Vicuna_7B_Instruct_QLoRA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gpt2go/llm2finetune/blob/main/Vicuna_7B_Instruct_QLoRA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrained by gpt2go (Aaron Hung) 2023.Aug.3"
      ],
      "metadata": {
        "id": "l8eg_gE67e6q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzGa3YSCgl4C"
      },
      "source": [
        "# Install"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install numba\n",
        "\n",
        "from numba import cuda\n",
        "device = cuda.get_current_device()\n",
        "device.reset()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9gwY5Ywjvsj",
        "outputId": "19c777a4-75fa-439a-e8b3-667b90684094"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (0.56.4)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba) (0.39.1)\n",
            "Requirement already satisfied: numpy<1.24,>=1.18 in /usr/local/lib/python3.10/dist-packages (from numba) (1.22.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba) (67.7.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "7wgbsa4U2TBk"
      },
      "outputs": [],
      "source": [
        "# https://stackoverflow.com/questions/56081324/why-are-google-colab-shell-commands-not-working\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGeTgx1Hgljb",
        "outputId": "9370fc74-1e99-43cb-ad9c-1e9b2c49db0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q -U einops\n",
        "!pip install -q -U safetensors\n",
        "!pip install -q -U torch\n",
        "!pip install -q -U xformers\n",
        "!pip install -q -U datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[sentencepiece]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaQn6mPx6siq",
        "outputId": "c5383ec4-72bf-4541-f966-7c203721da02"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.10/dist-packages (4.32.0.dev0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2.27.1)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.3.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (4.65.0)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (0.1.99)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers[sentencepiece]) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers[sentencepiece]) (4.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (3.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1zodOgcgqnO"
      },
      "source": [
        "# Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "v_r4SL_KmHGs"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline\n",
        "import transformers\n",
        "import torch\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torch\n",
        "from transformers import AutoTokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Quantized Model"
      ],
      "metadata": {
        "id": "alLhGWu9Ngvg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483,
          "referenced_widgets": [
            "c1d86ecea5894b939e9f5f7e32d6a071",
            "bdf7691b8e99477aa2fbba7704de4b2c",
            "8f44c6b88acb48d98f143b7a57da6d41",
            "391eec2f07334232a5ec017c31ee7779",
            "bdb6799d403e464f8fc58c1b5444e8c4",
            "6bddacce500845e8a5593cab4643dee8",
            "1364891728ab4d3abddf56953820618e",
            "9c97b53a886e4057b0beb2393c6bd1ec",
            "9904fcc724054a0094fb23c4052260d0",
            "e9c4953f923545a08cad5ad308719281",
            "3f8896522ecd4c2f983c140993b726fe"
          ]
        },
        "id": "tz4eBuxZHi7F",
        "outputId": "80655bcc-d658-42f5-f1bb-131dad04a5d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1d86ecea5894b939e9f5f7e32d6a071"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-714fc62d5e23>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrust_remote_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meos_token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModelForCausalLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquantization_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbnb_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrust_remote_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m             \u001b[0mmodel_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m             return model_class.from_pretrained(\n\u001b[0m\u001b[1;32m    512\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   2938\u001b[0m                 \u001b[0moffload_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2939\u001b[0m                 \u001b[0merror_msgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2940\u001b[0;31m             \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_pretrained_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2941\u001b[0m                 \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2942\u001b[0m                 \u001b[0mstate_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_load_pretrained_model\u001b[0;34m(cls, model, state_dict, loaded_keys, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, _fast_init, low_cpu_mem_usage, device_map, offload_folder, offload_state_dict, dtype, is_quantized, keep_in_fp32_modules)\u001b[0m\n\u001b[1;32m   3302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3303\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlow_cpu_mem_usage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3304\u001b[0;31m                     new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\n\u001b[0m\u001b[1;32m   3305\u001b[0m                         \u001b[0mmodel_to_load\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3306\u001b[0m                         \u001b[0mstate_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_load_state_dict_into_meta_model\u001b[0;34m(model, state_dict, loaded_state_dict_keys, start_prefix, expected_keys, device_map, offload_folder, offload_index, state_dict_folder, state_dict_index, dtype, is_quantized, is_safetensors, keep_in_fp32_modules)\u001b[0m\n\u001b[1;32m    727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m\"SCB\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparam_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 set_module_quantized_tensor_to_device(\n\u001b[0m\u001b[1;32m    730\u001b[0m                     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp16_statistics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfp16_statistics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/bitsandbytes.py\u001b[0m in \u001b[0;36mset_module_quantized_tensor_to_device\u001b[0;34m(module, tensor_name, device, value, fp16_statistics)\u001b[0m\n\u001b[1;32m    107\u001b[0m             \u001b[0mnew_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m             \u001b[0mnew_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0mnew_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: invalid argument\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n"
          ]
        }
      ],
      "source": [
        "model_id = \"CleverShovel/vicuna-7b-v1.3-sharded-bf16\" # sharded model by vilsonrodrigues\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0}, trust_remote_code=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "Hd22_v_mkbZy"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ],
      "metadata": {
        "id": "HOhd6empkd0_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ],
      "metadata": {
        "id": "-qjN7qOkkgKI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee3f15e7-8755-46c4-f72f-40b91a5a6dcd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 8388608 || all params: 3508801536 || trainable%: 0.23907331075678143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare Data"
      ],
      "metadata": {
        "id": "mxDuoyQvQfD2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "UZbrRk4J3qVH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "dc07876c82f945438375ba3aae86348d",
            "53ccafa890cd45cfb9f562a5ac2d3e4c",
            "1ad2f8276155437d8ff27f006237000e",
            "2c890d7e5de3404cb904736cc1a926cf",
            "eb484ed101c6423881b0ddc61661d04b",
            "c5b0a769c31341f7a64f43d3dd715fb3",
            "79ae15a44c044f5591c63440498cf3a2",
            "3bf307ef39fb4f4ab89443daec06a75b",
            "677d71a5e2e14b7bb4ce9fc322d8a2d4",
            "9108167d2912467a9878311dd73ff117",
            "6398989ada5d45fe8d5b692d423202fa",
            "5efdd766c38a4dfbab34176514ce4928",
            "ae70b109ea944f03bbf0ba21623322e3",
            "207f0b26e787478795434bb47dd56e10",
            "b55614902d884e528d46ff734b92bf31",
            "64f4ae1c68c841dbb0e6715c3bb3236b",
            "a064ffb5498c41df9bd6de87ed05094a",
            "aadb2ea0f6be4b159bdb7cd1ab16d2e1",
            "80c7bf668dba483f894b8b95752c32e5",
            "784c49f9016c48b0b275db01b1132501",
