
      "source": [
        "# from transformers import BitsAndBytesConfig\n",
        "\n",
        "# # nested quantization (4bit using NF4 quantization)\n",
        "# # bnb_4bit_use_double_quant: second quatnization after the\n",
        "# #                            first one to save add 0.4 bits\n",
        "# BNB_NF4_CONFIG = BitsAndBytesConfig(\n",
        "#     load_in_4bit=True,                    # enable 4-bit quantization\n",
        "#     bnb_4bit_use_double_quant=True,\n",
        "#     bnb_4bit_quant_type=\"nf4\",\n",
        "#     bnb_4bit_compute_dtype=torch.bfloat16\n",
        "# )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqmfWnr14wYP"
      },
      "source": [
        "Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381,
          "referenced_widgets": [
            "58947df763764c0198b5db0df5bc885e",
            "1602d3d2ec5241b9b1288a7ed5e17504",
            "32d75896ee73475a8eb6f940e58dec90",
            "a5499185b174487698aca1be6b2e5e6a",
            "44b9e53731b04997a2b9d100376604a5",
            "18336d060bea410cbc639d433fb4d7c3",
            "2e2db68270e5460bb698792b210406a1",
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            "59ec4112d44b4831aa42cc4b7ba192ea"
          ]
        },
        "id": "bZ9T-QR3D-oy",
        "outputId": "8d04e65a-710d-422e-8bff-4e95f6dd23f1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58947df763764c0198b5db0df5bc885e",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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          },
          "metadata": {},
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        },
        {
          "data": {
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          },
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        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "# model_id='facebook/bart-base'\n",
        "model_id='google/flan-t5-base'\n",
        "# model_id='google/flan-t5-small'\n",
        "# model_id='lucadiliello/bart-small'\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id,\n",
        "                                              # quantization_config = BNB_NF4_CONFIG,\n",
        "                                              # device_map = \"auto\"\n",
        "                                              )\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id,\n",
        "                                          use_fast=True,\n",
        "                                          )\n",
        "\n",
        "# # config tokenizer: assign the eos_token to the pad_token\n",
        "# tokenizer.pad_token = tokenizer.eos_token\n",
        "# tokenizer.padding_side = \"right\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUSPGTf5D-oz",
        "outputId": "d60083bb-6603-4280-fb6e-eced37ee9088"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 1: Index = 10\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "  Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories. They allow to verify and replicate existing findings, and to link is connected results. The notion of self-play, albeit often cited in multiagent Reinforcement Learning, has never been grounded in a formal model. We present a formalized framework, with clearly defined assumptions, which encapsulates the meaning of self-play as abstracted from various existing self-play algorithms. This framework is framed as an approximation to a theoretical solution concept for multiagent training. On a simple environment, we qualitatively measure how well a subset of the captured self-play methods approximate this solution when paired with the famous PPO algorithm. We also provide insights on interpreting quantitative metrics of performance for self-play training. Our results indicate that, throughout training, various self-play definitions exhibit cyclic policy evolutions. \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "A Comparison of Self-Play Algorithms Under a Generalized Framework\n",
            "---------------------------------------------------------------------------------------------------\n",
            "OUTPUT OF THE MODEL\n",
            "A formalized framework for self-play training.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 2: Index = 15\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "  In this article, we develop and analyze a finite element method with the first family N\\'ed\\'elec elements of the lowest degree for solving a Maxwell interface problem modeled by a $\\mathbf{H}(\\text{curl})$-elliptic equation on unfitted meshes. To capture the jump conditions optimally, we construct and use $\\mathbf{H}(\\text{curl})$ immersed finite element (IFE) functions on interface elements while keep using the standard N\\'ed\\'elec functions on all the non-interface elements. We establish a few important properties for the IFE functions including the unisolvence according to the edge degrees of freedom, the exact sequence relating to the $H^1$ IFE functions and the optimal approximation capabilities. In order to achieve the optimal convergence rates, we employ a Petrov-Galerkin method in which the IFE functions are only used as the trial functions and the standard N\\'ed\\'elec functions are used as the test functions which can eliminate the non-conformity errors. We analyze the inf-sup conditions under certain conditions and show the optimal convergence rates which are also validated by numerical experiments. \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "Solving Two Dimensional H(curl)-elliptic Interface Systems with Optimal   Convergence On Unfitted Meshes\n",
            "---------------------------------------------------------------------------------------------------\n",
            "OUTPUT OF THE MODEL\n",
            "We develop and analyze a finite element method for solving a Maxwell interface problem modeled by a $\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "for i, index in enumerate(samples_to_check):\n",
        "    abstract = dataset['train'][index][INPUT_COL_NAME]\n",
        "    title = dataset['train'][index][OUTPUT_COL_NAME]\n",
        "\n",
        "    inputs = tokenizer(abstract, return_tensors='pt')\n",
        "    output = tokenizer.decode(\n",
        "        model.generate(\n",
        "            inputs[\"input_ids\"],\n",
        "            max_new_tokens=25,\n",
        "        )[0],\n",
        "        skip_special_tokens=True\n",
        "    )\n",
        "\n",
        "    print_input_outputs(i,\n",
        "                        index,\n",
        "                        input=abstract,\n",
        "                        label=title,\n",
        "                        model_output=output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLH8NA0JD-oz"
      },
      "source": [
        "## 2.1) Prompt Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJx916n0D-oz"
      },
      "source": [
        "Zero-Shot Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pj3jlOLRD-oz",
        "outputId": "ee90802b-b723-4b0a-a4aa-0b8c2ab06540"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 1: Index = 10\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "\n",
            "    Generate a title related to the given abstract:\n",
            "      Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories. They allow to verify and replicate existing findings, and to link is connected results. The notion of self-play, albeit often cited in multiagent Reinforcement Learning, has never been grounded in a formal model. We present a formalized framework, with clearly defined assumptions, which encapsulates the meaning of self-play as abstracted from various existing self-play algorithms. This framework is framed as an approximation to a theoretical solution concept for multiagent training. On a simple environment, we qualitatively measure how well a subset of the captured self-play methods approximate this solution when paired with the famous PPO algorithm. We also provide insights on interpreting quantitative metrics of performance for self-play training. Our results indicate that, throughout training, various self-play definitions exhibit cyclic policy evolutions. \n",
            "\n",
            "    Generated Title:\n",
            "    \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "A Comparison of Self-Play Algorithms Under a Generalized Framework\n",
            "---------------------------------------------------------------------------------------------------\n",
            "OUTPUT OF THE MODEL\n",
            "Self-play in Multiagent Reinforcement Learning\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 2: Index = 15\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "\n",
            "    Generate a title related to the given abstract:\n",
