with_4_benchmarks_LORA_with_Chatglm2_6b.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "aBE7gRUJ3L8u"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "jsonl_path = \"../data/dataset_new.jsonl\"\n",
        "save_path = '../data/dataset_new'\n",
        "\n",
        "\n",
        "if os.path.exists(jsonl_path):\n",
        "    os.remove(jsonl_path)\n",
        "\n",
        "if os.path.exists(save_path):\n",
        "    shutil.rmtree(save_path)\n",
        "\n",
        "import os\n",
        "\n",
        "directory = \"../data\"\n",
        "if not os.path.exists(directory):\n",
        "    os.makedirs(directory)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7K9Kq-Jofco",
        "outputId": "ec913169-7565-4c2f-aec1-82675dfbdd94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.5)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.7)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.15)\n",
            "Requirement already satisfied: fsspec[http]<2023.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (17.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.3.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets transformers torch tqdm pandas huggingface_hub\n",
        "!pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "IHvV3smApIgJ"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "import datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvIBHhS5pV8o"
      },
      "source": [
        "1. TFNS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "9lykTXQwohAM"
      },
      "outputs": [],
      "source": [
        "dic = {\n",
        "    0:\"negative\",\n",
        "    1:'positive',\n",
        "    2:'neutral',\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVVWAq54ohCT",
        "outputId": "6a3fd818-657e-4581-ffe1-8be5c012feec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'output', 'instruction'],\n",
              "    num_rows: 9543\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "social_media_dataset = load_dataset('zeroshot/twitter-financial-news-sentiment')\n",
        "social_media_dataset = social_media_dataset['train']\n",
        "social_media_dataset = social_media_dataset.to_pandas()\n",
        "social_media_dataset['label'] = social_media_dataset['label'].apply(lambda x:dic[x])\n",
        "social_media_dataset['instruction'] = 'What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.'\n",
        "social_media_dataset.columns = ['input', 'output', 'instruction']\n",
        "social_media_dataset = datasets.Dataset.from_pandas(social_media_dataset)\n",
        "social_media_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXND6llnpTQj",
        "outputId": "72ec9442-5a3c-4d65-e49c-426f5a89daf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19086\n"
          ]
        }
      ],
      "source": [
        "tmp_dataset = datasets.concatenate_datasets([social_media_dataset]*2)\n",
        "train_dataset = tmp_dataset\n",
        "print(tmp_dataset.num_rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJUCTnVXWj3x",
        "outputId": "55deac1c-cc0a-417d-857b-7a6f7df05f72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19086, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "all_dataset = train_dataset.shuffle(seed = 42)\n",
        "all_dataset.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oob5cnSlpi7C"
      },
      "source": [
        "Make Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "mnZFyhcvpTIv"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "DjmW94OTpjuU"
      },
      "outputs": [],
      "source": [
        "def format_example(example: dict) -> dict:\n",
        "    context = f\"Instruction: {example['instruction']}\\n\"\n",
        "    if example.get(\"input\"):\n",
        "        context += f\"Input: {example['input']}\\n\"\n",
        "    context += \"Answer: \"\n",
        "    target = example[\"output\"]\n",
        "    return {\"context\": context, \"target\": target}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "oomCwuggpjsF"
      },
      "outputs": [],
      "source": [
        "data_list = []\n",
        "for item in all_dataset.to_pandas().itertuples():\n",
        "    tmp = {}\n",
        "    tmp[\"instruction\"] = item.instruction\n",
        "    tmp[\"input\"] = item.input\n",
        "    tmp[\"output\"] = item.output\n",
        "    data_list.append(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69,
          "referenced_widgets": [
            "eccc5f14f06648718a1afa1a43a87150",
            "564ce6b7f88c47f68c4034d981f5aac6",
            "679069d05aa048beae649b291e1f4840",
            "f358f15a1f684d4f8cf49eb28d09838c",
            "4bd588950fea427fbf332f97c45c89a8",
            "ebf441eb77b94db6bbc647629f13f575",
            "75ab7391f86643b38cc2c2ce0e81dd3c",
            "e25042b09b2b4d84b44a54f8f67b7117",
            "30959444dd594fda9050982767ae92e0",
            "22a9646e4daa4319b250e09b956e6643",
            "9e92c7533ebe477bbc0e6831bc68118c"
          ]
        },
        "id": "VuuRhlD1pjqH",