            "7f83d53183fa4ebaa20585ca0dc95015",
            "9c4b29fca63543c28aed4291806de775",
            "0bb3b29e194c4d5e8abb2bc2f17b1fb5",
            "85a58d478eaf4b77bb76256b55e92939",
            "bee7cf11464943ed8c9ad87f5826035f",
            "3d65f34dc01043aba9074cbacc05022d",
            "ea38fbf4fc7c469789390b4aa30435f9",
            "4354a34d33bd4d818a42273e1dc4890c",
            "c8094106a75141f9a5f0602ce7086452",
            "8347817e807e4ef2963a7067047c0bda",
            "122b96c225964286a15d2d2e338dc5ac",
            "6f9c35a4e2b0479398baeb46f8e028e6",
            "8b47b0bac56841d98498adf93c435a96",
            "ed7be2695f2f4a6791a848596147f75e",
            "41f4abb003b342fa9ddf7457970af679",
            "7e673571ccea4878a008e54afa4c1ff1",
            "a1fbea292018477ba51d3d55acbfd02c",
            "881a51f2dbfc470bbdb635f3318c765c",
            "a2738fb0ef374c1caacc6409a6171aae",
            "4751b30e56424f5b9b97c0dfa7d573fa",
            "e71e9337cda6450fb95d8294038fff6d",
            "8c08de6ee0d64356bdf965f09de7df8f",
            "cd37894a43394965b54bb3f0ec235730",
            "7547fa83d8c742f785642d9a2feccbad"
          ]
        },
        "outputId": "31db4fb8-a038-49c0-f3e4-6eca837a5766"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc07876c82f945438375ba3aae86348d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/167k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5efdd766c38a4dfbab34176514ce4928"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bb3b29e194c4d5e8abb2bc2f17b1fb5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed7be2695f2f4a6791a848596147f75e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "data = load_dataset(\"zadhart/ESG_Chat\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "c7Pbb_6H6C2t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6b14aec269f1480badd88de90ca8c089",
            "d25734bee39b420c868fc84f20b6767d",
            "3ea522e06bf9454886f2b859a4f5d761",
            "b6af54bc30614cb386786195cf4d1bbe",
            "2e50f0d5ef384788a9e2ef9b7aa6b086",
            "b521ba8467d840bd98f55f02fdd66a2b",
            "dd91872aae8b449a92759b77a7ccff5d",
            "3ec3473994d04aeb93bf5c912d86692d",
            "e6243a85d04240dab42d98aae1a54457",
            "16162da871a2484383d7fc96d20d2471",
            "9c4d32b7ab70449cac37856750733d33"
          ]
        },
        "outputId": "f436559d-0d49-49bd-dfb5-3d73f3a3bed2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/183 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b14aec269f1480badd88de90ca8c089"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# prompt_template = \"### Instruction: {prompt}\\n### Response:\"\n",
        "\n",
        "train_dataset = data['train'].map(lambda x: {\"input_text\": x['Perguntas']  + \"\\n\" + x['Respostas']})\n",
        "\n",
        "# Tokenize the datasets\n",
        "train_encodings = tokenizer(train_dataset['input_text'], truncation=True, padding=True, max_length=256, return_tensors='pt')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class TextDataset(Dataset):\n",
        "    def __init__(self, encodings):\n",
        "        self.encodings = encodings\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item[\"labels\"] = item[\"input_ids\"].clone()\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.encodings[\"input_ids\"])\n"
      ],
      "metadata": {
        "id": "whNBQA9m0EPb"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the encodings to PyTorch datasets\n",
        "train_dataset = TextDataset(train_encodings)"
      ],
      "metadata": {
        "id": "hrNxx33K0H9p"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example Before Fine Tuning"
      ],
      "metadata": {
        "id": "RWGTyUHhSZx-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate(index):\n",
        "\n",
        "  example_text = data['train'][index]['Perguntas']\n",
        "  correct_answer = data['train'][index]['Respostas']\n",
        "\n",
        "  print(\"Question:\")\n",
        "  print(example_text)\n",
        "\n",
        "  encoding = tokenizer(example_text, return_tensors=\"pt\").to(\"cuda:0\")\n",
        "  output = model.generate(input_ids=encoding.input_ids, attention_mask=encoding.attention_mask, max_new_tokens=150, do_sample=True, temperature=0.000001, eos_token_id=tokenizer.eos_token_id, top_k = 0)\n",
        "  predict = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "  predict = predict[len(example_text)+1:]\n",
        "  print(\"Answer:\")\n",
        "  print(predict)\n",
        "\n",
        "  print(\"Best Answer:\")\n",
        "  print(correct_answer)\n",
        "\n",
        "  print()"
      ],
      "metadata": {
        "id": "xx4mKCzFPEM7"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DP3mtlKbT8vv",
        "outputId": "f7831649-59d0-4c8f-be21-83eb4455b616"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            "Define Corporate Social Responsibility in ESG context\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1296: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer:\n",
            "Corporate Social Responsibility (CSR) refers to a company' The 2018-2023 World Outlook for Automotive Batteries\n",
            "This study covers the world outlook for automotive batteries across more than 190 countries. For each year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the country in question (in millions of U.S. dollars), the percent share the country is of the region, and of the globe.\n",
            "This report provides you with valuable data and insights about the potential, opportunities, and challenges of the automotive batteries market across\n",
            "Best Answer:\n",
            "Corporate Social Responsibility (CSR) in the ESG (Environmental Social and Governance) context is a framework that promotes responsible business practices that promote sustainable economic growth while taking into account broader social and environmental impacts. CSR in the ESG context requires companies to consider their impact on the environment social issues and governance practices.\n",
            "\n",
            "Environmental considerations include reducing carbon emissions conserving natural resources minimizing waste and ensuring sustainable business practices. Social considerations include promoting diversity and equality ensuring labor rights protecting human rights and engaging with local communities. Governance considerations include promoting transparency accountability and ethical conduct in all business operations.\n",
            "\n",
            "Companies that implement CSR in the ESG context are focused on creating long-term value for all their stakeholders including shareholders employees customers the environment and the broader society. By incorporating ESG factors into their decision-making processes companies can build a more sustainable and resilient business model that benefits all stakeholders.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(8)\n",
        "rand_q = random.sample(list(range(len(data['train']))),10)"
      ],
      "metadata": {
        "id": "PDlnwWZGDugJ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_response_set(model):\n",
        "   predictions = []\n",
        "\n",
        "   for index in rand_q:\n",
        "      example_text = data['train'][index]['Perguntas']\n",
        "      encoding = tokenizer(example_text, return_tensors=\"pt\").to(\"cuda:0\")\n",
        "      output = model.generate(input_ids=encoding.input_ids, attention_mask=encoding.attention_mask, max_new_tokens=150, do_sample=True, temperature=0.000001, eos_token_id=tokenizer.eos_token_id, top_k = 0)\n",
        "      predict = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "      predict = predict[len(example_text)+1:]\n",
        "      predictions.append(predict)\n",
        "\n",
        "   return predictions\n"
      ],
      "metadata": {
        "id": "GfTMQ3xnAACc"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = get_response_set(model)"
      ],
      "metadata": {
        "id": "mCH1gbUxEdZb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 954
        },