            "      In this article, we develop and analyze a finite element method with the first family N\\'ed\\'elec elements of the lowest degree for solving a Maxwell interface problem modeled by a $\\mathbf{H}(\\text{curl})$-elliptic equation on unfitted meshes. To capture the jump conditions optimally, we construct and use $\\mathbf{H}(\\text{curl})$ immersed finite element (IFE) functions on interface elements while keep using the standard N\\'ed\\'elec functions on all the non-interface elements. We establish a few important properties for the IFE functions including the unisolvence according to the edge degrees of freedom, the exact sequence relating to the $H^1$ IFE functions and the optimal approximation capabilities. In order to achieve the optimal convergence rates, we employ a Petrov-Galerkin method in which the IFE functions are only used as the trial functions and the standard N\\'ed\\'elec functions are used as the test functions which can eliminate the non-conformity errors. We analyze the inf-sup conditions under certain conditions and show the optimal convergence rates which are also validated by numerical experiments. \n",
            "\n",
            "    Generated Title:\n",
            "    \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "Solving Two Dimensional H(curl)-elliptic Interface Systems with Optimal   Convergence On Unfitted Meshes\n",
            "---------------------------------------------------------------------------------------------------\n",
            "OUTPUT OF THE MODEL\n",
            "Optimal convergence rates for the Maxwell interface problem\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "for i, index in enumerate(samples_to_check):\n",
        "    abstract = dataset['train'][index][INPUT_COL_NAME]\n",
        "    title = dataset['train'][index][OUTPUT_COL_NAME]\n",
        "\n",
        "    prompt=f'''\n",
        "    Generate a title related to the given abstract:\n",
        "    {abstract}\n",
        "\n",
        "    Generated Title:\n",
        "    '''\n",
        "\n",
        "    inputs=tokenizer(prompt,return_tensors='pt')\n",
        "    output=tokenizer.decode(\n",
        "        model.generate(\n",
        "            inputs['input_ids'],\n",
        "            max_new_tokens=512,\n",
        "        )[0],\n",
        "        skip_special_tokens=True\n",
        "    )\n",
        "\n",
        "    print_input_outputs(i,\n",
        "                        index,\n",
        "                        input=prompt,\n",
        "                        label=title,\n",
        "                        model_output=output)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CnQcMs_RD-o0"
      },
      "source": [
        "One Shot Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "MxQ__6x4D-o0"
      },
      "outputs": [],
      "source": [
        "def create_one_shot_prompt(base_given_dialogues, test_dialgoue_index):\n",
        "    prompt='There is an example dialogue with its summary:'\n",
        "\n",
        "    # add base samples in prompt firstly\n",
        "    for index in base_given_dialogues:\n",
        "      abstract = dataset['test'][index][INPUT_COL_NAME]\n",
        "      title = dataset['test'][index][OUTPUT_COL_NAME]\n",
        "\n",
        "      prompt+=f\"\"\"\n",
        "              Given abstract:\n",
        "              {abstract}\n",
        "\n",
        "              The title of the abstract given above:\n",
        "              {title}\n",
        "              \"\"\"\n",
        "\n",
        "    # add test abstract\n",
        "    test_abstract = dataset['test'][test_dialgoue_index][INPUT_COL_NAME]\n",
        "    prompt+=f\"\"\"\n",
        "              Generate a title related to the given abstract:\n",
        "              {test_abstract}\n",
        "\n",
        "              Generated title:\n",
        "              \"\"\"\n",
        "    return prompt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZMX00cjD-o0",
        "outputId": "f02bed5e-656a-41c0-a58a-c06442bc839d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (554 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There is an example dialogue with its summary:\n",
            "              Given abstract:\n",
            "                IEEE 802.11e standard defines two Medium Access Control (MAC) functions to support Quality of Service (QoS) for wireless local area networks: Enhanced Distributed Channel Access (EDCA) and HCF Controlled Channel Access (HCCA). EDCA provides fair prioritized QoS support while HCCA guarantees parameterized QoS for the traffics with rigid QoS requirements. The latter shows higher QoS provisioning with Constant Bit Rate (CBR) traffics. However, it does not efficiently cope with the fluctuation of the Variable Bit Rate (VBR) video streams since its reference scheduler generates a schedule based on the mean characteristics of the traffic. Scheduling based on theses characteristics is not always accurate as these tra_cs show high irregularity over the time. In this paper, we propose an enhancement on the HCCA polling mechanism to address the problem of scheduling pre-recorded VBR video streams. Our approach enhances the polling mechanism by feed-backing the arrival time of the subsequent video frame of the uplink traffic obtained through cross-layering approach. Simulation experiments have been conducted on several publicly available video traces in order to show the efficiency of our mechanism. The simulation results reveal the efficiency of the proposed mechanism in providing less delay and high throughput with conserving medium channel through minimizing the number of Null-Frames caused by wasted polls \n",
            "\n",
            "              The title of the abstract given above:\n",
            "              Feasible HCCA Polling Mechanism for Video Transmission in IEEE 802.11e   WLANs\n",
            "              \n",
            "              Generate a title related to the given abstract:\n",
            "                In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning. \n",
            "\n",
            "              Generated title:\n",
            "              \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n",
            "Sample 2: Index = 15\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT:\n",
            "There is an example dialogue with its summary:\n",
            "              Given abstract:\n",
            "                IEEE 802.11e standard defines two Medium Access Control (MAC) functions to support Quality of Service (QoS) for wireless local area networks: Enhanced Distributed Channel Access (EDCA) and HCF Controlled Channel Access (HCCA). EDCA provides fair prioritized QoS support while HCCA guarantees parameterized QoS for the traffics with rigid QoS requirements. The latter shows higher QoS provisioning with Constant Bit Rate (CBR) traffics. However, it does not efficiently cope with the fluctuation of the Variable Bit Rate (VBR) video streams since its reference scheduler generates a schedule based on the mean characteristics of the traffic. Scheduling based on theses characteristics is not always accurate as these tra_cs show high irregularity over the time. In this paper, we propose an enhancement on the HCCA polling mechanism to address the problem of scheduling pre-recorded VBR video streams. Our approach enhances the polling mechanism by feed-backing the arrival time of the subsequent video frame of the uplink traffic obtained through cross-layering approach. Simulation experiments have been conducted on several publicly available video traces in order to show the efficiency of our mechanism. The simulation results reveal the efficiency of the proposed mechanism in providing less delay and high throughput with conserving medium channel through minimizing the number of Null-Frames caused by wasted polls \n",
            "\n",
            "              The title of the abstract given above:\n",
            "              Feasible HCCA Polling Mechanism for Video Transmission in IEEE 802.11e   WLANs\n",
            "              \n",
            "              Generate a title related to the given abstract:\n",
            "                In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning. \n",
            "\n",
            "              Generated title:\n",
            "              \n",
            "---------------------------------------------------------------------------------------------------\n",
            "DATASET ORIGINAL LABEL:\n",
            "Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space\n",
            "---------------------------------------------------------------------------------------------------\n",
            "OUTPUT OF THE MODEL\n",
