Finance_Sentimental_Analysis_Mistral_7b.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eK2HFidBV7KF",
        "outputId": "f50b8961-e8fa-424f-b777-898e24d970ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AihKy5J6Jz2b",
        "outputId": "fd3bf210-90c8-4780-ff7a-e8149d904a4d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "HUSVJbSxKP0n"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/drive/MyDrive/kaggle_api_credentials/kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "huJz-4T2KcFo"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "AqvVZnN7Ke89"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download ankurzing/sentiment-analysis-for-financial-news"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ei45G8e-LyoA",
        "outputId": "5bfadae4-9258-4a80-80e5-29de062feb8a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading sentiment-analysis-for-financial-news.zip to /content\n",
            "100% 903k/903k [00:00<00:00, 1.60MB/s]\n",
            "100% 903k/903k [00:00<00:00, 1.60MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/sentiment-analysis-for-financial-news.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrCBCBgrMyHP",
        "outputId": "01fc2036-a412-4c4e-db46-480f2db6422e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/sentiment-analysis-for-financial-news.zip\n",
            "  inflating: FinancialPhraseBank/License.txt  \n",
            "  inflating: FinancialPhraseBank/README.txt  \n",
            "  inflating: FinancialPhraseBank/Sentences_50Agree.txt  \n",
            "  inflating: FinancialPhraseBank/Sentences_66Agree.txt  \n",
            "  inflating: FinancialPhraseBank/Sentences_75Agree.txt  \n",
            "  inflating: FinancialPhraseBank/Sentences_AllAgree.txt  \n",
            "  inflating: all-data.csv            \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Creation"
      ],
      "metadata": {
        "id": "kiyHbaLxOBbK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNrApYA5RR1s",
        "outputId": "f0994f59-62e8-4d73-8670-0fee711c8a41"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "filename = \"/content/all-data.csv\"\n",
        "\n",
        "df = pd.read_csv(filename,\n",
        "                 names=[\"sentiment\", \"text\"],\n",
        "                 encoding=\"utf-8\", encoding_errors=\"replace\")"
      ],
      "metadata": {
        "id": "T8p70NVlRdXY"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "D6ka0EnWRftG",
        "outputId": "80b162d0-4efb-49c3-bad7-bd7775d5396f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sentiment                                               text\n",
              "0   neutral  According to Gran , the company has no plans t...\n",
              "1   neutral  Technopolis plans to develop in stages an area...\n",
              "2  negative  The international electronic industry company ...\n",
              "3  positive  With the new production plant the company woul...\n",
              "4  positive  According to the company 's updated strategy f...\n",
              "5  positive  FINANCING OF ASPOCOMP 'S GROWTH Aspocomp is ag...\n",
              "6  positive  For the last quarter of 2010 , Componenta 's n...\n",
              "7  positive  In the third quarter of 2010 , net sales incre...\n",
              "8  positive  Operating profit rose to EUR 13.1 mn from EUR ...\n",
              "9  positive  Operating profit totalled EUR 21.1 mn , up fro..."
