llama3_1_mental_health.ipynb

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    "<center>\n",
    "    <img src=\"https://hyperight.com/wp-content/uploads/2024/07/Meta-Llama-31-1024x576.webp\" width=4500\">\n",
    "</center>"
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   "source": [
    "[Image Reference](https://hyperight.com/wp-content/uploads/2024/07/Meta-Llama-31-1024x576.webp)\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "<div style=\"background-color: #f9f9f9; border: 1px solid #ddd; padding: 20px; text-align: center; font-family: Arial, sans-serif;\">\n",
    "    <h3 style=\"margin-bottom: 20px;\">Muhammad Imran Zaman</h3>\n",
    "    <a href=\"https://www.kaggle.com/muhammadimran112233\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://www.kaggle.com/static/images/site-logo.svg\" width=\"100\" height=\"100\" alt=\"Kaggle Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://www.linkedin.com/in/muhammad-imran-zaman\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/LinkedIn_logo_initials.png/600px-LinkedIn_logo_initials.png\" width=\"50\" height=\"50\" alt=\"LinkedIn Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://scholar.google.com/citations?user=ulVFpy8AAAAJ&hl=en\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c7/Google_Scholar_logo.svg/768px-Google_Scholar_logo.svg.png\" width=\"50\" height=\"50\" alt=\"Google Scholar Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://www.youtube.com/@consolioo\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/7/72/YouTube_social_white_square_%282017%29.svg/640px-YouTube_social_white_square_%282017%29.svg.png\" width=\"50\" height=\"50\" alt=\"YouTube Channel\">\n",
    "    </a>\n",
    "    <a href=\"https://github.com/Imran-ml\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png\" width=\"50\" height=\"50\" alt=\"GitHub Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://imranzaman-5202.medium.com/\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"data:image/png;base64,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\" width=\"50\" height=\"50\" alt=\"Medium Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://huggingface.co/ImranzamanML\" style=\"text-decoration: none;\">\n",
    "        <img src=\"https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-with-title.svg\" width=\"200\" height=\"100\" alt=\"Hugging Face Profile\">\n",
    "    </a>\n",
    "</div>\n"
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  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-11-30T07:38:51.578836",
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     "start_time": "2020-11-30T07:38:51.513370",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">  \n",
    "<h2><center><strong>Building a Mental Health Chatbot by fine tuning Llama 3.1</strong></center></h2>\n",
    "        \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">\n",
    "        <strong>Mental health</strong> is a critical aspect of overall well being for emotional, psychological and social dimensions.\n",
    "    </p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Comic Sans MS, cursive, sans-serif; color: #333; font-size: 16px;\">\n",
    "    Let's find some mental peace 😊 by fine tuning Llama 3.1.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EPvcJDepawUV"
   },
   "outputs": [],
   "source": [
    "!pip install -qqq \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\" --progress-bar off\n",
    "!pip install -qqq --no-deps \"xformers<0.0.24\" \"trl<0.9.0\" peft accelerate bitsandbytes --progress-bar off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ztq8cn4LawUX"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">\n",
    "    <p><strong>We are going to use Unsloth</strong> because it significantly enhances the efficiency of fine-tuning large language models (LLMs) specially LLaMA and Mistral. With Unsloth, we can use advanced quantization techniques, such as <strong>4-bit</strong> and <strong>16-bit quantization</strong>, to reduce the memory and speed up both training and inference. This means we can deploy powerful models even on hardware with limited resources but without compromising on performance.</p>\n",
    "\n",
    "<p> Additionally, Unsloth broad compatibility and customization options allow to do the quantization process to fit the specific needs of products. This flexibility combined with its ability to cut <strong>VRAM usage by up to 60%</strong>, makes Unsloth an essential tool in AI toolkit. Its not just about optimizing models its about making cutting-edge AI more accessible and efficient for real world applications.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yOYZFie8awUX"
   },
   "source": [
    "#### For fine tuning, I used the following setup:\n",
    "\n",
    "<ul style=\"margin1 20px; padding: 10px; list-style-type: disc;\">\n",
    "    <li><strong><a href=\"https://pytorch.org/\" target=\"_blank\">Torch 2.1.1 with CUDA 12.1</a></strong> for efficient computation.</li>\n",