            "Hybrid Agent-Critic Architecture for Parameterized Action Space Reinforcement Learning\n",
            "---------------------------------------------------------------------------------------------------\n",
            "-------------------------------------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "base_given_dialogues=[10]\n",
        "test_dialgoue_index=20\n",
        "\n",
        "# input (with one shot prompt)\n",
        "one_shot_prompt = create_one_shot_prompt(base_given_dialogues, test_dialgoue_index)\n",
        "print(one_shot_prompt)\n",
        "print(\"\\n\\n\\n\")\n",
        "\n",
        "# label\n",
        "title_new = dataset['test'][test_dialgoue_index][OUTPUT_COL_NAME]\n",
        "\n",
        "# model output\n",
        "inputs=tokenizer(one_shot_prompt,return_tensors='pt')\n",
        "output=tokenizer.decode(\n",
        "    model.generate(\n",
        "        inputs['input_ids'],\n",
        "        max_new_tokens=512,\n",
        "    )[0],\n",
        "    skip_special_tokens=True\n",
        ")\n",
        "\n",
        "print_input_outputs(i,\n",
        "                    index,\n",
        "                    input=one_shot_prompt,\n",
        "                    label=title_new,\n",
        "                    model_output=output)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QJ_Ex4xgD-o0"
      },
      "outputs": [],
      "source": [
        "# summary = dataset['test'][test_dialgoue_index]['summary']\n",
        "\n",
        "# inputs=tokenizer(one_shot_prompt,return_tensors='pt')\n",
        "# output=tokenizer.decode(\n",
        "#     model.generate(\n",
        "#         inputs['input_ids'],\n",
        "#         max_new_tokens=200,\n",
        "#     )[0],\n",
        "#     skip_special_tokens=True\n",
        "# )\n",
        "# print(dash_line)\n",
        "# print(f'BASELINE HUMAN SUMMARY:\\n{summary}\\n')\n",
        "# print(dash_line)\n",
        "# print(f'MODEL GENERATION - ONE SHOT:\\n{output}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "to6l1kWbD-o0"
      },
      "source": [
        "# 3) Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0s7T5FpD-o0"
      },
      "source": [
        "## 3.1) Fine Tuning with PEFT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RO-wIqcHD-o0",
        "outputId": "07c05392-3557-4dbd-bbe4-29f3536b609e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 247577856\n",
            "all params: 247577856\n",
            "percentage of trainable params: 100.0\n"
          ]
        }
      ],
      "source": [
        "def trainable_model_params(model):\n",
        "  trainable_params=0\n",
        "  all_params=0\n",
        "  for _,param in model.named_parameters():\n",
        "    all_params+=param.numel()\n",
        "    if param.requires_grad:\n",
        "      trainable_params+=param.numel()\n",
        "  return f\"trainable params: {trainable_params}\\nall params: {all_params}\\npercentage of trainable params: {100*trainable_params/all_params}\"\n",
        "print(trainable_model_params(model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6TcyaIpD-o0"
      },
      "source": [
        "LoRA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gq6I9O4P4wYU"
      },
      "outputs": [],
      "source": [
        "# # # use significantly less GPU memory\n",
        "# # # activation checkpointing: freeze huge memory with small decrease in training speed\n",
        "# # model.gradient_checkpointing_enable()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kl-QCnj94wYU"
      },
      "outputs": [],
      "source": [
        "# # from peft import prepare_model_for_kbit_training\n",
        "\n",
        "# # # add training adapters\n",
        "# # model = prepare_model_for_kbit_training(model, use_gradient_checkpointing = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLe3rnWgD-o1",
        "outputId": "cbe1ccea-a2b7-471b-c727-86559583d381"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 884736\n",
            "all params: 248462592\n",
            "percentage of trainable params: 0.3560841867092814\n"
          ]
        }
      ],
      "source": [
        "LORA_R = 8\n",
        "LORA_ALPHA = 64         # scaling coefficient for LoRA\n",
        "LORA_DROPOUT = 0.01\n",
        "\n",
        "lora_config=LoraConfig(\n",
        "    r = LORA_R,\n",
        "    lora_alpha = LORA_ALPHA,\n",
        "    # target_modules = [\"q_proj\",\"v_proj\"],\n",
        "    target_modules=[\"q\", \"v\"],\n",
        "    lora_dropout = LORA_DROPOUT,\n",
        "    bias = \"lora_only\",                     # check with \"none\"\n",
        "    task_type = TaskType.SEQ_2_SEQ_LM\n",
        ")\n",
        "\n",
        "# model.enable_input_require_grads()\n",
        "\n",
        "peft_model=get_peft_model(model,lora_config)\n",
        "\n",
        "print(trainable_model_params(peft_model))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZCRDWVRD-o1"
      },
      "source": [
        "Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "8ce81845600b4a44a14bfeabb4932ba7",
            "7fc784bd9aff4852bb54dd8193f05977",
            "5debd8bc2fe346ff96bef252a7649e59",
            "9a462df8867c4291ba45e58a7280a800",
            "b6841cdfdfec4633ab241fe463ed17d2",
            "366b57cf44194424b694b8b43292b58a",
            "7a5926f0cef64813a81cbdbca6a5c5dd",
            "276dca4d954b49d791b355890d9d2ddc",
            "df2f9a9354304e89bb499a034ae48c5c",
            "4881c947f99348b4b1182be6e9821ebf",
            "d2de843408404d1fb41ae9ad34e7d884",
            "4469b67f31f64e9e92bfb23972da0804",
            "0dbebc261c0f4622851b92b4d67c9b4f",
            "c787454dc6c04089949694b6246e6f8d",
            "33fea58e591f49ebbc3d417493e4c981",
            "ac7062a0f1c34fcca655c9e0383325e6",
            "8ff052e4ec3c43ba9ecca9b1cbd7b49a",
            "0229100057064a69b029b62df9edd2a6",
            "0221e4ef8ed941fdaac55c795be222f3",
            "4fb4f33440a8439989ba0b78c880bc2b",
            "30bec2d056ab45b689a1153262c098bc",
            "93bb0e0a7c4d42438c099531ab25559c",
            "0ff85a3edf354e2ea0e9c65b2e2d6da3",
            "be0a8e2a39b446dc9d6ffae7b915158a",
            "6656980b8f3d4a7383a9a8ae7759a396",
            "aa87e3720cbf4394b2aed08b3aad2878",
            "8e35488d41a94969b69778ca8b03870c",
            "df0a8410440f41d6a3ad421d8fb78bfd",
            "6fc97e0d6f50492c885d9634b01832eb",
            "224f6bc406b04b1e9772a6020c604d92",
            "ffcbee67aa6242f79159025235c5a0e1",
            "5406873880e849a7a1bde220d3171fe2",
            "1fee8640344841cea99bd6b28a89c9b3"
          ]
        },
        "id": "hijw1BIsD-o1",
        "outputId": "fb2747f7-bc0f-43a8-bdaa-6c115bddc36f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ce81845600b4a44a14bfeabb4932ba7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/6000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4469b67f31f64e9e92bfb23972da0804",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ff85a3edf354e2ea0e9c65b2e2d6da3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/250 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def tokenize_function(example):\n",
        "    START_prompt = 'Generate a relevant title related to the given abstract:'\n",
        "    END_prompt = '\\n\\nGenerated Title: '\n",
        "\n",
        "    prompt = [START_prompt + abstract + END_prompt for abstract in example[INPUT_COL_NAME]]\n",
        "    example['input_ids'] = tokenizer(prompt,\n",
        "                                    padding=\"max_length\",\n",
        "                                    truncation=True,\n",
        "                                    return_tensors=\"pt\").input_ids\n",
        "\n",
        "    example['labels'] = tokenizer(example[\"title\"],\n",
        "                                  padding=\"max_length\",\n",
        "                                  truncation=True,\n",
        "                                  return_tensors=\"pt\").input_ids\n",
        "\n",
        "    return example\n",
        "\n",
        "\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
        "tokenized_datasets = tokenized_datasets.remove_columns(['title', 'abstract','Unnamed: 0', 'categories'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ll5tQVLFD-o1",
        "outputId": "8b8e6c7e-e792-46c4-fda4-88c658b44f87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['input_ids', 'labels'],\n",
            "        num_rows: 6000\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['input_ids', 'labels'],\n",
            "        num_rows: 1000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['input_ids', 'labels'],\n",
            "        num_rows: 250\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(tokenized_datasets)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2falxsQbD-o1"