        "outputId": "031bc104-73a2-4fd8-c9a2-c09ef57eb495"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-4ed6dcd31357>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_response_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-19-8d9fdba45604>\u001b[0m in \u001b[0;36mget_response_set\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m      5\u001b[0m       \u001b[0mexample_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Perguntas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m       \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cuda:0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m       \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_new_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.000001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meos_token_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meos_token_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m       \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_special_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m       \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1003\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneration_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1006\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_inputs_for_generation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_model_prepare_inputs_for_generation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, **kwargs)\u001b[0m\n\u001b[1;32m   1613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1614\u001b[0m             \u001b[0;31m# 13. run sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1615\u001b[0;31m             return self.sample(\n\u001b[0m\u001b[1;32m   1616\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1617\u001b[0m                 \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   2771\u001b[0m             \u001b[0;31m# sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2772\u001b[0m             \u001b[0mprobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext_token_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2773\u001b[0;31m             \u001b[0mnext_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultinomial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2774\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2775\u001b[0m             \u001b[0;31m# finished sentences should have their next token be a padding token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "TC6u3m4fIKtd",
        "outputId": "6fb2af6d-4242-48f2-b0ee-753bd7df5167"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-fd2951b82103>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'predictions' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reference = data[\"train\"][\"Respostas\"]"
      ],
      "metadata": {
        "id": "f142XdefJ15e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "references = []\n",
        "questions = []\n",
        "for i in rand_q:\n",
        "   references.append(reference[i])\n",
        "   questions.append(data['train'][i]['Perguntas'])\n",
        "\n",
        "references"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBOpiZNTJg-d",
        "outputId": "92d2e9a1-f1ed-4304-ce76-f41b348c7682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Green pricing programs are programs offered by electricity providers that allow customers to pay a premium for electricity that is generated from renewable energy sources such as wind or solar power. These programs are often offered as a way to address environmental social and governance (ESG) concerns by reducing carbon emissions and promoting sustainable energy production. The premiums paid by customers are often used to purchase renewable energy certificates (RECs) or fund additional renewable energy projects. By participating in green pricing programs customers can demonstrate their commitment to sustainability and support the development of renewable energy resources which in turn can help to mitigate climate change and promote a more sustainable future.',\n",
              " 'Green financing is a type of financing that is focused on environmentally sustainable projects and initiatives. It is guided by environmental social and governance (ESG) considerations and aims to support projects which have a positive impact on the environment and society. Green financing can come in the form of loans bonds and other types of investments that are focused on eco-friendly projects such as renewable energy clean transportation and sustainable agriculture. The primary goal of green financing is to promote sustainable development and create a more resilient and environmentally-friendly economy.',\n",
              " 'In the context of environmental social and governance (ESG) data security refers to the protection of confidential and sensitive information from unauthorized access theft or breach with the purpose of preserving the privacy rights and minimizing the risks for all stakeholders. It encompasses efforts to safeguard digital assets including personal financial and operational data and involves implementing appropriate policies procedures and technical solutions to ensure integrity availability and confidentiality of data. Effective data security practices are essential for mitigating operational reputational and financial risks for organizations and building trust with their customers and other stakeholders. As such they are an integral component of ESG strategies aimed at promoting trust accountability and sustainability.',\n",
              " \"Living wage is a concept in ESG (environmental social and governance) context that refers to a wage rate that is sufficient to provide workers with a decent standard of living. It is often calculated based on the actual cost of living in a given region or country taking into account essential expenses such as food shelter healthcare and education.\\n\\nIn the context of ESG considerations companies that pay their workers a living wage are considered to be socially responsible as they are ensuring that their employees are able to meet their basic needs and have a good quality of life. This can also have positive impacts on the environment and governance as workers who are paid a living wage may be more motivated and have better working conditions leading to more productive and sustainable operations.\\n\\nSome investors and stakeholders may use living wage as a metric to assess a company's social performance and determine whether they are investing in socially responsible companies. Additionally some governments may adopt policies that require companies to pay their workers a living wage as part of efforts to promote social justice and reduce inequality.\",\n",
              " 'Biofuels are renewable energy sources derived from organic materials such as plants crops or waste. In an ESG (Environmental Social and Governance) context biofuels can be seen as a more sustainable alternative to traditional fossil fuels as they emit fewer greenhouse gases contribute to the reduction of carbon footprint and support the circular economy by utilizing waste and biomass.\\n\\nESG considerations around biofuels also involve evaluating the social and economic impact of their production and use. This includes assessing whether biofuels contribute to land use change or deforestation whether they require excessive amounts of water and other resources and whether their production creates fair safe and healthy working conditions for farmers and laborers.\\n\\nOverall biofuels can play an important role in decarbonizing the energy sector and enhancing sustainability but it is important to carefully evaluate their production and use in the context of ESG principles to ensure the positive impact on the environment society and governance.',\n",
              " \"Refinitiv Scoring in ESG context refers to an assessment framework developed by Refinitiv a global provider of financial market data and analytics to evaluate the environmental social and governance (ESG) performance of companies. The Refinitiv Scoring methodology relies on a wide range of data sources including corporate disclosures news articles and third-party surveys to measure a company's ESG performance across multiple dimensions such as carbon emissions labor practices and board diversity. The scores are then used by investors financial institutions and other stakeholders to make informed decisions about investments lending and corporate engagement based on ESG considerations. Refinitiv Scoring is regarded as one of the leading ESG evaluation frameworks in the financial industry.\",\n",
              " \"Corporate governance in ESG (Environmental Social and Governance) context refers to the system of rules practices and processes that a company has in place to ensure that it operates in a sustainable and responsible manner. It involves ensuring that the company's management is accountable to its stakeholders such as shareholders customers employees and the communities in which it operates and that it takes into account the effects of its actions on the environment and society. This involves considering issues such as board composition executive compensation transparency and disclosure risk management social responsibility and environmental impact. Effective corporate governance in the ESG context is essential for companies to create long-term value for all stakeholders and maintain their social license to operate.\",\n",