        "outputId": "f4a7135e-c535-4281-be2a-284ede89501b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "formatting..:   0%|          | 0/19086 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eccc5f14f06648718a1afa1a43a87150"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "with open(\"../data/dataset_new.jsonl\", 'w') as f:\n",
        "    for example in tqdm(data_list, desc=\"formatting..\"):\n",
        "        f.write(json.dumps(format_example(example)) + '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YptAdKh0preG"
      },
      "source": [
        "Tokenize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "oOhgGNTqLX3J"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Sjh98Wr_pjmI"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import datasets\n",
        "from transformers import AutoTokenizer, AutoConfig\n",
        "\n",
        "model_name = \"THUDM/chatglm2-6b\"\n",
        "# tokenizer = AutoTokenizer.from_pretrained(\n",
        "#     model_name, trust_remote_code=True)\n",
        "# config = AutoConfig.from_pretrained(\n",
        "#     model_name, trust_remote_code=True, device_map='auto')\n",
        "jsonl_path = \"../data/dataset_new.jsonl\"  # updated path\n",
        "save_path = '../data/dataset_new'  # updated path\n",
        "max_seq_length = 512\n",
        "skip_overlength = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "_WJtjr5MpjkA"
      },
      "outputs": [],
      "source": [
        "def preprocess(tokenizer, config, example, max_seq_length):\n",
        "    prompt = example[\"context\"]\n",
        "    target = example[\"target\"]\n",
        "    prompt_ids = tokenizer.encode(prompt, max_length=max_seq_length, truncation=True)\n",
        "    target_ids = tokenizer.encode(\n",
        "        target,\n",
        "        max_length=max_seq_length,\n",
        "        truncation=True,\n",
        "        add_special_tokens=False)\n",
        "    input_ids = prompt_ids + target_ids + [config.eos_token_id]\n",
        "    return {\"input_ids\": input_ids, \"seq_len\": len(prompt_ids)}\n",
        "\n",
        "def read_jsonl(path, max_seq_length, skip_overlength=False):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\n",
        "        model_name, trust_remote_code=True)\n",
        "    config = AutoConfig.from_pretrained(\n",
        "        model_name, trust_remote_code=True, device_map='auto')\n",
        "    with open(path, \"r\") as f:\n",
        "        for line in tqdm(f.readlines()):\n",
        "            example = json.loads(line)\n",
        "            feature = preprocess(tokenizer, config, example, max_seq_length)\n",
        "            if skip_overlength and len(feature[\"input_ids\"]) > max_seq_length:\n",
        "                continue\n",
        "            feature[\"input_ids\"] = feature[\"input_ids\"][:max_seq_length]\n",
        "            yield feature\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tk8egozPaRG7"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69,
          "referenced_widgets": [
            "dc612097a38743309f4da8f395defb11",
            "03b52da347dd48bbb35bb20a9bfe01eb",
            "879b2202229649b7a702736c7b6c3a15",
            "d64d4b8abb504ac8975d85a0799c2394",
            "984003aa68754f2abc9866903ce1f487",
            "10321e5440774a4baac5daa923ba1173",
            "1d4fc602fb774b9794eab94d7aa53585",
            "737d6f5d37f149f6bd02eb2c57b327f9",
            "ea0c5ab360214c9fa50918fab5d3cfb1",
            "4191ede8198b4894a10614e382c61d7a",
            "76984954d6ba4483868186871fb56e3b"
          ]
        },
        "id": "B5yRJ52HpjiD",
        "outputId": "e243c211-4982-4d35-9bf8-837454b41132"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/19086 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc612097a38743309f4da8f395defb11"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = datasets.Dataset.from_generator(\n",
        "    lambda: read_jsonl(jsonl_path, max_seq_length, skip_overlength)\n",
        "    )\n",
        "dataset.save_to_disk(save_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hXbOd_8cpjT5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fb9a35e-1cad-4285-c043-b8952f681340"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in the dataset: 19086\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_from_disk\n",
        "\n",
        "loaded_dataset = load_from_disk('../data/dataset_new')\n",
        "\n",
        "num_samples = loaded_dataset.num_rows\n",
        "\n",
        "print(f'Number of samples in the dataset: {num_samples}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Xe5XKnf39Opx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCrg5Tua9Pe7"
      },
      "source": [
        "The start of LORA:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fu7GJche9RKe",
        "outputId": "48d756eb-61e2-45fb-bec6-f8bfb1b1b17e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.15.2+cu118)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.0.2+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (17.0.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard) (3.2.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n"