      },
      "source": [
        "PEFT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "3MZvqLUs4wYV"
      },
      "outputs": [],
      "source": [
        "output_model_name = \"t5\"\n",
        "output_config_option = \"peft-lora\"\n",
        "output_task = \"title-generation\"\n",
        "output_ver = \"v1-1\"\n",
        "\n",
        "OUTPUT_DIR = f'./{output_model_name}-{output_config_option}-{output_task}-{output_ver}'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "B_8FUMRyD-o1"
      },
      "outputs": [],
      "source": [
        "# MAIN PARAMS\n",
        "NUM_EPOCHS = 6\n",
        "L_RATE = 2e-4\n",
        "TRAIN_OPTIMIZER = \"paged_adamw_32bit\"      # \"paged_adamw_32bit\"\n",
        "\n",
        "# OTHER PARAMS\n",
        "EVAL_STEPS = 0.2\n",
        "LR_SCHED_TYPE = \"cosine\"\n",
        "\n",
        "peft_training_args = TrainingArguments(\n",
        "    output_dir = OUTPUT_DIR,\n",
        "\n",
        "    num_train_epochs = NUM_EPOCHS,\n",
        "    per_device_train_batch_size = 4,\n",
        "    gradient_accumulation_steps = 2, # accumulating gradients over several batches\n",
        "\n",
        "    learning_rate = L_RATE,\n",
        "    optim = TRAIN_OPTIMIZER,\n",
        "    lr_scheduler_type = LR_SCHED_TYPE,\n",
        "    # max_grad_norm = 0.4,             # for gradient clipping\n",
        "    # warmup_ratio = 0.02,             # ratio of total training steps used in [0, LR]\n",
        "\n",
        "    logging_steps = 4,\n",
        "    do_train = True,\n",
        "    do_eval = True,\n",
        "    evaluation_strategy = \"epoch\",      # set \"steps\" to see each val step\n",
        "    # eval_steps = 4,\n",
        "    # logging_strategy= \"epoch\",\n",
        "\n",
        "    logging_dir = f\"{OUTPUT_DIR}/logs\",\n",
        "    # save_strategy = \"epoch\",\n",
        "\n",
        "    # load_best_model_at_end = True,\n",
        "    # save_total_limit=2,\n",
        "    metric_for_best_model = 'eval_loss',\n",
        "    save_strategy = \"epoch\"\n",
        "\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "\n",
        "peft_trainer = Trainer(\n",
        "    model = peft_model,\n",
        "    args = peft_training_args,\n",
        "    train_dataset = tokenized_datasets[\"train\"],\n",
        "    eval_dataset = tokenized_datasets['validation'],\n",
        "    # compute_metrics=compute_metrics\n",
        "    # neftune_noise_alpha=5,             # use NEFTune\n",
        "    # data_collator=data_collator,\n",
        "    # max_seq_length=1024,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "V7Vx3waQD-o1",
        "outputId": "2c0ebf3d-f75e-4331-aa71-521eb9ab8a32"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='4500' max='4500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [4500/4500 2:43:08, Epoch 6/6]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.110100</td>\n",
              "      <td>0.082300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.089400</td>\n",
              "      <td>0.077186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.082500</td>\n",
              "      <td>0.075563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.094900</td>\n",
              "      <td>0.074766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.086000</td>\n",
              "      <td>0.074481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.099000</td>\n",
              "      <td>0.074469</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "T_START_TRAIN = time.time()\n",
        "peft_trainer.train()\n",
        "T_END_TRAIN = time.time()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fDvxBueuOt1l"
      },
      "outputs": [],
      "source": [
        "# torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwVX72SID-o2",
        "outputId": "e7df6019-f0bd-4635-de1a-36192190a6ad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('./t5-lora-title-generator-checkpoint-1-1/tokenizer_config.json',\n",
              " './t5-lora-title-generator-checkpoint-1-1/special_tokens_map.json',\n",
              " './t5-lora-title-generator-checkpoint-1-1/tokenizer.json')"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "PATH_peft_model=\"./t5-lora-title-generator-checkpoint-1-1\"\n",
        "\n",
        "peft_trainer.model.save_pretrained(PATH_peft_model)\n",
        "tokenizer.save_pretrained(PATH_peft_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "t7jCJ5EtD-o2"
      },
      "outputs": [],
      "source": [
        "# Load lora model and base LLM model together\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(model,\n",
        "                                       PATH_peft_model, #PATH_peft_model\n",
        "                                       torch_dtype=torch.bfloat16,\n",
        "                                       is_trainable=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3ZRSACrD-o2"
      },
      "source": [
        "Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7eGdmlVD-o2",
        "outputId": "f66c66eb-2898-47c8-a423-ecacffa799ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Generate a title related to the given abstract:\n",
            "  We develop a hyperparameter optimisation algorithm, Automated Budget Constrained Training (AutoBCT), which balances the quality of a model with the computational cost required to tune it. The relationship between hyperparameters, model quality and computational cost must be learnt and this learning is incorporated directly into the optimisation problem. At each training epoch, the algorithm decides whether to terminate or continue training, and, in the latter case, what values of hyperparameters to use. This decision weighs optimally potential improvements in the quality with the additional training time and the uncertainty about the learnt quantities. The performance of our algorithm is verified on a number of machine learning problems encompassing random forests and neural networks. Our approach is rooted in the theory of Markov decision processes with partial information and we develop a numerical method to compute the value function and an optimal strategy. \n",
            "\n",
            "Generated Title:\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Label:\n",
            "Automatic model training under restrictive time constraints\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Original Model Summary:\n",
            "Automated Budget Constrained Training for Data Quality optimization\n",
            "---------------------------------------------------------------------------------------------------\n",
            "PEFT Summary:\n",
            "Automated Budget Constrained Training in Hyperparameter Optimal Learning\n",
            "---------------------------------------------------------------------------------------------------\n",
            "PEFT Summary No Token len:\n",
            "The Prognosis in the Estimation of Hyperparameters: A Neural Neural Network Optimising Algorithm for Superfast Learning and Cost Effective Encoding\n"
          ]
        }
      ],
      "source": [
        "TEST_INDEX = 22\n",
        "\n",
        "abstract = dataset['test'][TEST_INDEX][INPUT_COL_NAME]\n",
        "title = dataset['test'][TEST_INDEX][OUTPUT_COL_NAME]\n",
        "\n",
        "prompt = f\"\"\"\n",
        "Generate a title related to the given abstract:\n",
        "{abstract}\n",
        "\n",
        "Generated Title:\n",
        "\"\"\"\n",
        "inputs = tokenizer(prompt, return_tensors='pt')\n",
        "\n",
        "output_peft = tokenizer.decode(\n",
        "    peft_model.generate(\n",
        "        input_ids = inputs['input_ids'].cuda(),\n",
        "        generation_config = GenerationConfig(max_new_tokens=70,\n",
        "                                             temperatue=1,\n",
        "                                             do_sample=True),\n",
        "    )[0],\n",
        "    skip_special_tokens = True\n",
        ")\n",
        "\n",
        "output_base_model = tokenizer.decode(\n",
        "    model.generate(\n",
        "        input_ids = inputs['input_ids'].cuda(),\n",
        "        generation_config = GenerationConfig(max_new_tokens=70,\n",
        "                                             temperature=1,\n",
        "                                             do_sample=True),\n",
        "    )[0],\n",
        "    skip_special_tokens = True\n",
        ")\n",
        "\n",
        "output_base_model_2 = tokenizer.decode(\n",
        "    model.generate(\n",
        "        input_ids = inputs['input_ids'].cuda(),\n",
        "        generation_config = GenerationConfig( max_new_tokens=1024,\n",