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dc5dc59b-2d87-4e04-a0c9-5e3a2cf601f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4846,\n  \"fields\": [\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4838,\n        \"samples\": [\n          \"The Company serves approximately 3,000 customers in over 100 countries .\",\n          \"On Dec. 1 , Grimaldi acquired 1.5 million shares and a 50.1-percent stake in Finnlines .\",\n          \"The extracted filtrates are very high in clarity while the dried filter cakes meet required transport moisture limits (TMLs)for their ore grades .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = list()\n",
        "X_test = list()\n",
        "for sentiment in [\"positive\", \"neutral\", \"negative\"]:\n",
        "    train, test  = train_test_split(df[df.sentiment==sentiment],\n",
        "                                    train_size=300,\n",
        "                                    test_size=300,\n",
        "                                    random_state=42)\n",
        "    X_train.append(train)\n",
        "    X_test.append(test)\n",
        "\n",
        "X_train = pd.concat(X_train).sample(frac=1, random_state=10)\n",
        "X_test = pd.concat(X_test)\n",
        "\n",
        "eval_idx = [idx for idx in df.index if idx not in list(train.index) + list(test.index)]\n",
        "X_eval = df[df.index.isin(eval_idx)]\n",
        "X_eval = (X_eval\n",
        "          .groupby('sentiment', group_keys=False)\n",
        "          .apply(lambda x: x.sample(n=50, random_state=10, replace=True)))\n",
        "X_train = X_train.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "fX45PHCEODaD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(data_point):\n",
        "    return f\"\"\"\n",
        "            Analyze the sentiment of the news headline enclosed in square brackets,\n",
        "            determine if it is positive, neutral, or negative, and return the answer as\n",
        "            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\n",
        "\n",
        "            [{data_point[\"text\"]}] = {data_point[\"sentiment\"]}\n",
        "            \"\"\".strip()"
      ],
      "metadata": {
        "id": "qoLcjb2WRyo_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "def generate_test_prompt(data_point):\n",
        "    return f\"\"\"\n",
        "            Analyze the sentiment of the news headline enclosed in square brackets,\n",
        "            determine if it is positive, neutral, or negative, and return the answer as\n",
        "            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\n",
        "\n",
        "            [{data_point[\"text\"]}] =\n",
        "\n",
        "            \"\"\".strip()\n",
        "\n",
        "X_train = pd.DataFrame(X_train.apply(generate_prompt, axis=1),\n",
        "                       columns=[\"text\"])\n",
        "X_eval = pd.DataFrame(X_eval.apply(generate_prompt, axis=1),\n",
        "                      columns=[\"text\"])\n",
        "\n",
        "y_true = X_test.sentiment\n",
        "X_test = pd.DataFrame(X_test.apply(generate_test_prompt, axis=1), columns=[\"text\"])\n",
        "\n",
        "train_data = Dataset.from_pandas(X_train)\n",
        "eval_data = Dataset.from_pandas(X_eval)"
      ],
      "metadata": {
        "id": "17D0Tf66QjF9"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import (accuracy_score,\n",
        "                             classification_report,\n",
        "                             confusion_matrix)\n",
        "import numpy as np\n",
        "def evaluate(y_true, y_pred):\n",
        "    labels = ['positive', 'neutral', 'negative']\n",
        "    mapping = {'positive': 2, 'neutral': 1, 'none':1, 'negative': 0}\n",
        "    def map_func(x):\n",
        "        return mapping.get(x, 1)\n",
        "\n",
        "    y_true = np.vectorize(map_func)(y_true)\n",
        "    y_pred = np.vectorize(map_func)(y_pred)\n",
        "\n",
        "    # Calculate accuracy\n",
        "    accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
        "    print(f'Accuracy: {accuracy:.3f}')\n",
        "\n",
        "    # Generate accuracy report\n",
        "    unique_labels = set(y_true)  # Get unique labels\n",
        "\n",
        "    for label in unique_labels:\n",
        "        label_indices = [i for i in range(len(y_true))\n",
        "                         if y_true[i] == label]\n",
        "        label_y_true = [y_true[i] for i in label_indices]\n",
        "        label_y_pred = [y_pred[i] for i in label_indices]\n",
        "        accuracy = accuracy_score(label_y_true, label_y_pred)\n",
        "        print(f'Accuracy for label {label}: {accuracy:.3f}')\n",
        "\n",
        "    # Generate classification report\n",
        "    class_report = classification_report(y_true=y_true, y_pred=y_pred)\n",
        "    print('\\nClassification Report:')\n",
        "    print(class_report)\n",
        "\n",
        "    # Generate confusion matrix\n",
        "    conf_matrix = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[0, 1, 2])\n",
        "    print('\\nConfusion Matrix:')\n",
        "    print(conf_matrix)"
      ],
      "metadata": {
        "id": "v771CFyVTLXk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Model Gemma-7b using Unslot"
      ],
      "metadata": {
        "id": "Pcb_t1VNZzNn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "import torch\n",
        "major_version, minor_version = torch.cuda.get_device_capability()\n",
        "# Must install separately since Colab has torch 2.2.1, which breaks packages\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "if major_version >= 8:\n",
        "    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n",
        "    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n",
        "else:\n",
        "    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n",
        "    !pip install --no-deps xformers trl peft accelerate bitsandbytes\n",
        "pass"
      ],
      "metadata": {
        "id": "XJMIgnDfZ7I7"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
        "fourbit_models = [\n",
        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
        "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-it-bnb-4bit\", # Instruct version of Gemma 7b\n",
        "    \"unsloth/gemma-2b-bnb-4bit\",\n",
        "    \"unsloth/gemma-2b-it-bnb-4bit\", # Instruct version of Gemma 2b\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/mistral-7b-bnb-4bit\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328,