          ]
        }
      ],
      "source": [
        "!pip install torch torchvision torchaudio\n",
        "!pip install transformers\n",
        "!pip install loguru\n",
        "!pip install datasets\n",
        "!pip install peft\n",
        "!pip install bitsandbytes\n",
        "!pip install tensorboard\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Jpn7KAIW9RuP"
      },
      "outputs": [],
      "source": [
        "# only for WSL\n",
        "import os\n",
        "os.environ[\"PATH\"] = f\"{os.environ['PATH']}:/usr/local/cuda/bin\"\n",
        "os.environ['LD_LIBRARY_PATH'] = \"/usr/lib/wsl/lib:/usr/local/cuda/lib64\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "gW5hW4-F9RsX"
      },
      "outputs": [],
      "source": [
        "from typing import List, Dict, Optional\n",
        "\n",
        "import datasets\n",
        "import torch\n",
        "from loguru import logger\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModel,\n",
        "    AutoTokenizer,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        "    BitsAndBytesConfig\n",
        ")\n",
        "from peft import (\n",
        "    TaskType,\n",
        "    LoraConfig,\n",
        "    get_peft_model,\n",
        "    set_peft_model_state_dict,\n",
        "    prepare_model_for_kbit_training,\n",
        "    prepare_model_for_int8_training,\n",
        ")\n",
        "from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "uoLxrCAHBOy_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8324c523-f1ef-443b-dd7d-e7c194e0c819"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.23.0)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
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            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "4w2Pe1rT9Rqv"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "        output_dir='./finetuned_model',    # saved model path\n",
        "        logging_steps = 500,\n",
        "        # max_steps=10000,\n",
        "        num_train_epochs = 2,\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=8,\n",
        "        learning_rate=1e-4,\n",
        "        weight_decay=0.01,\n",
        "        warmup_steps=1000,\n",
        "        save_steps=500,\n",
        "        fp16=True,\n",
        "        # bf16=True,\n",
        "        torch_compile = False,\n",
        "        load_best_model_at_end = True,\n",
        "        evaluation_strategy=\"steps\",\n",
        "        remove_unused_columns=False,\n",
        "\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "OfKTizyP9Rmn"
      },
      "outputs": [],
      "source": [
        " # Quantization\n",
        "q_config = BitsAndBytesConfig(load_in_4bit=True,\n",
        "                                bnb_4bit_quant_type='nf4',\n",
        "                                bnb_4bit_use_double_quant=True,\n",
        "                                bnb_4bit_compute_dtype=torch.float16\n",
        "                                )"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d3ERPZodl12D"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "th_3Rnqy9Rkg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390,
          "referenced_widgets": [
            "e1d4797db9604d2f82cb9438cc4937bd",
            "c6eeb859c25747b48cfe3bb4f1a49add",
            "c0542ddee91c4070a746656c71bff567",
            "e8e6e5cce2fb41d4918f3d5087ff9547",
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        "outputId": "b4bb1c78-4e68-43b0-f29a-1e9cf04a225a"
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      "outputs": [
        {
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          "metadata": {}
        },
        {
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          },
          "metadata": {}
        },
        {
          "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,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Load tokenizer & model\n",
        "model_name = \"THUDM/chatglm2-6b\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(\n",
        "        model_name,\n",
        "        quantization_config=q_config,\n",
        "        trust_remote_code=True,\n",
        "        device='cuda'\n",
        "    )\n",
        "model = prepare_model_for_int8_training(model, use_gradient_checkpointing=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "IDbX7Ati9RiK"
      },
      "outputs": [],
      "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",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "RgPzHGUV9RgT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "215c44d1-33aa-4964-e450-70f7330d2c40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1949696 || all params: 3390261248 || trainable%: 0.05750872447219737\n"
          ]
        }
      ],
      "source": [
        "# LoRA\n",
        "target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING['chatglm']\n",
        "lora_config = LoraConfig(\n",
        "    task_type=TaskType.CAUSAL_LM,\n",
        "    inference_mode=False,\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.1,\n",