              " 'Governance through exit in ESG context refers to the practice of investors using their power to sell or divest from companies that do not align with their environmental social and governance (ESG) criteria. Instead of engaging with companies through dialogue or activism investors may choose to exit or divest from such companies as a way to hold them accountable for their actions and drive change. This strategy is based on the belief that withdrawing financial support will pressure companies to adopt responsible practices and improve their ESG performance. It is a form of market-based activism that seeks to use the power of capital to promote sustainable business practices and advance the goals of ESG investing.',\n",
              " \"Stakeholders in an ESG (Environmental Social and Governance) context refer to individuals or groups who have an interest in a company's sustainability practices beyond just generating profits. These stakeholders include shareholders employees customers suppliers regulators communities and the environment. Each of these stakeholders has a unique perspective on how a company's ESG practices may affect their interests and they often hold the company accountable for their actions. For example shareholders may want the company to prioritize long-term sustainability goals while customers may prefer products that are environmentally friendly. ESG reporting and disclosure helps companies navigate these complex interests and build trust with their stakeholders.\",\n",
              " 'In the context of ESG (Environmental Social and Governance) factors floods refer to natural disasters that have immense social and environmental impacts causing widespread damage and destruction to communities infrastructure and ecosystems. Floods can result from various causes including heavy rainfall snowmelt storm surges dam ruptures or human activities such as deforestation and urbanization. Floods also have economic impacts disrupting businesses and leading to financial losses. Addressing the risk of floods is a critical aspect of sustainable development and resilience planning calling for responsible environmental practices infrastructure investments and effective disaster management strategies.']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def compare_df(question,pred,ref) -> pd.DataFrame:\n",
        "   info = {\n",
        "      \"quetsion\" : question,\n",
        "      \"response\" : pred,\n",
        "      \"answer\" : ref,\n",
        "   }\n",
        "\n",
        "   return pd.DataFrame(info)\n",
        "\n",
        "\n",
        "df = compare_df(questions,predictions,references)"
      ],
      "metadata": {
        "id": "8H1B4amGI6xN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate\n",
        "!pip install rouge_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgKjDhPiLMG6",
        "outputId": "86c80b91-0dcc-40e2-8cb8-25fa2f08f7e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/81.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m61.4/81.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.13.1)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n",
            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (9.0.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n",
            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.0 responses-0.18.0\n",
            "Collecting rouge_score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (4.65.0)\n",
            "Building wheels for collected packages: rouge_score\n",
            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24934 sha256=4dfcb0aca65e72e9359b61ee24420661cec7cd8c12f4393508512d2208f5f730\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge_score\n",
            "Installing collected packages: rouge_score\n",
            "Successfully installed rouge_score-0.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "1yKFSFSJLF01",
        "outputId": "044a8f5f-b932-43b9-e3b0-d0cd42f255e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                       quetsion  \\\n",
              "0  Define green pricing programs in ESG context   \n",
              "1         Define green financing in ESG context   \n",
              "2           Define data security in ESG context   \n",
              "3             Define Living Wage in ESG context   \n",
              "4                Define biofuels in ESG context   \n",
              "\n",
              "                                            response  \\\n",
              "0  Identify key ESG issues relevant to green pric...   \n",
              "1  Green financing refers to the financing of pro...   \n",
              "2  Data security refers to the protection of sens...   \n",
              "3  The living wage is the minimum amount of incom...   \n",
              "4  Biofuels are renewable, low-carbon energy sour...   \n",
              "\n",
              "                                              answer  \n",
              "0  Green pricing programs are programs offered by...  \n",
              "1  Green financing is a type of financing that is...  \n",
              "2  In the context of environmental social and gov...  \n",
              "3  Living wage is a concept in ESG (environmental...  \n",
              "4  Biofuels are renewable energy sources derived ...  "
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              "\n",
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              "          const element = document.querySelector('#df-bf445a70-d466-432a-8bb3-bce1fe8e352e');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate"
      ],
      "metadata": {
        "id": "2EIGlQd3ioxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rouge = evaluate.load('rouge')\n",
        "results = rouge.compute(predictions=predictions,references=references,use_stemmer=True)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "120d8f187bce4e4e88b779293490f4b3",
            "c7c1df9b6a62469baf1adcac4133aaaf",
            "94c25cf0b33640bab80bed32fdbb576b",
            "8bbad0e6c49f43f08f775e466cd911aa",
            "461d197536354d9ab49aee62fb115aa2",
            "ac511008d25d499fb8bb02e80fd16aa2",
            "c61203b593c2462b87eb806afd4350da",
            "f563bb77fcf84f60a74668bb99724039",
            "64e205bb1244445f884cb58fce21f178",
            "c48cd19140de4b59b58198ab70d146d5",
            "c2342555d2c14359814ef528d12041c9"
          ]
        },
        "id": "JXuQlkY9LdjL",
        "outputId": "30c16d2a-06ab-4eba-e784-27fd7db85ed8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "120d8f187bce4e4e88b779293490f4b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.4521135134161909,\n",
              " 'rouge2': 0.16549681545190473,\n",
              " 'rougeL': 0.27429576028322344,\n",
              " 'rougeLsum': 0.3053308783332366}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "J53zPp1qSdZp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iDniJ-bq4nxD",
        "outputId": "4bcdbb73-e645-4014-d2cb-d4c354922317"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-424aeced090c>:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 21:25, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.477900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.446700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.443000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.400400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.310200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.277300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.198000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.145900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.148100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.087900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.108100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.046300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.997800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.000200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.988800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.994100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.978300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.976200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.919900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.008300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.965800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.927900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.926100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.911300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.948300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.926100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.930800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.935200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.913500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=30, training_loss=1.0852774620056151, metrics={'train_runtime': 1331.9626, 'train_samples_per_second': 1.441, 'train_steps_per_second': 0.023, 'total_flos': 9494392398151680.0, 'train_loss': 1.0852774620056151, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=train_dataset,\n",