        "                                              temperature=1,\n",
        "                                              do_sample=True),\n",
        "    )[0],\n",
        "    skip_special_tokens = True\n",
        ")\n",
        "\n",
        "\n",
        "print(prompt)\n",
        "print(seperator_print_short)\n",
        "\n",
        "print('Label:')\n",
        "print(title)\n",
        "print(seperator_print_short)\n",
        "\n",
        "print('Original Model Summary:')\n",
        "print(output_base_model)\n",
        "print(seperator_print_short)\n",
        "\n",
        "print('PEFT Summary:')\n",
        "print(output_peft)\n",
        "print(seperator_print_short)\n",
        "\n",
        "print('PEFT Summary No Token len:')\n",
        "print(output_base_model_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pa2-c04FYxEK"
      },
      "outputs": [],
      "source": [
        "def get_decoded_prediction(abstract, max_target_length=40):\n",
        "\n",
        "  inputs = tokenizer(abstract, return_tensors=\"pt\").to(device)\n",
        "  outputs = model.generate(**inputs, max_new_tokens=max_target_length)\n",
        "  return tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "result_model_generation = get_decoded_prediction(abstract)\n",
        "result_model_generation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgofzx54D-o2"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wwVk7xl-s8W"
      },
      "source": [
        "# 4) Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7mRFWMX-vEX",
        "outputId": "240a86e4-4640-41ba-9373-a2f0492f4361"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeftModelForSeq2SeqLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): T5ForConditionalGeneration(\n",
              "      (shared): Embedding(32128, 768)\n",
              "      (encoder): T5Stack(\n",
              "        (embed_tokens): Embedding(32128, 768)\n",
              "        (block): ModuleList(\n",
              "          (0): T5Block(\n",
              "            (layer): ModuleList(\n",
              "              (0): T5LayerSelfAttention(\n",
              "                (SelfAttention): T5Attention(\n",
              "                  (q): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (v): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (relative_attention_bias): Embedding(32, 12)\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (1): T5LayerFF(\n",
              "                (DenseReluDense): T5DenseGatedActDense(\n",
              "                  (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "                  (dropout): Dropout(p=0.1, inplace=False)\n",
              "                  (act): NewGELUActivation()\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1-11): 11 x T5Block(\n",
              "            (layer): ModuleList(\n",
              "              (0): T5LayerSelfAttention(\n",
              "                (SelfAttention): T5Attention(\n",
              "                  (q): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (v): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (1): T5LayerFF(\n",
              "                (DenseReluDense): T5DenseGatedActDense(\n",
              "                  (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "                  (dropout): Dropout(p=0.1, inplace=False)\n",
              "                  (act): NewGELUActivation()\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (final_layer_norm): T5LayerNorm()\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (decoder): T5Stack(\n",
              "        (embed_tokens): Embedding(32128, 768)\n",
              "        (block): ModuleList(\n",
              "          (0): T5Block(\n",
              "            (layer): ModuleList(\n",
              "              (0): T5LayerSelfAttention(\n",
              "                (SelfAttention): T5Attention(\n",
              "                  (q): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (v): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (relative_attention_bias): Embedding(32, 12)\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (1): T5LayerCrossAttention(\n",
              "                (EncDecAttention): T5Attention(\n",
              "                  (q): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (v): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (2): T5LayerFF(\n",
              "                (DenseReluDense): T5DenseGatedActDense(\n",
              "                  (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "                  (dropout): Dropout(p=0.1, inplace=False)\n",
              "                  (act): NewGELUActivation()\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1-11): 11 x T5Block(\n",
              "            (layer): ModuleList(\n",
              "              (0): T5LayerSelfAttention(\n",
              "                (SelfAttention): T5Attention(\n",
              "                  (q): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (v): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (1): T5LayerCrossAttention(\n",
              "                (EncDecAttention): T5Attention(\n",
              "                  (q): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "                  (v): lora.Linear(\n",
              "                    (base_layer): Linear(in_features=768, out_features=768, bias=False)\n",
              "                    (lora_dropout): ModuleDict(\n",
              "                      (default): Dropout(p=0.01, inplace=False)\n",
              "                    )\n",
              "                    (lora_A): ModuleDict(\n",
              "                      (default): Linear(in_features=768, out_features=8, bias=False)\n",
              "                    )\n",
              "                    (lora_B): ModuleDict(\n",
              "                      (default): Linear(in_features=8, out_features=768, bias=False)\n",
              "                    )\n",
              "                    (lora_embedding_A): ParameterDict()\n",
              "                    (lora_embedding_B): ParameterDict()\n",
              "                  )\n",
              "                  (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (2): T5LayerFF(\n",
              "                (DenseReluDense): T5DenseGatedActDense(\n",
              "                  (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "                  (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "                  (dropout): Dropout(p=0.1, inplace=False)\n",
              "                  (act): NewGELUActivation()\n",
              "                )\n",
              "                (layer_norm): T5LayerNorm()\n",
              "                (dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (final_layer_norm): T5LayerNorm()\n",
              "        (dropout): Dropout(p=0.1, inplace=False)\n",
              "      )\n",
              "      (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "peft_model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "MYLy3AgJ_KNc"
      },
      "outputs": [],
      "source": [
        "metric = evaluate.load(\"rouge\")\n",
        "\n",
        "def get_decoded_pred_samples(sample, max_target_length=40):\n",
        "    ## predictions\n",
        "    # generate summary\n",
        "    # outputs = model.generate(input_ids=sample[\"input_ids\"].unsqueeze(0).cuda(),\n",
        "    #                          do_sample=True,\n",
        "    #                         #  top_p=0.9,\n",
        "    #                          max_new_tokens=max_target_length)\n",
        "\n",
        "    outputs = peft_model.generate(input_ids=sample[\"input_ids\"].unsqueeze(0).cuda(),\n",
        "                             do_sample=True,\n",
        "                            #  top_p=0.9,\n",
        "                             max_new_tokens=max_target_length)\n",
        "\n",
        "\n",
        "\n",
        "    outputs = model.generate(input_ids=sample[\"input_ids\"].unsqueeze(0).cuda(),\n",
        "                             do_sample=True,\n",
        "                            #  top_p=0.9,\n",
        "                             max_new_tokens=max_target_length)\n",
        "\n",
        "\n",
        "    prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(),\n",
        "                                  skip_special_tokens=True)\n",
        "\n",
        "    # Replace -100 in the labels which we can't decode them\n",
        "    labels = np.where(sample['labels'] != -100,\n",
        "                      sample['labels'],\n",
        "                      tokenizer.pad_token_id)\n",
        "\n",
        "\n",