        "    target_modules=target_modules,\n",
        "    bias='none',\n",
        ")\n",
        "model = get_peft_model(model, lora_config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Qt2DjmPn9ReS"
      },
      "outputs": [],
      "source": [
        "resume_from_checkpoint = None\n",
        "if resume_from_checkpoint is not None:\n",
        "    checkpoint_name = os.path.join(resume_from_checkpoint, 'pytorch_model.bin')\n",
        "    if not os.path.exists(checkpoint_name):\n",
        "        checkpoint_name = os.path.join(\n",
        "            resume_from_checkpoint, 'adapter_model.bin'\n",
        "        )\n",
        "        resume_from_checkpoint = False\n",
        "    if os.path.exists(checkpoint_name):\n",
        "        logger.info(f'Restarting from {checkpoint_name}')\n",
        "        adapters_weights = torch.load(checkpoint_name)\n",
        "        set_peft_model_state_dict(model, adapters_weights)\n",
        "    else:\n",
        "        logger.info(f'Checkpoint {checkpoint_name} not found')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "kOT2o7FU9Rcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c013361-1f42-4e19-ab36-fde24699920b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1,949,696 || all params: 6,245,533,696 || trainable%: 0.031217444255383614\n"
          ]
        }
      ],
      "source": [
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "4PbUCglW9RaM"
      },
      "outputs": [],
      "source": [
        "# load data\n",
        "dataset = datasets.load_from_disk(\"../data/dataset_new\")\n",
        "dataset = dataset.train_test_split(0.2, shuffle=True, seed = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "dUgghgf59RYG"
      },
      "outputs": [],
      "source": [
        "class ModifiedTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        return model(\n",
        "            input_ids=inputs[\"input_ids\"],\n",
        "            labels=inputs[\"labels\"],\n",
        "        ).loss\n",
        "\n",
        "    def prediction_step(self, model: torch.nn.Module, inputs, prediction_loss_only: bool, ignore_keys = None):\n",
        "        with torch.no_grad():\n",
        "            res = model(\n",
        "                input_ids=inputs[\"input_ids\"].to(model.device),\n",
        "                labels=inputs[\"labels\"].to(model.device),\n",
        "            ).loss\n",
        "        return (res, None, None)\n",
        "\n",
        "    def save_model(self, output_dir=None, _internal_call=False):\n",
        "        from transformers.trainer import TRAINING_ARGS_NAME\n",
        "\n",
        "        os.makedirs(output_dir, exist_ok=True)\n",
        "        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n",
        "        saved_params = {\n",
        "            k: v.to(\"cpu\") for k, v in self.model.named_parameters() if v.requires_grad\n",
        "        }\n",
        "        torch.save(saved_params, os.path.join(output_dir, \"adapter_model.bin\"))\n",
        "\n",
        "def data_collator(features: list) -> dict:\n",
        "    len_ids = [len(feature[\"input_ids\"]) for feature in features]\n",
        "    longest = max(len_ids)\n",
        "    input_ids = []\n",
        "    labels_list = []\n",
        "    for ids_l, feature in sorted(zip(len_ids, features), key=lambda x: -x[0]):\n",
        "        ids = feature[\"input_ids\"]\n",
        "        seq_len = feature[\"seq_len\"]\n",
        "        labels = (\n",
        "            [tokenizer.pad_token_id] * (seq_len - 1) + ids[(seq_len - 1) :] + [tokenizer.pad_token_id] * (longest - ids_l)\n",
        "        )\n",
        "        ids = ids + [tokenizer.pad_token_id] * (longest - ids_l)\n",
        "        _ids = torch.LongTensor(ids)\n",
        "        labels_list.append(torch.LongTensor(labels))\n",
        "        input_ids.append(_ids)\n",
        "    input_ids = torch.stack(input_ids)\n",
        "    labels = torch.stack(labels_list)\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"labels\": labels,\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ITubEZSK9RVv"
      },
      "outputs": [],
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from transformers.integrations import TensorBoardCallback"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "Cw4Zik6a9RT3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "82adfbe5-05bf-4943-cb7c-3e915b0102b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are adding a <class 'transformers.integrations.TensorBoardCallback'> to the callbacks of this Trainer, but there is already one. The currentlist of callbacks is\n",
            ":DefaultFlowCallback\n",
            "TensorBoardCallback\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "WARNING:transformers_modules.THUDM.chatglm2-6b.8fd7fba285f7171d3ae7ea3b35c53b6340501ed1.modeling_chatglm:`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='954' max='954' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [954/954 40:26, Epoch 1/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>9.592700</td>\n",