          "referenced_widgets": [
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        },
        "id": "A7DMBI6nZ_kS",
        "outputId": "57b4ed3a-b004-4f10-e89f-8f2b91165495"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==((====))==  Unsloth: Fast Mistral patching release 2024.3\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.25. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/4.13G [00:00<?, ?B/s]"
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              "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
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              "tokenizer_config.json:   0%|          | 0.00/971 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
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              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/438 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import bitsandbytes as bnb\n",
        "def find_all_linear_names(model):\n",
        "  cls = bnb.nn.Linear4bit #if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n",
        "  lora_module_names = set()\n",
        "  for name, module in model.named_modules():\n",
        "    if isinstance(module, cls):\n",
        "      names = name.split('.')\n",
        "      lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
        "    if 'lm_head' in lora_module_names: # needed for 16-bit\n",
        "      lora_module_names.remove('lm_head')\n",
        "  return list(lora_module_names)"
      ],
      "metadata": {
        "id": "Vw0LLQC9aQXH"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modules = find_all_linear_names(model)\n",
        "print(modules)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vF0V5B5raR_l",
        "outputId": "9ec0c0a7-245b-4a0b-93bc-86ce7f03be12"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['v_proj', 'down_proj', 'gate_proj', 'up_proj', 'q_proj', 'k_proj', 'o_proj']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "def predict(X_test, model, tokenizer):\n",
        "    y_pred = []\n",
        "    for i in tqdm(range(len(X_test))):\n",
        "        prompt = X_test.iloc[i][\"text\"]\n",
        "        input_ids = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "        outputs= model.generate(**input_ids,pad_token_id=tokenizer.eos_token_id, max_new_tokens=1,temperature=0.0)\n",
        "        result = tokenizer.decode(outputs[0])\n",
        "        answer = result.split(\"=\")[-1].lower()\n",
        "        if \"positive\" in answer:\n",
        "            y_pred.append(\"positive\")\n",
        "        elif \"negative\" in answer:\n",
        "            y_pred.append(\"negative\")\n",
        "        elif \"neutral\" in answer:\n",
        "            y_pred.append(\"neutral\")\n",
        "        else:\n",
        "            y_pred.append(\"none\")\n",
        "    return y_pred"
      ],
      "metadata": {
        "id": "RLfVEdcuanSO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = predict(X_test, model, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v83aN5iKO8Sk",
        "outputId": "41f2a223-1a1d-41c9-de97-93a0be89cd65"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 900/900 [17:08<00:00,  1.14s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pi_IfeV0aqsO",
        "outputId": "b63475d2-bec6-41bc-a2a0-40ca7c160e15"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.649\n",
            "Accuracy for label 0: 0.793\n",
            "Accuracy for label 1: 0.270\n",
            "Accuracy for label 2: 0.883\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.79      0.85       300\n",
            "           1       0.52      0.27      0.35       300\n",
            "           2       0.55      0.88      0.68       300\n",
            "\n",
            "    accuracy                           0.65       900\n",
            "   macro avg       0.66      0.65      0.63       900\n",
            "weighted avg       0.66      0.65      0.63       900\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[238  44  18]\n",
            " [ 18  81 201]\n",
            " [  3  32 265]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fine Tuning the model  \n",
        "\n",
        "Since accuracy is 66 percent"
      ],
      "metadata": {
        "id": "gTn9aN3kg1iU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = modules,\n",
        "    lora_alpha = 32,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    use_gradient_checkpointing = True,\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ],
      "metadata": {
        "id": "wG0K50mMhbSj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bce4fe9d-a0d8-4f06-f269-08a2d76aa25e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.3 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=eval_data,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 100,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
        "        bf16 = torch.cuda.is_bf16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "8a32f1e1e80e48849fbc66088e5aa4c0",
            "c0dbb4854a644feda7872016effb341a",
            "41967b8e58ee401ba1c757683cc31eec",
            "67e2776fa43e4a4b9a41b010b019ae71",
            "a34d05b6fb024820be52d7bb1fabeb12",
            "9f9b2ee76a4444d3bf204fea50d6259f",
            "6276c54239f340fc86499e7c68a652ef",
            "b46d5056808a4df6a155676be0592d7f",
            "a1461a3ca1b84627a988041cd3b7bc57",
            "454fce1571504eff917071952e59b7f1",
            "2f0771114c30465992a9b21613805014",
            "a35569947b6c41c4a00f4cb041c36b86",
            "c815ff4426074866bd1bb10e75551c65",
            "4cdad773bc864170ac0b0324835f029a",
            "b5f0864dc3964588aafa1b03deff0fc8",
            "438848d15798461988172cefe7f4cbc8",
            "a8b0b7de53564136920870e3c452a66a",
            "801a21a03ed44cefbe8089bbdd0257a0",
            "a5d2419c2e294b88bc7268a4b18b19ba",
            "f8b0443a09674a18bddc95263c89626a",
            "61a355d7c081498d915359ceefa12762",
            "73402ea1f6d543238b4ea3fee0b4a5b3"
          ]
        },
        "id": "BRptslqug-Zx",
        "outputId": "1ea222ea-f279-408b-a029-05da696be428"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/900 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a32f1e1e80e48849fbc66088e5aa4c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a35569947b6c41c4a00f4cb041c36b86"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_stats = trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HKCLraqahLz8",