        "    ## samples\n",
        "    # decode eval sample\n",
        "    labels = tokenizer.decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # Some simple post-processing\n",
        "    return prediction, labels\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wD25dXjh_uev",
        "outputId": "6a11a780-a36e-4dc0-a846-9bacfcb3817f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 250/250 [05:28<00:00,  1.31s/it]\n"
          ]
        }
      ],
      "source": [
        "test_dataset = tokenized_datasets['test'].with_format(\"torch\")\n",
        "\n",
        "df_list_elements = []\n",
        "\n",
        "T_START_PRED = time.time()\n",
        "# run predictions\n",
        "predictions, references, = [] , []\n",
        "for sample in tqdm(test_dataset):\n",
        "    pred, y = get_decoded_pred_samples(sample, max_target_length=60)\n",
        "    predictions.append(pred)\n",
        "    references.append(y)\n",
        "\n",
        "    df_list_elements.append([sample, y, pred])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SLpmk1OF97U",
        "outputId": "7dc5a225-1739-444b-8447-198d99efc376"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Learning a Holomorphic Theory of Adversarial Examples\n",
            "Using Ambient Tactical Deception in Email Confusing Human Adversarial Intimidation to Motivate Them\n",
            "An audio tokenization framework based in mel-spectrograms: A review of the findings\n",
            "Cosine Similarity of Visual Features for Large Data\n",
            "JANET, a forget-gate-only for long short-term memory: a chrono-initialized gate\n"
          ]
        }
      ],
      "source": [
        "for pred in predictions[:5]:\n",
        "  print(pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "H1h8lqvSIV5j"
      },
      "outputs": [],
      "source": [
        "# # string manipulation for predictions (delete prompt parts)\n",
        "predictions_v1 = [pred.replace(\"Generate a relevant title related to the given abstracte: \", \" \") for pred in predictions]\n",
        "predictions_v2 = [pred.strip() for pred in predictions_v1]\n",
        "# predictions_v2 = [pred.strip() for pred in predictions]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cv27G-9iF5zY",
        "outputId": "59ea6f63-0b41-48b5-f501-6e4768310caf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rogue1: 34.282341%\n",
            "rouge2: 15.487016%\n",
            "rougeL: 29.761828%\n",
            "rougeLsum: 29.682001%\n"
          ]
        }
      ],
      "source": [
        "rogue = metric.compute(predictions=predictions,\n",
        "                       references=references,\n",
        "                       use_stemmer=True)\n",
        "T_END_PRED = time.time()\n",
        "\n",
        "\n",
        "print(f\"Rogue1: {rogue['rouge1']* 100:2f}%\")\n",
        "print(f\"rouge2: {rogue['rouge2']* 100:2f}%\")\n",
        "print(f\"rougeL: {rogue['rougeL']* 100:2f}%\")\n",
        "print(f\"rougeLsum: {rogue['rougeLsum']* 100:2f}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "vDWMqOv9G5L4",
        "outputId": "b5506476-e558-4f27-e72f-0c22303ec462"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b10d0d50-bfdf-402c-9538-3b0be56dcebe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "      <th>prediction</th>\n",
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              "      <th>0</th>\n",
              "      <td>An Analytic Framework for Robust Training of A...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Manipulation of Perceived Politeness in a Web-...</td>\n",
              "      <td>Using Ambient Tactical Deception in Email Conf...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Discrete Audio Representation as an Alternativ...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A Fast Content-Based Image Retrieval Method Us...</td>\n",
              "      <td>Cosine Similarity of Visual Features for Large...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>245</th>\n",
              "      <td>Tracking Initiative in Collaborative Dialogue ...</td>\n",
              "      <td>Estimates for the Transition Transition betwee...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>Convective transport in nanofluids: the statio...</td>\n",
              "      <td>Matrix of convective transport in nanofluids</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>Real-time Full-stack Traffic Scene Perception ...</td>\n",
              "      <td>A framework for traffic scene perception with ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>Logical Step-Indexed Logical Relations</td>\n",
              "      <td>Logical Relationality Modeling for Binary Step...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>Orthogonal-Pad'e Activation Functions: Trainab...</td>\n",
              "      <td>Hermite-Pade in Advanced Deep Learning</td>\n",
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            ],
            "text/plain": [
              "                                                 label  \\\n",
              "0    An Analytic Framework for Robust Training of A...   \n",
              "1    Manipulation of Perceived Politeness in a Web-...   \n",
              "2    Discrete Audio Representation as an Alternativ...   \n",
              "3    A Fast Content-Based Image Retrieval Method Us...   \n",
              "4    The unreasonable effectiveness of the forget gate   \n",
              "..                                                 ...   \n",
              "245  Tracking Initiative in Collaborative Dialogue ...   \n",
              "246  Convective transport in nanofluids: the statio...   \n",
              "247  Real-time Full-stack Traffic Scene Perception ...   \n",
              "248             Logical Step-Indexed Logical Relations   \n",
              "249  Orthogonal-Pad'e Activation Functions: Trainab...   \n",
              "\n",
              "                                            prediction  \n",
              "0    Learning a Holomorphic Theory of Adversarial E...  \n",
              "1    Using Ambient Tactical Deception in Email Conf...  \n",
              "2    An audio tokenization framework based in mel-s...  \n",
              "3    Cosine Similarity of Visual Features for Large...  \n",
              "4    JANET, a forget-gate-only for long short-term ...  \n",
              "..                                                 ...  \n",
              "245  Estimates for the Transition Transition betwee...  \n",
              "246       Matrix of convective transport in nanofluids  \n",
              "247  A framework for traffic scene perception with ...  \n",
              "248  Logical Relationality Modeling for Binary Step...  \n",
              "249             Hermite-Pade in Advanced Deep Learning  \n",
              "\n",
              "[250 rows x 2 columns]"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_model_results = pd.DataFrame(df_list_elements,\n",
        "                                columns=[\"sample\", \"label\", \"prediction\"])\n",
        "\n",
        "df_model_results = df_model_results.drop('sample', axis=1)\n",
        "df_model_results['prediction'] = df_model_results['prediction'].apply(lambda x: x.replace(\"Generate a relevant title related to the given abstract: \", \" \"))\n",
        "df_model_results['prediction'] = df_model_results['prediction'].apply(lambda x: x.replace(\"/\", \"\"))\n",
        "df_model_results['prediction'] = df_model_results['prediction'].apply(lambda x: x.replace(\"_\", \"\"))\n",
        "df_model_results['prediction'] = df_model_results['prediction'].apply(lambda x: x.strip())\n",
        "df_model_results.to_csv('t5_6000_cs_abs_test_results_1.csv')\n",
        "df_model_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TQEA7UBfDFdu",
        "outputId": "6c45f31c-eed8-4840-ce0b-fcebaac3dfb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "time for training: 9793.74146771431\n",
            "time for prediction: 339.44929242134094\n",
            "lora r: 8\n",
            "lora alpha: 64\n",
            "lora alpha: 0.01\n",
            "Len Training: (6000, 2)\n",
            "Len Validation: (1000, 2)\n",
            "Len Test: (250, 2)\n"
          ]
        }
      ],
      "source": [
        "print(\"time for training:\",T_END_TRAIN - T_START_TRAIN)\n",
        "print(\"time for prediction:\",T_END_PRED - T_START_PRED)\n",
        "print(\"lora r:\",LORA_R)\n",
        "print(\"lora alpha:\",LORA_ALPHA)\n",
        "print(\"lora alpha:\",LORA_DROPOUT)\n",
        "print(f\"Len Training: {tokenized_datasets['train'].shape}\")\n",
        "print(f\"Len Validation: {tokenized_datasets['validation'].shape}\")\n",