              "      <td>6.024675</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Train\n",
        "writer = SummaryWriter()\n",
        "trainer = ModifiedTrainer(\n",
        "    model=model,\n",
        "    args=training_args,             # Trainer args\n",
        "    train_dataset=dataset[\"train\"], # Training set\n",
        "    eval_dataset=dataset[\"test\"],   # Testing set\n",
        "    data_collator=data_collator,    # Data Collator\n",
        "    callbacks=[TensorBoardCallback(writer)],\n",
        ")\n",
        "trainer.train()\n",
        "writer.close()\n",
        "# save model\n",
        "model.save_pretrained(training_args.output_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "HUYxzwS_9lMI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "362712b4-2a50-4d2a-a97c-9c80f7604ecc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/./finetuned_model/ (stored 0%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/ (stored 0%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/optimizer.pt (deflated 7%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/trainer_state.json (deflated 53%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/scheduler.pt (deflated 49%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/rng_state.pth (deflated 28%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/adapter_model.bin (deflated 8%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/training_args.bin (deflated 48%)\n",
            "  adding: content/./finetuned_model/runs/ (stored 0%)\n",
            "  adding: content/./finetuned_model/runs/Oct05_20-54-53_87b54dd2f706/ (stored 0%)\n",
            "  adding: content/./finetuned_model/runs/Oct05_20-54-53_87b54dd2f706/events.out.tfevents.1696539422.87b54dd2f706.2749.1 (deflated 59%)\n",
            "  adding: content/./finetuned_model/adapter_model.bin (deflated 8%)\n",
            "  adding: content/./finetuned_model/README.md (deflated 39%)\n",
            "  adding: content/./finetuned_model/adapter_config.json (deflated 42%)\n"
          ]
        }
      ],
      "source": [
        "!zip -r /content/saved_model.zip /content/{training_args.output_dir}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "s-7s2Cjw9pAM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "22911882-063a-429c-cc31-dcd35f13cb42"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_688ef3f0-00ee-43a3-bbb2-f9b56892ea67\", \"saved_model.zip\", 28950853)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.download('/content/saved_model.zip')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "unRoLshR9RQZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a259e730-9c2e-47be-8d3b-b24e62c6bda4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size: 29.84746265411377 MB\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "def get_folder_size(folder_path):\n",
        "    total_size = 0\n",
        "    for dirpath, _, filenames in os.walk(folder_path):\n",
        "        for f in filenames:\n",
        "            fp = os.path.join(dirpath, f)\n",
        "            total_size += os.path.getsize(fp)\n",
        "    return total_size / 1024 / 1024  # Size in MB\n",
        "\n",
        "model_size = get_folder_size(training_args.output_dir)\n",
        "print(f\"Model size: {model_size} MB\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76g_Qlp8t_Yp"
      },
      "source": [
        "Inference in benchmarks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "ehjG2bpft_OH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9d99aec9-7aaf-4962-8c70-6aa33c58a6c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers==4.30.2 in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
            "Collecting peft==0.4.0\n",
            "  Downloading peft-0.4.0-py3-none-any.whl (72 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/72.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (3.12.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.30.2) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft==0.4.0) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.4.0) (2.0.1+cu118)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from peft==0.4.0) (0.23.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers==4.30.2) (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==4.30.2) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.4.0) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.4.0) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.4.0) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.4.0) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft==0.4.0) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft==0.4.0) (17.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.30.2) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.30.2) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.30.2) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.30.2) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft==0.4.0) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft==0.4.0) (1.3.0)\n",
            "Installing collected packages: peft\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.5.0\n",