        "    # eval_dataset=val_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        num_train_epochs=1,\n",
        "        per_device_train_batch_size=16,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_ratio=0.05,\n",
        "        max_steps=30,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        lr_scheduler_type='cosine',\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example After Fine Tuning"
      ],
      "metadata": {
        "id": "KWXT-FNfSlpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.use_cache = True\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9vrd1FXSz8W",
        "outputId": "7d7fb6f3-a04e-40fb-a6c9-65fdda070c53"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): LlamaForCausalLM(\n",
              "      (model): LlamaModel(\n",
              "        (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x LlamaDecoderLayer(\n",
              "            (self_attn): LlamaAttention(\n",
              "              (q_proj): Linear4bit(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "              (v_proj): Linear4bit(\n",
              "                in_features=4096, out_features=4096, bias=False\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.05, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "              )\n",
              "              (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "              (rotary_emb): LlamaRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): LlamaMLP(\n",
              "              (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "              (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "              (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "              (act_fn): SiLUActivation()\n",
              "            )\n",
              "            (input_layernorm): LlamaRMSNorm()\n",
              "            (post_attention_layernorm): LlamaRMSNorm()\n",
              "          )\n",
              "        )\n",
              "        (norm): LlamaRMSNorm()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate(39)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MezO1msTSnEs",
        "outputId": "009ba4d2-b3d8-47a4-86ac-027920dbc018"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question:\n",
            "Define emissions in ESG context\n",
            "Answer:\n",
            "Emissions in the ESG (Environmental Social and Governance) context refer to the release of pollutants or greenhouse gases into the atmosphere that contribute to climate change and environmental degradation. These emissions can come from a variety of sources including industrial facilities power plants transportation and agriculture. In the ESG context emissions are considered a critical issue as they have a direct impact on the environment and human health and can also affect a company's reputation and financial performance. Companies are increasingly being held accountable for their emissions and are expected to take steps to reduce their environmental impact. This can include implementing sustainable practices such as reducing energy consumption and using renewable energy sources. Companies\n",
            "Best Answer:\n",
            "Emissions in the ESG (Environmental Social and Governance) context refer to the release of harmful gases or pollutants into the environment which contribute to climate change and related environmental impacts. Emissions can come from a variety of sources including industrial facilities transportation energy production and agriculture. In particular the three primary greenhouse gases (GHGs) that contribute to climate change are carbon dioxide methane and nitrous oxide. Companies and organizations are increasingly under pressure to reduce their emissions and adopt sustainable practices as part of their ESG commitments to mitigate climate change and protect the environment for future generations.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction2 = get_response_set(model)"
      ],
      "metadata": {
        "id": "OYFKiHuOcUyX"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTmBM2wmihax",
        "outputId": "c5cd4575-0998-4e6b-c387-d6f0ed32106f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Green pricing programs are a type of environmental social and governance (ESG) initiative that allows customers to pay a premium for electricity that is generated from renewable sources such as wind solar or hydroelectric power. The premium is typically passed on to the utility company which then invests in renewable energy projects to reduce the carbon footprint of the power grid.\\n\\nGreen pricing programs are designed to encourage customers to adopt sustainable energy practices and reduce their environmental impact. By choosing to participate in a green pricing program customers can support the transition to a low-carbon energy system reduce their carbon footprint and contribute to a more sustainable future.\\n\\nIn addition to supporting renew',\n",
              " 'Green financing refers to the investment of financial resources in projects or companies that promote environmental social and governance (ESG) principles. In the context of ESG green financing refers to the investment of capital in companies or projects that have a positive impact on the environment such as reducing carbon emissions promoting renewable energy or preserving natural resources. Green financing also considers social factors such as labor practices human rights and community engagement and considers governance factors such as transparency accountability and ethical behavior. The goal of green financing is to promote sustainable investing and to support companies and projects that have a positive impact on the environment and society. It is a way to align financial interests with',\n",
              " 'In the context of Environmental Social and Governance (ESG) data security refers to the protection of sensitive data related to environmental social and governance factors. This includes personal data financial data and other sensitive information related to sustainability initiatives human rights policies and corporate governance practices. Data security is critical in the ESG context as it ensures that sensitive information is protected from unauthorized access theft or misuse which can have significant legal and reputational consequences for companies. It also enables companies to comply with data privacy regulations and demonstrate their commitment to transparency and accountability. Therefore data security is an essential component of any ESG strategy and companies should take steps to ensure the protection of',\n",
              " 'In the context of ESG (Environmental Social and Governance) factors the Living Wage refers to the minimum wage level that allows workers to maintain a decent standard of living. It is a key component of social sustainability and is often used as a benchmark for assessing the social impact of a company or organization. The Living Wage is calculated based on the cost of living in a particular region or country and takes into account factors such as housing education and healthcare. It is often used as a benchmark for determining fair wages and compensation for workers and is seen as an important measure of social sustainability. In ESG context the Living Wage is considered an essential component of responsible business practices and is seen as',\n",
              " 'Biofuels are renewable energy sources that are derived from organic matter such as plants trees and waste. In the context of Environmental Social and Governance (ESG) biofuels are an important component of sustainable energy strategies as they have the potential to reduce greenhouse gas emissions mitigate climate change and promote energy security. Biofuels can be produced from crops such as corn sugarcane and palm oil or from waste biomass. The production of biofuels can also create jobs and contribute to rural development. However the production and use of biofuels can have negative social and environmental impacts such as land use change deforestation and greenhouse gas emissions from indirect',\n",