        "print(f\"Len Test: {tokenized_datasets['test'].shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "LNjNw0FOFFsR",
        "outputId": "b719afc2-d6df-43e5-de20-8a0a0b52039f"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>learning_rate</th>\n",
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              "      <th>step</th>\n",
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              "      <th>0</th>\n",
              "      <td>44.2316</td>\n",
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              "      <td>1.999965e-04</td>\n",
              "      <td>0.02</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31.3713</td>\n",
              "      <td>1.999938e-04</td>\n",
              "      <td>0.02</td>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28.1403</td>\n",
              "      <td>1.999903e-04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1127</th>\n",
              "      <td>0.0894</td>\n",
              "      <td>1.559637e-09</td>\n",
              "      <td>5.99</td>\n",
              "      <td>4492</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>0.0869</td>\n",
              "      <td>3.899100e-10</td>\n",
              "      <td>5.99</td>\n",
              "      <td>4496</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1129</th>\n",
              "      <td>0.0990</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>4500</td>\n",
              "      <td>0.074469</td>\n",
              "      <td>107.3013</td>\n",
              "      <td>9.32</td>\n",
              "      <td>1.165</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1131</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9793.0337</td>\n",
              "      <td>3.676</td>\n",
              "      <td>0.46</td>\n",
              "      <td>2.474911e+16</td>\n",
              "      <td>0.385062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1132 rows × 13 columns</p>\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",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_trainer_logs')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "      const buttonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_trainer_logs');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         loss  learning_rate  epoch  step  eval_loss  eval_runtime  \\\n",
              "0     44.2316   1.999996e-04   0.01     4        NaN           NaN   \n",
              "1     39.9561   1.999984e-04   0.01     8        NaN           NaN   \n",
              "2     35.7861   1.999965e-04   0.02    12        NaN           NaN   \n",
              "3     31.3713   1.999938e-04   0.02    16        NaN           NaN   \n",
              "4     28.1403   1.999903e-04   0.03    20        NaN           NaN   \n",
              "...       ...            ...    ...   ...        ...           ...   \n",
              "1127   0.0894   1.559637e-09   5.99  4492        NaN           NaN   \n",
              "1128   0.0869   3.899100e-10   5.99  4496        NaN           NaN   \n",
              "1129   0.0990   0.000000e+00   6.00  4500        NaN           NaN   \n",
              "1130      NaN            NaN   6.00  4500   0.074469      107.3013   \n",
              "1131      NaN            NaN   6.00  4500        NaN           NaN   \n",
              "\n",
              "      eval_samples_per_second  eval_steps_per_second  train_runtime  \\\n",
              "0                         NaN                    NaN            NaN   \n",
              "1                         NaN                    NaN            NaN   \n",
              "2                         NaN                    NaN            NaN   \n",
              "3                         NaN                    NaN            NaN   \n",
              "4                         NaN                    NaN            NaN   \n",
              "...                       ...                    ...            ...   \n",
              "1127                      NaN                    NaN            NaN   \n",
              "1128                      NaN                    NaN            NaN   \n",
              "1129                      NaN                    NaN            NaN   \n",
              "1130                     9.32                  1.165            NaN   \n",
              "1131                      NaN                    NaN      9793.0337   \n",
              "\n",
              "      train_samples_per_second  train_steps_per_second    total_flos  \\\n",
              "0                          NaN                     NaN           NaN   \n",
              "1                          NaN                     NaN           NaN   \n",
              "2                          NaN                     NaN           NaN   \n",
              "3                          NaN                     NaN           NaN   \n",
              "4                          NaN                     NaN           NaN   \n",
              "...                        ...                     ...           ...   \n",
              "1127                       NaN                     NaN           NaN   \n",
              "1128                       NaN                     NaN           NaN   \n",
              "1129                       NaN                     NaN           NaN   \n",
              "1130                       NaN                     NaN           NaN   \n",
              "1131                     3.676                    0.46  2.474911e+16   \n",
              "\n",
              "      train_loss  \n",
              "0            NaN  \n",
              "1            NaN  \n",
              "2            NaN  \n",
              "3            NaN  \n",
              "4            NaN  \n",
              "...          ...  \n",
              "1127         NaN  \n",
              "1128         NaN  \n",
              "1129         NaN  \n",
              "1130         NaN  \n",
              "1131    0.385062  \n",
              "\n",
              "[1132 rows x 13 columns]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_trainer_logs = pd.DataFrame(peft_trainer.state.log_history)\n",
        "\n",
        "df_trainer_logs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "876t2wnkUoFm"
      },
      "outputs": [],
      "source": [
        "df_trainer_logs.to_csv('t5_6000_cs_abs_train_logs.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "VM0KpfwTUa9A",
        "outputId": "4b434464-ebbf-4d28-bbf0-ff1e459efebe"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>epoch</th>\n",
              "      <th>loss</th>\n",
              "      <th>eval_loss</th>\n",
              "      <th>learning_rate</th>\n",
              "      <th>step</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.01</td>\n",
              "      <td>44.2316</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.999996e-04</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.01</td>\n",
              "      <td>39.9561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.999984e-04</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02</td>\n",
              "      <td>35.7861</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.999965e-04</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.02</td>\n",
              "      <td>31.3713</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.999938e-04</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.03</td>\n",
              "      <td>28.1403</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.999903e-04</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1127</th>\n",
              "      <td>5.99</td>\n",
              "      <td>0.0894</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.559637e-09</td>\n",
              "      <td>4492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>5.99</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.899100e-10</td>\n",
              "      <td>4496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1129</th>\n",
              "      <td>6.00</td>\n",
              "      <td>0.0990</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.074469</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1131</th>\n",
              "      <td>6.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1132 rows × 5 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-5358e00f-2b33-4373-96e4-0c90d8821859')\"\n",