        "outputId": "78cc2d42-0618-4add-fa58-a654d4df53c0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 900 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 100\n",
            " \"-____-\"     Number of trainable parameters = 167,772,160\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 08:04, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.328400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.319400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.859900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.516800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.188500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.072800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.012300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.043800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.057000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.790900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.768900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.888300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.897200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.002500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.087500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.612100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.957000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.698500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.835000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.769100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.838300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.872000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.666600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.640600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.823900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.817100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.094200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.814000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.642700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.717100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.690600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.872700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.797300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.659200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.949500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.657900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.594800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.827100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.906100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.661400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.835200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.678400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.762700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.604900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.726900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.800200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.822900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.779400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.791900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.868300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.873400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.863500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.684800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.923700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.650200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.674100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.644500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.813500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.662100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.812300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.696400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.682200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.695700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.871100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.665100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.747900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.715700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.789700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.698400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.668400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.579900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.626900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.598800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.743000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.826700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.792400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.832600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.585100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.617000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.665300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.850900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.692000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.631400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.874000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.789800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.686900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.856300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.840800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.731500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.779800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.736800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.798800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.867000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.994900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.841300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.976200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.796700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory         /max_memory*100, 3)\n",
        "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "nJR36LfPmqEx",
        "outputId": "14a3342d-0466-4930-df0a-05a123b0c903"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'start_gpu_memory' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-21a4da9893ae>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#@title Show final memory and time stats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mused_memory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_memory_reserved\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mused_memory_for_lora\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_gpu_memory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\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      4\u001b[0m \u001b[0mused_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m         \u001b[0;34m/\u001b[0m\u001b[0mmax_memory\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlora_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory_for_lora\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mmax_memory\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'start_gpu_memory' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"mistral-7b-unsloth-bfsi-Sentiment-Analysis\")"
      ],
      "metadata": {
        "id": "zYneRg5djsJc"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# After training it the accuracy increased to 85% from 60%"
      ],
      "metadata": {
        "id": "VfAUEqIoleyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = predict(X_test, model, tokenizer)\n",
        "evaluate(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_skPHjikSqN",
        "outputId": "4a847adc-c7d9-46f2-b35d-8650c6865dbf"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 900/900 [04:31<00:00,  3.32it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.857\n",
            "Accuracy for label 0: 0.980\n",
            "Accuracy for label 1: 0.813\n",
            "Accuracy for label 2: 0.777\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.98      0.95       300\n",
            "           1       0.79      0.81      0.80       300\n",
            "           2       0.85      0.78      0.81       300\n",
            "\n",
            "    accuracy                           0.86       900\n",
            "   macro avg       0.86      0.86      0.86       900\n",
            "weighted avg       0.86      0.86      0.86       900\n",
            "\n",
            "\n",
            "Confusion Matrix:\n",
            "[[294   5   1]\n",
            " [ 15 244  41]\n",
            " [  7  60 233]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation = pd.DataFrame({'text': X_test[\"text\"],\n",
        "                           'y_true':y_true,\n",
        "                           'y_pred': y_pred},\n",
        "                         )"
      ],
      "metadata": {
        "id": "J6pQLI4RlthI"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation.to_csv(\"test_predictions.csv\", index=False)"
      ],
      "metadata": {
        "id": "A2_EnPAAlvuH"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filename = \"/content/test_predictions.csv\"\n",
        "\n",
        "output_df = pd.read_csv(filename,\n",
        "                 names=[\"sentiment\", \"text\"],\n",
        "                 encoding=\"utf-8\", encoding_errors=\"replace\")"
      ],
      "metadata": {
        "id": "BtHifEjKl4L-"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "70C1o88Nl9R_",
        "outputId": "06ae5eb2-36ff-4102-a6de-73b663b3e6f6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   sentiment      text\n",
              "text                                                  y_true    y_pred\n",
              "Analyze the sentiment of the news headline encl...  positive   neutral\n",
              "Analyze the sentiment of the news headline encl...  positive  positive\n",
              "Analyze the sentiment of the news headline encl...  positive  negative\n",
              "Analyze the sentiment of the news headline encl...  positive  positive\n",
              "Analyze the sentiment of the news headline encl...  positive   neutral\n",
              "Analyze the sentiment of the news headline encl...  positive   neutral\n",
              "Analyze the sentiment of the news headline encl...  positive  positive\n",
              "Analyze the sentiment of the news headline encl...  positive   neutral\n",
              "Analyze the sentiment of the news headline encl...  positive   neutral"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea9682ba-5a27-4432-a9fb-946ed7c9a6e9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>sentiment</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>text</th>\n",
              "      <td>y_true</td>\n",
              "      <td>y_pred</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [The new agreement , which expands a long-established cooperation between the companies , involves the transfer of certain engineering and documentation functions from Larox to Etteplan .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [( ADP News ) - Finnish handling systems provider Cargotec Oyj ( HEL : CGCBV ) announced on Friday it won orders worth EUR 10 million ( USD 13.2 m ) to deliver linkspans to Jordan , Morocco and Ireland .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [The world 's biggest magazine paper maker said the program to improve efficiency will include closing several of its least competitive mills and would cover all the company 's operations resulting in annual savings of some euro200 million US$ 240 million .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [a January 11 , 2010 EPHC board of directors has approved an increase in the quarterly dividend from $ 0.03 to $ 0.05 per share .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [With this appointment Kaupthing Bank aims to further co-ordinate Capital Markets activities within the Group and to improve the overall service to clients .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [ST. PETERSBURG , Oct 14 ( PRIME-TASS ) -- Finnish tire producer Nokian Tyres plans to invest about 50 million euros in the expansion of its tire plant in the city of Vsevolozhsk in Russia 's Leningrad Region in 2011 , the company 's President Kim Gran told reporters Thursday .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [During the past decade it has gradually divested noncore assets and bought several sports equipment makers , including California-based Fitness Products International and Sparks , Nevada-based ATEC , a leading maker of baseball and softball pitching machines .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [- BEIJING XFN-ASIA - Hong Kong-listed Standard Chartered Bank said it has signed a China mobile phone dealer financing agreement with Nokia , making it the first foreign bank to offer financing to the country 's small and medium enterprise -LR] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analyze the sentiment of the news headline enclosed in square brackets,\\n            determine if it is positive, neutral, or negative, and return the answer as\\n            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\"\\n\\n            [According to Schmardin , Nordea will most likely try to win customers over from other pension fund providers .] =</th>\n",