            "    Uninstalling peft-0.5.0:\n",
            "      Successfully uninstalled peft-0.5.0\n",
            "Successfully installed peft-0.4.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "peft"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (17.0.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (17.0.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.0.1+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.30.2)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from peft) (0.23.0)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft) (0.3.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft) (17.0.2)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate->peft) (0.16.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.13.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate->peft) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.5)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.15)\n",
            "Requirement already satisfied: fsspec[http]<2023.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.16.4)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.30.2 peft==0.4.0\n",
        "!pip install sentencepiece\n",
        "!pip install accelerate\n",
        "!pip install torch\n",
        "!pip install peft\n",
        "!pip install datasets\n",
        "!pip install bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "y5jyY7S_uEls",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ced0133-2103-4a1c-a9f1-99c425767b19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'FinNLP'...\n",
            "remote: Enumerating objects: 1316, done.\u001b[K\n",
            "remote: Counting objects: 100% (375/375), done.\u001b[K\n",
            "remote: Compressing objects: 100% (166/166), done.\u001b[K\n",
            "remote: Total 1316 (delta 219), reused 303 (delta 175), pack-reused 941\u001b[K\n",
            "Receiving objects: 100% (1316/1316), 4.21 MiB | 14.51 MiB/s, done.\n",
            "Resolving deltas: 100% (592/592), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/AI4Finance-Foundation/FinNLP.git\n",
        "\n",
        "import sys\n",
        "sys.path.append('/content/FinNLP/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "zRsmSTFZuEjt"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "from finnlp.benchmarks.fpb import test_fpb\n",
        "from finnlp.benchmarks.fiqa import test_fiqa , add_instructions\n",
        "from finnlp.benchmarks.tfns import test_tfns\n",
        "from finnlp.benchmarks.nwgi import test_nwgi\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "EBqKeUYV9VjF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "outputId": "3b002c35-50fc-4d8f-9c80-83cbc629312b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
            "Collecting peft\n",
            "  Using cached peft-0.5.0-py3-none-any.whl (85 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.0.1+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.30.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.1)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from peft) (0.23.0)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft) (0.3.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft) (17.0.2)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate->peft) (0.16.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.13.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate->peft) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
            "Installing collected packages: peft\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.4.0\n",
            "    Uninstalling peft-0.4.0:\n",
            "      Successfully uninstalled peft-0.4.0\n",
            "Successfully installed peft-0.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "peft"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install --upgrade peft\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "gRRw9drdA2hv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed6b4254-f65e-4d20-8c42-0ac7acf7f6d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "LxCAhg9QpkyI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "049426bf-93fa-4aa9-8515-3a27542340c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path exists.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "# Define the path you want to check\n",
        "path_to_check = \"/content/drive/My Drive/finGPTresearch/content\"\n",
        "\n",
        "# Check if the specified path exists\n",
        "if os.path.exists(path_to_check):\n",
        "    print(\"Path exists.\")\n",
        "else:\n",
        "    print(\"Path does not exist.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "bRljPCKC_srt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69,
          "referenced_widgets": [
            "5804f28620db474c868856456b3b908a",
            "5c3f41ef7060488a82f1ba43808ec003",
            "e85b0ad4244c4aa5a60f1e90f9e03632",
            "9769d17d084147b4ab3d07594142e7d6",
            "d26c2db6bbf74f4586c716eaed19b0bf",
            "de27f4bb7ff04e5e8fe7ef5fe9ac76a5",
            "855e5f9a534e40f8a1ea5631ef418794",