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              "\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-5358e00f-2b33-4373-96e4-0c90d8821859 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-5358e00f-2b33-4373-96e4-0c90d8821859');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </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",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\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-d5e4c186-6602-42aa-82df-46e6e216b946 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_45377e8f-0ae8-420f-8a0a-d6ab79f55735\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_trainer_logs')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_45377e8f-0ae8-420f-8a0a-d6ab79f55735 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_trainer_logs');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      epoch     loss  eval_loss  learning_rate  step\n",
              "0      0.01  44.2316        NaN   1.999996e-04     4\n",
              "1      0.01  39.9561        NaN   1.999984e-04     8\n",
              "2      0.02  35.7861        NaN   1.999965e-04    12\n",
              "3      0.02  31.3713        NaN   1.999938e-04    16\n",
              "4      0.03  28.1403        NaN   1.999903e-04    20\n",
              "...     ...      ...        ...            ...   ...\n",
              "1127   5.99   0.0894        NaN   1.559637e-09  4492\n",
              "1128   5.99   0.0869        NaN   3.899100e-10  4496\n",
              "1129   6.00   0.0990        NaN   0.000000e+00  4500\n",
              "1130   6.00      NaN   0.074469            NaN  4500\n",
              "1131   6.00      NaN        NaN            NaN  4500\n",
              "\n",
              "[1132 rows x 5 columns]"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_trainer_logs = df_trainer_logs[[\"epoch\",\"loss\",\"eval_loss\",\"learning_rate\",\"step\"]]\n",
        "df_trainer_logs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "TjM5mTRpYZht",
        "outputId": "509e2114-0301-445c-e237-137a0a345d4f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss')"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "steps = df_trainer_logs.index\n",
        "train_loss = df_trainer_logs.loss\n",
        "eval_loss_list = df_trainer_logs.eval_loss\n",
        "\n",
        "plt.plot(steps, train_loss, label=\"train_loss\", color = \"red\")\n",
        "plt.scatter(steps, eval_loss_list,\n",
        "            c=\"blue\", alpha=0.7, s=16)\n",
        "\n",
        "plt.legend([\"Training\", \"Validation\"], loc =\"upper right\")\n",
        "plt.xlabel(\"Steps\")\n",
        "plt.ylabel(\"Loss\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ck1NX_x5sqNi",
        "outputId": "6e02f12d-0303-4b4e-c950-f3bd97f8b7b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.19.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.11.17)\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "cnFVXXwPNgeN"
      },
      "outputs": [],
      "source": [
        "peft_model = peft_model.merge_and_unload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "22674c9ac538401c8c93c82f495d906f",
            "fe06666b4dd3439289458799cdeafd97",
            "a1b6919eaab144f397aaed73bf9953de",
            "eebab466a45a4353a2ead0a03cd6284c",
            "527cddb3ff144662ae3640d057a29e82",
            "9df7326f056e4cccb674e64feafaab65",
            "1322ef2119cf4930a45a662a96380697",
            "d7ce9f23a3a44ce7a2bf6fdea622c58d",
            "692840b760d64fa0bebdb5016aa9808b",
            "42640b26455b4d9a8049732e23d9c84d",
            "894637ceabc4476182b23d2cad863736",
            "0d854c9c71014a2fa409be051b87bde7",
            "8e667a758a314c1992659074cf3ae18a",
            "94d2d80658d247cabfd895787f32c941",
            "56e42f0a85db42698ca0b62c57bfebce",
            "4b1f767e85284b85a40fdca556c33c89",
            "b11c8a282a364fd38d3ba003f2878d48",
            "28a17e04827745cda2d47c7a2ab34602",
            "e47d1d6c097b46ee86808e815e6f0743",
            "06e039a46ffa4404bd17dd59a1a68b77",
            "b4c0a37f63e041a591fb3ae44b8eb8d1",
            "3c19e11a17e44a219e3fa2139f02173c",
            "e74ae5b5d7c64970abd02277d84b6493",
            "f3348eec45594c228d06171b7dcc5652",
            "4b99c8443d7b48bf9da72ff68cc592df",
            "87ff5573e6894f6798dba7ccf3d1ca90",
            "d46d3417889c4177ac24a15f0e68d91c",
            "90d7eb5c8f334b52826312faa262de59",
            "5263413af4674416a81477075cdb6ff1",
            "63f965bacdfe40c4a9794443a930fef3",
            "653ddf3bd9f2417285b8a9031100a242",
            "e4a57cfc1b65470ca3fd4a2db317b174"
          ]
        },
        "id": "K4tCtY8dsh4M",
        "outputId": "76709b39-d25c-49cb-da10-f66a6d4b6a6e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "22674c9ac538401c8c93c82f495d906f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "dc644feb79ef4779be8d5e3844b8cdd4",
            "31315113a1614cbe8346488a39653c10",
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            "0de3580c5a484b34aee1301c1ded8700",
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            "6b4f9f1791b0463aae9f32dace5e89dd",
            "f6f53ad46be841c7a2dfdf1159783f2a",
            "135cf64eb60d4922a866eda259db3c4f"
          ]
        },
        "id": "ACu4BKr0svu6",
        "outputId": "43a37cac-0c71-4774-caad-b95b3dd8c442"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dc644feb79ef4779be8d5e3844b8cdd4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/robuno/title-generation-t5base-6000-v2-1/commit/e810c5f96df9accd9e5b8933b6d38d1e49820957', commit_message='Upload T5ForConditionalGeneration', commit_description='', oid='e810c5f96df9accd9e5b8933b6d38d1e49820957', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "peft_model.push_to_hub(\"robuno/title-generation-t5base-6000-v2-1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXmLLU9QtV9l",
        "outputId": "db2efd5d-0ff7-4cee-9612-ef0a00fd97a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/robuno/title-generation-t5base-6000-v2-1/commit/81c90530977596b31093a9206adb06c70532bd0d', commit_message='Upload tokenizer', commit_description='', oid='81c90530977596b31093a9206adb06c70532bd0d', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.push_to_hub(\"robuno/title-generation-t5base-6000-v2-1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DV5aYwvugx-w"
      },
      "source": [
        "# 5) Tracking Memory Usage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKC3jo1yHKkC",
        "outputId": "4e3e797a-bf29-4da3-c4ff-32118f8977ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANSI_X3.4-1968\n"
          ]
        }
      ],
      "source": [
        "import locale\n",
        "print(locale.getpreferredencoding())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "fBF-IItfHNf6"
      },
      "outputs": [],
      "source": [
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2H7cvv03HVpm",
        "outputId": "4088d7e1-d3fd-4c9e-c004-655239471a5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting nvidia-ml-py3\n",
            "  Downloading nvidia-ml-py3-7.352.0.tar.gz (19 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: nvidia-ml-py3\n",
            "  Building wheel for nvidia-ml-py3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nvidia-ml-py3: filename=nvidia_ml_py3-7.352.0-py3-none-any.whl size=19171 sha256=6f3831072f2af7a187c2c4e49c6cd56d7e477c0d549d43df1142a6285a927aa0\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/d8/c0/46899f8be7a75a2ffd197a23c8797700ea858b9b34819fbf9e\n",
            "Successfully built nvidia-ml-py3\n",
            "Installing collected packages: nvidia-ml-py3\n",
            "Successfully installed nvidia-ml-py3-7.352.0\n"
          ]
        }
      ],
      "source": [
        "pip install nvidia-ml-py3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgMTCnO2g3bK",
        "outputId": "542be32e-db3f-4be5-ec2d-a3653ad8ae30"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sun Dec 31 13:42:47 2023       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   72C    P0              32W /  70W |  11641MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GjToGN9ThD0h",
        "outputId": "de090ee2-7436-4320-b0ef-926be879a46e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tesla T4\n",
            "Memory Usage:\n",
            "Allocated: 1.0 GB\n",
            "Cached:    11.2 GB\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/cuda/memory.py:444: FutureWarning: torch.cuda.memory_cached has been renamed to torch.cuda.memory_reserved\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "if device.type == 'cuda':\n",
        "    print(torch.cuda.get_device_name(0))\n",
        "    print('Memory Usage:')\n",
        "    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')\n",
        "    print('Cached:   ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRjIiP4fKQIs"
      },
      "source": [
        "Merging Models and Upload"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hYB8cp08KSnm"
      },
      "outputs": [],
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "del pipe_titlegen\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ]
    }
  ],
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