              " \"Refinitiv Scoring is a methodology used to evaluate the environmental social and governance (ESG) performance of companies. It is a proprietary ESG scoring system developed by Refinitiv a leading provider of financial data and analytics. The Refinitiv Scoring system assesses companies on a range of ESG factors including environmental impact social responsibility and corporate governance. The scores are used by investors financial analysts and other stakeholders to evaluate the sustainability and long-term viability of companies. The Refinitiv Scoring system is designed to provide a comprehensive and objective assessment of a company's ESG performance allowing investors to make informed decisions based on sustainability\",\n",
              " \"Corporate governance in the ESG (Environmental Social and Governance) context refers to the system of rules and practices that ensure that a company operates in a responsible and sustainable manner while also meeting the financial and legal obligations of its shareholders. It involves the establishment of policies and procedures that promote transparency accountability and ethical behavior within the company. This includes ensuring that the company's operations are environmentally friendly that it respects human rights and that it engages with stakeholders such as employees customers and communities. Corporate governance also involves the appointment of independent directors who can provide oversight and guidance to the company's management and help ensure that the company'\",\n",
              " 'Governance through exit in the ESG (Environmental Social and Governance) context refers to the practice of investors using their exit power to influence the behavior of companies on ESG issues. This can include divesting from companies that do not meet certain ESG standards or engaging with companies to encourage them to adopt more sustainable practices. The idea is that by using their exit power investors can exert pressure on companies to improve their ESG performance which can ultimately benefit all stakeholders including shareholders employees and the communities in which the company operates.\\n\\nGovernance through exit is seen as a powerful tool for promoting sustainable and responsible business practices as it provides investors with',\n",
              " \"Stakeholders in the ESG (Environmental Social and Governance) context refer to individuals or groups who have a direct or indirect interest in a company's environmental social and governance practices. These stakeholders can include shareholders employees customers suppliers communities and regulators. In the context of ESG investing stakeholders play a critical role in shaping a company's policies and practices and investors often consider the views and concerns of stakeholders when making investment decisions. Companies that prioritize ESG factors and engage with stakeholders can benefit from increased trust and loyalty from customers and investors and can also create long-term value for the company\",\n",
              " \"Floods in the ESG (Environmental Social and Governance) context refer to natural disasters that result from excessive rainfall or other forms of water accumulation that lead to widespread damage to infrastructure homes and businesses. Floods can have significant environmental impacts such as erosion and sedimentation of waterways and coastal areas can contribute to climate change and can have social and economic consequences such as displacement of communities and economic losses.\\n\\nIn the context of ESG investing floods are considered a significant environmental risk factor and investors may consider companies' preparedness for floods and their potential impact on their operations and assets. Companies that are located in flood\"]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results2 = rouge.compute(predictions=prediction2,references=references,use_stemmer=True)"
      ],
      "metadata": {
        "id": "6Q7kwttpdklX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "91a82a60-ae3d-43b2-ee68-821c64ca9508"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-95f9d056a78b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrouge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprediction2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreferences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreferences\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0muse_stemmer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'rouge' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiL0j3Tzjdju",
        "outputId": "ba16decd-0d5a-47a8-9329-ce888187e99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.5697584496189545,\n",
              " 'rouge2': 0.2639641892305975,\n",
              " 'rougeL': 0.3603096245769519,\n",
              " 'rougeLsum': 0.39111540660527333}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bleu = evaluate.load('bleu')\n",
        "results_bleu = bleu.compute(predictions=predictions, references=references)\n",
        "results_bleu2 = bleu.compute(predictions=prediction2, references=references)\n",
        "\n",
        "print(results_bleu)\n",
        "print(results_bleu2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "85a26df3e5a842f3aec1e57f8e6061d1",
            "e5cd63532ad94e7f9f696a253eb432a3",
            "bf60f11910c34a278492951de60e79cc",
            "74fb01b8763b42598ff36756d9b371da",
            "4a820f97f5f340efa372ef2972f0d5b5",
            "7dd24d705ec44e52af78aacdf5758d83",
            "ee9ae619b8574324b27c13e74574c4f2",
            "eea2c0cff2a74d9f85b25006b50e20c4",
            "c996140660cc475da89e057242583aac",
            "7ddb7cf894bf4ffab71e933e37aac075",
            "db0b1f6a639b40c29158b13d8622d326",
            "799ec1d483de46b08280b1e35bb76f29",
            "1d9eae61c2b94551a945e332e2344299",
            "fd98689208944eef98abd7e221c95d02",
            "baf11c1d049e410d85d525d046040492",
            "45abbc7b74d4435f8cf7dd664418fec6",
            "b4c9c1be932a4cc9a3ca4cb261ddda6e",
            "ac6f9639e7444d588af0bb3937167c14",
            "f3d432fd5cae4bfe98169a7be1393866",
            "4d903443f424457ca4db6e0969204fef",
            "e7bc5db150c84cd8ac9b6fe3e5f8e2c3",
            "9242eaf7b7d14f4a858f42aefaba2e62",
            "a09091472d564938b93ed74778fd609f",
            "d161dcc30e8049dbac423988968209ad",
            "a71ef1451b4b45948678f95a4ec44775",
            "767109470c4e400a9000b2481bfc36d7",
            "e514bf259b2f4c80a55316b3e92411ee",
            "c7835ce9cd8a4b79af5951cb7a0d8170",
            "a4cfc49598c34638bad7c3b9cc6ffe40",
            "c012aaaa38174e63ab1ce6505807801e",
            "6b543093d3014a4aad1f895dd4b493c1",
            "a6677f2547f54ce2af5ab9cd35171600",
            "da129625c45746d6b1d0875fafe045bf"
          ]
        },
        "id": "fePqDz_k0JaS",
        "outputId": "0c2ece34-900d-45f9-91d7-dd093090613a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.94k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
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            "{'bleu': 0.07690578149807137, 'precisions': [0.4226713532513181, 0.13386524822695037, 0.0518783542039356, 0.01895306859205776], 'brevity_penalty': 0.8904814832507468, 'length_ratio': 0.8960629921259843, 'translation_length': 1138, 'reference_length': 1270}\n",
            "{'bleu': 0.20775165644711646, 'precisions': [0.565587734241908, 0.26288659793814434, 0.16031195840554593, 0.10839160839160839], 'brevity_penalty': 0.9214822901003235, 'length_ratio': 0.9244094488188976, 'translation_length': 1174, 'reference_length': 1270}\n"
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    {
      "cell_type": "code",
      "source": [
        "toxicity = evaluate.load(\"toxicity\")\n",
        "results_tox = toxicity.compute(predictions=predictions, aggregation=\"ratio\")\n",
        "results_tox2 = toxicity.compute(predictions=prediction2, aggregation=\"ratio\")\n",
        "print(results_tox)\n",
        "print(results_tox2)"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "name": "stderr",
          "text": [
            "WARNING:evaluate_modules.metrics.evaluate-measurement--toxicity.2390290fa0bf6d78480143547c6b08f3d4f8805b249df8c7a8e80d0ce8e3778b.toxicity:Using default facebook/roberta-hate-speech-dynabench-r4-target checkpoint\n"
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          "data": {
            "text/plain": [
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        {
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          "data": {
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.11k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