    "    <li><strong><a href=\"https://github.com/unslothai/unsloth\" target=\"_blank\">Unsloth</a></strong> to achieve 2X faster training speeds for the large language model (LLM).</li>\n",
    "    <li><strong><a href=\"https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/h100/PB-11773-001_v01.pdf\" target=\"_blank\">  H100 NVL GPU</a></strong> to handle the intensive processing requirement but you can use the less power GPU as well.</li>\n",
    "</ul>\n",
    "\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T-WYpAvYawUY",
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "<ul style=\"margin: 10px; padding: 10px; list-style-type: disc;\">\n",
    "    <strong><a href=\"https://llama.meta.com/\" target=\"_blank\">Why LLaMA 3.1?</a></strong>\n",
    "\n",
    "**Its Open Source and Accessible** and offers the flexibility to customize and fine-tune it with the specific needs. Due to open source weights of the model from Meta, it is very easy to fine tune on any problem and we are going to fine tune it on mental health dataset from the <strong><a href=\"https://huggingface.co/datasets/Amod/mental_health_counseling_conversations\" target=\"_blank\">Hugging Face</a></strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YbnIKohrawUY"
   },
   "source": [
    "<h2 style=\"font-family: Arial, sans-serif; color: #4CAF50; border-bottom: 2px solid #4CAF50; padding-bottom: 5px; margin-bottom: 20px;\">\n",
    "    Python Libraries 📕 📗 📘 📙\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IE_MzoFdawUZ"
   },
   "outputs": [],
   "source": [
    "# Data Handling and Visualization\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# LLM model training\n",
    "import torch\n",
    "from trl import SFTTrainer\n",
    "from transformers import TrainingArguments, TextStreamer\n",
    "from unsloth.chat_templates import get_chat_template\n",
    "from unsloth import FastLanguageModel, is_bfloat16_supported\n",
    "from datasets import Dataset\n",
    "\n",
    "# Saving model\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "# Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qMKMFmPTawUa"
   },
   "source": [
    "<h2 style=\"font-family: Arial, sans-serif; color: #4CAF50; border-bottom: 2px solid #4CAF50; padding-bottom: 5px; margin-bottom: 20px;\">\n",
    "    Calling the dataset\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CPATz15KawUa"
   },
   "outputs": [],
   "source": [
    "data = pd.read_json(\"hf://datasets/Amod/mental_health_counseling_conversations/combined_dataset.json\", lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DxEqTOEJawUb"
   },
   "source": [
    "<h2 style=\"font-family: Arial, sans-serif; color: #4CAF50; border-bottom: 2px solid #4CAF50; padding-bottom: 5px; margin-bottom: 20px;\">\n",
    "    Exploratory data analysis 🔎 📊\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "auM6ljVLawUb"
   },
   "source": [
    "#### Five top records of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NdSS6nzOawUb",
    "outputId": "48d6b7ca-e0e1-48f6-dee3-74fe852e58c7"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Context</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I'm going through some things with my feelings...</td>\n",
       "      <td>If everyone thinks you're worthless, then mayb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I'm going through some things with my feelings...</td>\n",
       "      <td>Hello, and thank you for your question and see...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I'm going through some things with my feelings...</td>\n",
       "      <td>First thing I'd suggest is getting the sleep y...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I'm going through some things with my feelings...</td>\n",
       "      <td>Therapy is essential for those that are feelin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I'm going through some things with my feelings...</td>\n",
       "      <td>I first want to let you know that you are not ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Context  \\\n",
       "0  I'm going through some things with my feelings...   \n",
       "1  I'm going through some things with my feelings...   \n",
       "2  I'm going through some things with my feelings...   \n",
       "3  I'm going through some things with my feelings...   \n",
       "4  I'm going through some things with my feelings...   \n",
       "\n",
       "                                            Response  \n",
       "0  If everyone thinks you're worthless, then mayb...  \n",
       "1  Hello, and thank you for your question and see...  \n",
       "2  First thing I'd suggest is getting the sleep y...  \n",
       "3  Therapy is essential for those that are feelin...  \n",
       "4  I first want to let you know that you are not ...  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7FwDILF0awUd"
   },
   "source": [
    "#### Length of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BC_bQYLUawUd",