              "      <td>positive</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea9682ba-5a27-4432-a9fb-946ed7c9a6e9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ea9682ba-5a27-4432-a9fb-946ed7c9a6e9 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-ea9682ba-5a27-4432-a9fb-946ed7c9a6e9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2f00b313-1241-4e31-91ff-73650e3328ab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2f00b313-1241-4e31-91ff-73650e3328ab')\"\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2f00b313-1241-4e31-91ff-73650e3328ab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "output_df",
              "summary": "{\n  \"name\": \"output_df\",\n  \"rows\": 901,\n  \"fields\": [\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"positive\",\n          \"negative\",\n          \"y_true\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"y_pred\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained_merged(\"outputs\", tokenizer, save_method = \"merged_16bit\",)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ANgugu7m6Ox",
        "outputId": "e41a6ef6-e9f5-4c6b-f407-6aeff40751d0"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n",
            "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n",
            "To force `safe_serialization`, set it to `None` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 5.74 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 53%|█████▎    | 17/32 [00:00<00:00, 23.12it/s]We will save to Disk and not RAM now.\n",
            "100%|██████████| 32/32 [00:46<00:00,  1.44s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving outputs/pytorch_model-00001-of-00003.bin...\n",
            "Unsloth: Saving outputs/pytorch_model-00002-of-00003.bin...\n",
            "Unsloth: Saving outputs/pytorch_model-00003-of-00003.bin...\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login, HfApi\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ba5d4526cac74e7ca12992009defaa27",
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            "a9c6b543ac5844938180257b554d75df",
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            "ca306f382b084ceab1dcd17a2ddfdd31",
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            "dc1dfd25d8644e8fa9c64d893c8da08c",
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            "4e74239d3c0349e6bb09ec7546efe17f",
            "65e16e7dbddf487ea270a83209a196b4",
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            "40580a04365b40008a20c859994fdb9d",
            "75f10b373dbf42d88186f5fdccacca62",
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            "80ee66c7fcce4250a6dc5e9d6a594570",
            "76644a7acd7a4da78c8be353e34a966d"
          ]
        },
        "id": "R9o_nnhloZfs",
        "outputId": "501b0f5b-7008-426d-8a9a-f5b418182e5b"
      },
      "execution_count": 37,
      "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": "ba5d4526cac74e7ca12992009defaa27"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub_merged(\"mistral-7b-unsloth-bfsi-Sentiment-Analysis-merged\", tokenizer, save_method = \"merged_16bit\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318,
          "referenced_widgets": [
            "b76579508625474dbe20db0016384cc0",
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            "1fb802431592420b8200cf1e2d6e1956",
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            "13da6a4d0279476fbbd17ae09b75623f",
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            "5a0a48ee980e4409ba1c5a2af3e1ae03",
            "563947fda81543e5b302c63cdb7e7c35",
            "4fbcc6cc5790400cbeb25408debf583b",
            "9877cb214a8448b09451a79d75bfc477",
            "bc61c14e5f8446e19ff4d6db784ca568",
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            "86e2af18b1c942a8a9462624b24de513",
            "b6cedc00201e45fb8ef35d4ab93cc400",
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            "e0192bb834d6459393a63b022be7e237",
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            "f5942f1db5b74d40aaab945f2419edef",
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            "1f2c20c7a9714ab6bbb90880f22b08e7",
            "5387a1bed5054ebf886dfec26a0d5326",
            "cadc98242bdd4699902a0256a6a9fbff",
            "06ad41c3e77046c0b3bec7cd856200c4",
            "7c71fe0a61cc4a6990b0c4481878151d",
            "dc075dc593314a56859b5c12e05756d5",
            "95d84c2d99d24b4dbed13176922756a5",
            "1b0a337a6e13495f9e157885799e9077"
          ]
        },
        "id": "WOEfFDBvmstO",
        "outputId": "0c330383-3b5a-4a15-897d-3dd98a8cfa43"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 5.59 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 32/32 [01:29<00:00,  2.81s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving mistral-7b-unsloth-bfsi-Sentiment-Analysis-merged/pytorch_model-00001-of-00003.bin...\n",
            "Unsloth: Saving mistral-7b-unsloth-bfsi-Sentiment-Analysis-merged/pytorch_model-00002-of-00003.bin...\n",
            "Unsloth: Saving mistral-7b-unsloth-bfsi-Sentiment-Analysis-merged/pytorch_model-00003-of-00003.bin...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00002-of-00003.bin:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b76579508625474dbe20db0016384cc0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 3 LFS files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13da6a4d0279476fbbd17ae09b75623f"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00003.bin:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a0a48ee980e4409ba1c5a2af3e1ae03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00003-of-00003.bin:   0%|          | 0.00/4.54G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5942f1db5b74d40aaab945f2419edef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done.\n",
            "Saved merged model to https://huggingface.co/None/mistral-7b-unsloth-bfsi-Sentiment-Analysis-merged\n"
          ]
        }
      ]
    }
  ]
}