            "817910620c6547cabefd81cb4643f9ad",
            "9e3406c76eef44e88eceb28e579efa66",
            "f6470ec821944528889990c8f09b2390",
            "ba0db288453e4790b49282cb9cb7d6db"
          ]
        },
        "outputId": "cca076f0-a583-4733-b196-faf50ddea250"
      },
      "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": "5804f28620db474c868856456b3b908a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "base_model = \"THUDM/chatglm2-6b\"\n",
        "peft_model = training_args.output_dir\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(base_model, trust_remote_code=True, load_in_8bit=True, device_map=\"auto\")\n",
        "\n",
        "model = PeftModel.from_pretrained(model, peft_model)\n",
        "\n",
        "model = model.eval()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "w37K19SvlwYh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69,
          "referenced_widgets": [
            "04eddb43168e4240bee34b9b4ea11bc5",
            "cf15bb119fda4b3eab82767828155540",
            "c5ee570f051e4845a293fec983a639d2",
            "ad2fc5a74c314fec98f95b028401afec",
            "b7168c527898478bab660add2d317c82",
            "3db5b101a6ca4f6fa3c8fc703c4b9919",
            "f0e009d1b014494c92606419bceed64f",
            "eeb601fbdc594b6dab14048e708dab04",
            "56d2a7edfd864bb781bb87624e922583",
            "f0cd51c8101846b98f8a765443b44758",
            "e443c1b1e68e4173a9bf24f6623f36b3"
          ]
        },
        "outputId": "7d393b6c-f9df-4261-d3db-588f33861c64"
      },
      "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": "04eddb43168e4240bee34b9b4ea11bc5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "base_model = \"THUDM/chatglm2-6b\"\n",
        "peft_model = \"/content/drive/My Drive/finGPTresearch/content/finetuned_model\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(base_model, trust_remote_code=True, load_in_8bit=True, device_map=\"auto\")\n",
        "\n",
        "model = PeftModel.from_pretrained(model, peft_model)\n",
        "\n",
        "model = model.eval()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "rv5tja2AuEf3"
      },
      "outputs": [],
      "source": [
        "batch_size = 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "0Z4capS_uEeF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bed1de40-11b4-497e-cc03-1b6caad1ec5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: $ALLY - Ally Financial pulls outlook https://t.co/G9Zdi1boy5\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 2388. Batchsize: 8. Total steps: 299\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 299/299 [01:41<00:00,  2.96it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.8618090452261307. F1 macro: 0.828965375193647. F1 micro: 0.8618090452261307. F1 weighted (BloombergGPT): 0.8632472452505952. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# TFNS\n",
        "res = test_tfns(model, tokenizer, batch_size = batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "7we6z7kwuEcO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379,
          "referenced_widgets": [
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            "2d3d8342f1be475c9ef1be35e9524186",
            "92dc39d424844fd6a2b873593b65970b",
            "038279aa58d645418891c13b728e312c",
            "3049127619ca44f3b8331f5b0b7d79c4",
            "7f78d023327e4505845b5b78e55d3eff",
            "05e7a274a2a44ee2a2b73095760a9118",
            "1977bf43a2e24d528a6f070b6e86052e",
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        },
        "outputId": "e9a02ce9-989c-4f11-8568-d55420c2d15a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.04k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6640349ce41407a923a68bdcf43f7c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/13.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3049127619ca44f3b8331f5b0b7d79c4"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab9433191b0b4ae89b182a291a13fb4f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/682k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cf275e866774364a3efac08c80179c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/4846 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae288ce4969c4395807957c4f4ae72da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: L&T has also made a commitment to redeem the remaining shares by the end of 2011 .\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 1212. Batchsize: 8. Total steps: 152\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 152/152 [00:51<00:00,  2.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.7970297029702971. F1 macro: 0.7575108820595529. F1 micro: 0.7970297029702971. F1 weighted (BloombergGPT): 0.7781642224313978. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# FPB\n",
        "res = test_fpb(model, tokenizer, batch_size = batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "UTpEPwnIuEaN",
        "colab": {
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        },
        "outputId": "5573087d-c85e-4fd6-af91-6e92e0541d34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