    "outputId": "94187c3d-2c6b-469f-a9ed-c099980595dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lenght of data is 3512\n"
     ]
    }
   ],
   "source": [
    "print('lenght of data is', len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hrgy9qMIawUe"
   },
   "source": [
    "#### Shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VFLIfkYTawUe",
    "outputId": "4a0be774-624d-492c-adca-f94352a05d4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3512, 2)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_4nnSj3awUf"
   },
   "source": [
    "#### Checking Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M6-Ij_kwawUf",
    "outputId": "df4c927c-2650-4ad3-d040-4ad34057c74d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.isnull().any(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-yp_W7jRawUf"
   },
   "source": [
    "#### Rows and columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vd228qJtawUg",
    "outputId": "403e9994-1997-4c3c-f15f-14c0b3300728"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of columns in the data is:   2\n"
     ]
    }
   ],
   "source": [
    "print('Count of columns in the data is:  ', len(data.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tA_G7czfawUg",
    "outputId": "ffa5c0ea-e356-447a-bf8e-d43d2a8a0bff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of rows in the data is:   3512\n"
     ]
    }
   ],
   "source": [
    "print('Count of rows in the data is:  ', len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hun-QYyHawUg"
   },
   "source": [
    "#### Lets check the lenght of words in each context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9zM9Z_ZyawUh",
    "outputId": "95ac9a79-4f48-48bd-8e01-247341fd5806"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Context_length'] = data['Context'].apply(len)\n",
    "plt.figure(figsize=(10, 3))\n",
    "sns.histplot(data['Context_length'], bins=50, kde=True)\n",
    "plt.title('Distribution of Context Lengths')\n",
    "plt.xlabel('Length of Context')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WVBVs6AZawUh"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">   <strong>Note:</strong> :</strong> As we can see above, the least number of words are 1500, and then there is a significant difference, so we are just going to use the data with 1500 words or less.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ePnToCe2awUi"
   },
   "outputs": [],
   "source": [
    "filtered_data = data[data['Context_length'] <= 1500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BLJq0qI1awUi",
    "outputId": "7b97ffca-9a50-43a9-9237-a59fd4511eae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_Context = filtered_data['Context'].apply(len)\n",
    "plt.figure(figsize=(10, 3))\n",
    "sns.histplot(ln_Context, bins=50, kde=True)\n",
    "plt.title('Distribution of Context Lengths')\n",
    "plt.xlabel('Length of Context')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z0sOAhMNawUi"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">   <strong>Note:</strong> :</strong> Now its fine to use this data.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "evQ2Fh_kawUi"
   },
   "source": [
    "#### Lets check now the lenght of words in each Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G5hlcgMpawUj",
    "outputId": "1e4c0f57-e259-44ac-bf52-7becdabe0508"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_Response = filtered_data['Response'].apply(len)\n",
    "plt.figure(figsize=(10, 3))\n",
    "sns.histplot(ln_Response, bins=50, kde=True, color='teal')\n",
    "plt.title('Distribution of Response Lengths')\n",
    "plt.xlabel('Length of Response')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IHdpypblawUj"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">   <strong>Note:</strong> :</strong> Here is also after 4000 words lenght response, there are is significant fall.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_6SNm3ZtawUj"
   },
   "outputs": [],
   "source": [
    "filtered_data = filtered_data[ln_Response <= 4000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zAXf9O9BawUj",
    "outputId": "8021c3fe-223e-452e-ad0a-2ebf45b940df"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_Response = filtered_data['Response'].apply(len)\n",
    "plt.figure(figsize=(10, 3))\n",
    "sns.histplot(ln_Response, bins=50, kde=True, color='teal')\n",
    "plt.title('Distribution of Response Lengths')\n",
    "plt.xlabel('Length of Response')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XZ0XezbuawUk"
   },
   "source": [
    "<div style=\"background-color: #f9f9f9; border-left: 6px solid #4CAF50; padding: 10px; margin: 20px 0;\">\n",
    "    <strong>Note:</strong> There is no need for such data preparation to handle the lengths of text for LLM models, but for consistency in the number of words, I just took under 4000 words as example so you can do any data preprocessing as per needs.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X7PPnzVrawUk"
   },
   "source": [