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          "data": {
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          "name": "stdout",
          "text": [
            "{'toxicity_ratio': 0.0}\n",
            "{'toxicity_ratio': 0.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.insert(loc = 2, column=\"vicuna7b_model_response\", value=prediction2)"
      ],
      "metadata": {
        "id": "t7c_-kawPQuS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.insert(loc = 3, column=\"vicuna7b_response\", value=predictions3)"
      ],
      "metadata": {
        "id": "F8DsnWozZSs6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3919
        },
        "id": "fFgoMMC_wGPK",
        "outputId": "728ff4e5-8c18-4b9b-caf2-0f99b78b7d49"
      },
      "execution_count": null,
      "outputs": [
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              "                                        quetsion  \\\n",
              "0   Define green pricing programs in ESG context   \n",
              "1          Define green financing in ESG context   \n",
              "2            Define data security in ESG context   \n",
              "3              Define Living Wage in ESG context   \n",
              "4                 Define biofuels in ESG context   \n",
              "5        Define Refinitiv Scoring in ESG context   \n",
              "6     Define corporate governance in ESG context   \n",
              "7  Define governance through exit in ESG context   \n",
              "8             Define stakeholders in ESG context   \n",
              "9                   Define floods in ESG context   \n",
              "\n",
              "                                            response  \\\n",
              "0  Identify key ESG issues relevant to green pric...   \n",
              "1  Green financing refers to the financing of pro...   \n",
              "2  Data security refers to the protection of sens...   \n",
              "3  The living wage is the minimum amount of incom...   \n",
              "4  Biofuels are renewable, low-carbon energy sour...   \n",
              "5  \\n* Refinitiv Scoring is a proprietary ESG sco...   \n",
              "6  Corporate governance refers to the system of r...   \n",
              "7  In the context of Environmental, Social and Go...   \n",
              "8  Explain the role of stakeholders in ESG\\nDiscu...   \n",
              "9  Floods are a natural hazard that can cause sig...   \n",
              "\n",
              "                             vicuna7b_model_response  \\\n",
              "0  Green pricing programs are a type of environme...   \n",
              "1  Green financing refers to the investment of fi...   \n",
              "2  In the context of Environmental Social and Gov...   \n",
              "3  In the context of ESG (Environmental Social an...   \n",
              "4  Biofuels are renewable energy sources that are...   \n",
              "5  Refinitiv Scoring is a methodology used to eva...   \n",
              "6  Corporate governance in the ESG (Environmental...   \n",
              "7  Governance through exit in the ESG (Environmen...   \n",
              "8  Stakeholders in the ESG (Environmental Social ...   \n",
              "9  Floods in the ESG (Environmental Social and Go...   \n",
              "\n",
              "                                              answer  \n",
              "0  Green pricing programs are programs offered by...  \n",
              "1  Green financing is a type of financing that is...  \n",
              "2  In the context of environmental social and gov...  \n",
              "3  Living wage is a concept in ESG (environmental...  \n",
              "4  Biofuels are renewable energy sources derived ...  \n",
              "5  Refinitiv Scoring in ESG context refers to an ...  \n",
              "6  Corporate governance in ESG (Environmental Soc...  \n",
              "7  Governance through exit in ESG context refers ...  \n",
              "8  Stakeholders in an ESG (Environmental Social a...  \n",
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              "      <th></th>\n",
              "      <th>quetsion</th>\n",
              "      <th>response</th>\n",
              "      <th>vicuna7b_model_response</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Define green pricing programs in ESG context</td>\n",
              "      <td>Identify key ESG issues relevant to green pric...</td>\n",
              "      <td>Green pricing programs are a type of environme...</td>\n",
              "      <td>Green pricing programs are programs offered by...</td>\n",
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              "      <th>1</th>\n",
              "      <td>Define green financing in ESG context</td>\n",
              "      <td>Green financing refers to the financing of pro...</td>\n",
              "      <td>Green financing refers to the investment of fi...</td>\n",
              "      <td>Green financing is a type of financing that is...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Define data security in ESG context</td>\n",
              "      <td>Data security refers to the protection of sens...</td>\n",
              "      <td>In the context of Environmental Social and Gov...</td>\n",
              "      <td>In the context of environmental social and gov...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Define Living Wage in ESG context</td>\n",
              "      <td>The living wage is the minimum amount of incom...</td>\n",
              "      <td>In the context of ESG (Environmental Social an...</td>\n",
              "      <td>Living wage is a concept in ESG (environmental...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Define biofuels in ESG context</td>\n",
              "      <td>Biofuels are renewable, low-carbon energy sour...</td>\n",
              "      <td>Biofuels are renewable energy sources that are...</td>\n",
              "      <td>Biofuels are renewable energy sources derived ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Define Refinitiv Scoring in ESG context</td>\n",
              "      <td>\\n* Refinitiv Scoring is a proprietary ESG sco...</td>\n",
              "      <td>Refinitiv Scoring is a methodology used to eva...</td>\n",
              "      <td>Refinitiv Scoring in ESG context refers to an ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Define corporate governance in ESG context</td>\n",
              "      <td>Corporate governance refers to the system of r...</td>\n",
              "      <td>Corporate governance in the ESG (Environmental...</td>\n",
              "      <td>Corporate governance in ESG (Environmental Soc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Define governance through exit in ESG context</td>\n",
              "      <td>In the context of Environmental, Social and Go...</td>\n",
              "      <td>Governance through exit in the ESG (Environmen...</td>\n",
              "      <td>Governance through exit in ESG context refers ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Define stakeholders in ESG context</td>\n",
              "      <td>Explain the role of stakeholders in ESG\\nDiscu...</td>\n",
              "      <td>Stakeholders in the ESG (Environmental Social ...</td>\n",
              "      <td>Stakeholders in an ESG (Environmental Social a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Define floods in ESG context</td>\n",
              "      <td>Floods are a natural hazard that can cause sig...</td>\n",
              "      <td>Floods in the ESG (Environmental Social and Go...</td>\n",
              "      <td>In the context of ESG (Environmental Social an...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-17593625-b453-480a-9c6c-aa44a0a771d8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2f82c352-f219-42be-92e1-dc9b1bbf7316 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2f82c352-f219-42be-92e1-dc9b1bbf7316');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c1 = \"rouge1\"\n",
        "c2 = \"rouge2\"\n",
        "cL = \"rougeL\"\n",
        "cLsum = \"rougeLsum\"\n",
        "df_score = pd.DataFrame({\n",
        "    c1 : [results[c1], results2[c1], results3[c1]],\n",
        "    c2 : [results[c2], results2[c2], results3[c2]],\n",
        "    cL : [results[cL], results2[cL], results3[cL]],\n",
        "    cLsum : [results[cLsum], results2[cLsum], results3[cLsum]],\n",
        "})\n",
        "df_score.index = [\"falcon7b\" , \"ESG_model\", \"vicuna7b\"]\n",
        "df_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "NHRavyfeZn7_",