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        {
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          "data": {
            "text/plain": [
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        {
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          "data": {
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              "Downloading data:   0%|          | 0.00/25.3k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd2ed0158ca645189f8a2ee28ee78d4d"
            }
          },
          "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": "c828f651560542d6b51eab3fac65e9ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34f9f59b2ec545c78bc168bb9520df2f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ba00395f2794b2bae8821374bcac651"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: This $BBBY stock options trade would have more than doubled your money https://t.co/Oa0loiRIJL via @TheStreet\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 275. Batchsize: 8. Total steps: 35\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 35/35 [00:12<00:00,  2.75it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.6254545454545455. F1 macro: 0.5741047534089461. F1 micro: 0.6254545454545455. F1 weighted (BloombergGPT): 0.6876670875957228. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# FiQA\n",
        "res = test_fiqa(model, tokenizer, prompt_fun = add_instructions, batch_size = batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "tWl6ILV2uEYA",
        "colab": {
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          "height": 443,
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        "outputId": "651d1077-cc86-4055-9fbd-fd1fb83636af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/682 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
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          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
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              "version_minor": 0,
              "model_id": "dd5752edc8564ab1808b0220d3fd92bc"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f357c6d499104257bc351e94848cc1ed"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1b3bbef38ed45169ba74354fc3baf43"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e77aba83479417b91eb2400e9d3ba17"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/16184 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5082ef1f20bb4f01bf22efb0f1075070"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/4047 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fadc6095c8814caf82f570052dd5d7a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: In the latest trading session, Adobe Systems (ADBE) closed at $535.98, marking a +0.31% move from the previous day.\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 4047. Batchsize: 8. Total steps: 506\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "100%|██████████| 506/506 [02:58<00:00,  2.84it/s]"
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            "Acc: 0.5688164072152212. F1 macro: 0.569744027675689. F1 micro: 0.5688164072152212. F1 weighted (BloombergGPT): 0.5614102592371769. \n"
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        "# NWGI\n",
        "res = test_nwgi(model, tokenizer, batch_size = batch_size)"
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        "Conclusion:\n",
        "\n",
        "***TFNS:***\n",
        "Preivous:\n",
        "Acc: 0.876465661641541. F1 macro: 0.8422150651552788. F1 micro: 0.876465661641541. F1 weighted (BloombergGPT): 0.8760116165894634.\n",
        "\n",
        "Current:\n",
        "Acc: 0.8618090452261307. F1 macro: 0.828965375193647. F1 micro: 0.8618090452261307. F1 weighted (BloombergGPT): 0.8632472452505952.\n",
        "\n",
        "\n",
        "\n",
        "----\n",
        "***FRB:***\n",
        "Preivous:\n",
        "Acc: 0.8531353135313532. F1 macro: 0.8355311905054061. F1 micro: 0.8531353135313532. F1 weighted (BloombergGPT): 0.8518766219402919.\n",
        "\n",
        "Current:\n",
        "Acc: 0.7970297029702971. F1 macro: 0.7575108820595529. F1 micro: 0.7970297029702971. F1 weighted (BloombergGPT): 0.7781642224313978.\n",
        "\n",
        "----\n",
        "***FiQA:***\n",
        "Preivous:\n",
        "Acc: 0.8327272727272728. F1 macro: 0.7457078313253013. F1 micro: 0.8327272727272728. F1 weighted (BloombergGPT): 0.8475613362541076.\n",
        "\n",
        "Current:\n",
        "Acc: 0.6254545454545455. F1 macro: 0.5741047534089461. F1 micro: 0.6254545454545455. F1 weighted (BloombergGPT): 0.6876670875957228.\n",
        "\n",
        "\n",
        "----\n",
        "***NWGI:***\n",
        "Preivous:\n",
        "Acc: 0.6375092661230541. F1 macro: 0.6449413175330677. F1 micro: 0.6375092661230541. F1 weighted (BloombergGPT): 0.6368355381269861.\n",
        "\n",
        "Current:\n",
        "Acc: 0.5688164072152212. F1 macro: 0.569744027675689. F1 micro: 0.5688164072152212. F1 weighted (BloombergGPT): 0.5614102592371769."
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