        "outputId": "d623a437-536e-4cae-c448-486702e0f03f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             rouge1    rouge2    rougeL  rougeLsum\n",
              "falcon7b   0.466976  0.145024  0.273879   0.294289\n",
              "ESG_model  0.525273  0.203351  0.325743   0.354960\n",
              "vicuna7b   0.457632  0.168178  0.277941   0.301291"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b2ba0827-f07d-4843-a159-94acb0803ab1\">\n",
              "    <div 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",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rouge1</th>\n",
              "      <th>rouge2</th>\n",
              "      <th>rougeL</th>\n",
              "      <th>rougeLsum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>falcon7b</th>\n",
              "      <td>0.466976</td>\n",
              "      <td>0.145024</td>\n",
              "      <td>0.273879</td>\n",
              "      <td>0.294289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ESG_model</th>\n",
              "      <td>0.525273</td>\n",
              "      <td>0.203351</td>\n",
              "      <td>0.325743</td>\n",
              "      <td>0.354960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vicuna7b</th>\n",
              "      <td>0.457632</td>\n",
              "      <td>0.168178</td>\n",
              "      <td>0.277941</td>\n",
              "      <td>0.301291</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2ba0827-f07d-4843-a159-94acb0803ab1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-08f32f73-8945-469f-8195-6b7210487a0b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08f32f73-8945-469f-8195-6b7210487a0b')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-08f32f73-8945-469f-8195-6b7210487a0b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b2ba0827-f07d-4843-a159-94acb0803ab1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b2ba0827-f07d-4843-a159-94acb0803ab1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Merge Adapters and Save Model to Hub"
      ],
      "metadata": {
        "id": "f6FqNGBAz7ct"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "76dfe40946324e0da3ed07082c7ce627",
            "a634f42bb193461ea3bb58e813f3f63b",
            "842717ee9e744114af24bc81daa5f6fe",
            "aa5a2bd550154cee9220674d147cbb96",
            "78b38cf92d874390a40adf6f6c249e45",
            "6463c80a99fe426ba3f11e059a49a253",
            "31a783a63b9d42e699f65b96465cde4d",
            "6d85db864caf4242a035e15d50e57087",
            "dde222e691d04438969355de11c72eaa",
            "8dab6203b2274f8eaf1c363327ee5d0f",
            "f58337bce6b44614893ca7c9a66b03e3",
            "03086cef0b394e2ba6fd0525ab2195a2",
            "b9d65cdecb004cdbb616866b96ee671b",
            "925c7934060b4a988b9102bb3f6a30eb",
            "1b1bad00bc144be7aaad960cf0e750fa",
            "1165e7a9ac664cbdabb0d34d1bb525a5",
            "f7d219b994ac47bc90d7620b1e06b3a5",
            "58ca684f60eb4eac99f32099e3ab7cfb",
            "22d885493d4f41b188004dee27f94519",
            "01a96754c9e74153ae8daf06ac4a7a63",
            "ce90c842133c46949362e08b1f9e421b",
            "8a9be1b7a29a44aea8f6d45bee3a0fa0",
            "c50c8c81f6ec4143abc0ed3ee0fb852a",
            "e43c9d38a6444eaea27f949ec699c621",
            "d2c8b32ef4c34861942378f4df7b42e6",
            "0c59533261b946309ae4c4b20d4bbdf8",
            "2d07610347c34f1b9c3435585ed4f90c",
            "9762b3f277464a649a78bba9ca75672c",
            "35521d581d36438988e0d6c93befd79b",
            "12d106723fa2452584efcdfed9f37c86",
            "be54c7f828384fcfa36c34d26a97f526",
            "9092331054084fe7aadfdac501168c36"
          ]
        },
        "id": "Xmo798pPAwVm",
        "outputId": "4ba3cd27-1b5d-4bc8-d088-98709c353e01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76dfe40946324e0da3ed07082c7ce627"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"vicuna-7b-instruct-ft-adapters-ESG-chatting\", push_to_hub=True) # save LORA adapters\n",
        "model.push_to_hub(\"vicuna-7b-instruct-ft-adapters-ESG-chatting\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "737a7431e1f349fd80cf7f9b4399b4ee",
            "b8518f429fc1459f90972b94d1eecfbd",
            "e4cd65775b9049f587dac614f2a4de97",
            "3ae122ed27f4460db2a4a6027e4fd278",
            "d8fbcf464a7c419abd939a490ac64df3",
            "6a8179dc94b744fbb60d5435afb93deb",
            "0dd952d7cabd4c628495d947c376023f",
            "2e231bc443c14054b4f4f0cfae0bb67c",
            "bb7b9210778a490e862ad3fbbe1ca152",
            "048a0b4f4456466288feb97392f9b4c2",
            "8f5a10d876f94944898112eef3d0f484"
          ]
        },
        "id": "e19FlnnCBlrM",
        "outputId": "bf6430bc-9372-4899-c00e-4177efe46bf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/33.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "737a7431e1f349fd80cf7f9b4399b4ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/FelixChao/vicuna-7b-instruct-ft-adapters-ESG-chatting/commit/39181a93826799ba097346dcd71d569fa0844a16', commit_message='Upload model', commit_description='', oid='39181a93826799ba097346dcd71d569fa0844a16', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel, PeftConfig, LoraConfig\n",
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "config = LoraConfig.from_pretrained(\"FelixChao/falcon-7b-instruct-ft-adapters-ESG-chatting\")\n",
        "# reload the base model (you might need pro subscription for this)"
      ],
      "metadata": {
        "id": "zZPgnENAHmuN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5bb706033491465eb6d38586cb6229fe",
            "cc2051ed32f04bc7b60fcff9d50a20ae",
            "c717425726a04c3e9e2a6cd1f8e93d4f",
            "140277b7fa7a4ab6be5c9efb5dcebf0b",
            "1d9d818d231f4f51ad8ce4bbe37f696a",
            "bf2112ced93046ad8c08dab8e3c5f475",
            "e20e6c254080487aaf799f54a4cf51fb",
            "593af00e18c94b3fae278f9500022459",
            "5c20cd781cb2408f8111ccf8d4f81323",
            "1d2f718f91b14e2597a547bd4101f12a",
            "cc2793c0228d4ab6ac50aedd57d15bd6"
          ]
        },
        "outputId": "a4c5731f-573d-4c97-d4fd-6a19e20eb0fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/438 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5bb706033491465eb6d38586cb6229fe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "\n",
        "# load perf model with new adapters\n",
        "model = PeftModel.from_pretrained(\n",
        "    model,\n",
        "    \"FelixChao/falcon-7b-instruct-ft-adapters-ESG-chatting\",\n",
        ")"
      ],
      "metadata": {
        "id": "rrwiiYLqG6sp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "1610b35ecff64a0480ca76444bea1828",
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            "69e2403cbe464d0c8650a479b2460412",
            "2ff1a07d4eb34584861cfe306203a999",
            "f59e39cfd5dd4a988dec3f06200a0949",
            "08ab5c97802f46e2a52aea211472e0fd",
            "9de25fea018f45ff8c2d3fb95a80dc58",
            "39f42097c3b0473586a77f7ef94c2729",
            "9301988fa5cf4a60abdee6547e6a43d9",
            "29faf09af63446709c218d1333964096"
          ]
        },
        "outputId": "0d15a0e4-92e8-4fec-830f-4747621311c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "861bef5398ea4d82b77ddef2eb8b0e05"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.merge_and_unload() # merge adapters with"
      ],
      "metadata": {
        "id": "YOQybEtGHJ9v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "14297674-6651-4f19-a1ea-edb0c2444329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-451be6022b15>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge_and_unload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# merge adapters with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/tuners/lora.py\u001b[0m in \u001b[0;36mmerge_and_unload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_loaded_in_8bit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_loaded_in_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot merge LORA layers when the model is loaded in 8-bit mode\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0mkey_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_modules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"lora\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot merge LORA layers when the model is loaded in 8-bit mode"
          ]
        }
      ]
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        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"tiiuae/falcon-7b-instruct\", trust_remote_code=True)\n",
        "tokenizer.push_to_hub(\"falcon-7b-instruct-ft\", use_auth_token=True)"
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/220 [00:00<?, ?B/s]"
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