    "<h2 style=\"font-family: Arial, sans-serif; color: #4CAF50; border-bottom: 2px solid #4CAF50; padding-bottom: 5px; margin-bottom: 20px;\">\n",
    "   Model training 🧪\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GzQADu5PawUk"
   },
   "source": [
    "<img src=\"https://miro.medium.com/v2/resize:fit:1400/1*mjbBdZ1-p7cKQkp-bU7aCg.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DV32mkg2awUk"
   },
   "source": [
    "\n",
    "[Image Reference](https://miro.medium.com/v2/resize:fit:1400/1*mjbBdZ1-p7cKQkp-bU7aCg.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q05BfngtawUs"
   },
   "source": [
    "> Lets deep dive into Llama 3.1 model and train it on our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Auokv8pFawUs"
   },
   "source": [
    "### Loading the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gt6qcpmvawUs"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\"> We are going to use Llama 3.1 with 8 billion parameters, but you can use the 70 billion version as well. However, you cannot use the 405 billion parameter version yet with Unsloth because it doesn't support it yet. When support for the 405 billion version is available, you will be able to use it by simply replacing the number!\n",
    "</div>\n",
    "\n",
    "<h3 style=\"color: #388e3c; font-family: Arial, sans-serif;\">Key aspects which can be followed as per your requirement as well:</h3>\n",
    "\n",
    "<ol style=\"margin-left: 20px;\">\n",
    "    <li><strong>Max Sequence Length:</strong>\n",
    "        <p>We used <code>max_seq_length</code> 5020, the maximum number of tokens can be used in model that can handle in a single input sequence. This is crucial for tasks requiring the processing of long texts, ensuring that the model can capture more context in each pass. It can be used as per requirements.</p>\n",
    "    </li>\n",
    "    <li><strong>Loading Llama 3.1 Model:</strong>\n",
    "        <p>The model and tokenizer are loaded using <code>FastLanguageModel.from_pretrained</code> with a specific pre-trained model, <code>\"unsloth/Meta-Llama-3.1-8B-bnb-4bit\"</code>. This is optimized for 4-bit precision, which reduces memory usage and increases training speed without significantly compromising performance. The <code>load_in_4bit=True</code> parameter enables this efficient 4-bit quantization, making it more suitable for fine-tuning on less powerful hardware.</p>\n",
    "    </li>\n",
    "    <li><strong>Applying PEFT (Parameter-Efficient Fine-Tuning):</strong>\n",
    "        <p>Then we configured model using <code>get_peft_model</code>, which applies LoRA (Low-Rank Adaptation) techniques. This approach focuses on fine-tuning only specific layers or parts of the model, rather than the entire network, drastically reducing the computational resources needed.</p>\n",
    "        <p>Parameters such as <code>r=16</code> and <code>lora_alpha=16</code> adjust the complexity and scaling of these adaptations. The use of <code>target_modules</code> specifies which layers of the model should be adapted, which include key components involved in attention mechanisms like <code>q_proj</code>, <code>k_proj</code>, and <code>v_proj</code>.</p>\n",
    "        <p><code>use_rslora=True</code> activates Rank-Stabilized LoRA, which improves the stability of the fine-tuning process. <code>use_gradient_checkpointing=\"unsloth\"</code> ensures that memory usage is optimized during training by selectively storing only necessary computations, further enhancing the model's efficiency.</p>\n",
    "    </li>\n",
    "    <li><strong>Verifying Trainable Parameters:</strong>\n",
    "        <p>Finally we are using <code>model.print_trainable_parameters()</code> to print out the number of parameters that will be updated during fine-tuning, allowing to verify that only the intended parts of the model are being trained.</p>\n",
    "    </li>\n",
    "</ol>\n",
    "\n",
    "<p>This combination of techniques makes the fine-tuning process not only more efficient but also more accessible, allowing you to deploy this model even with limited computational resources.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d51SyBp9awUs"
   },
   "source": [
    "> Setting maximum lenght of tokenz 5020 is more than enough as Low-Rank Adaptation (LoRA) for training but you can use as per your data and requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AwCN3ykMawUt",
    "outputId": "52c51e87-3c1c-4249-9cb6-bca8456f91b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth 2024.8: Fast Llama patching. Transformers = 4.44.1.\n",
      "   \\\\   /|    GPU: NVIDIA H100 NVL. Max memory: 93.003 GB. Platform = Linux.\n",
      "O^O/ \\_/ \\    Pytorch: 2.1.1. CUDA = 9.0. CUDA Toolkit = 12.1.\n",
      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.23.post1. FA2 = False]\n",
      " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unsloth 2024.8 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 41,943,040 || all params: 8,072,204,288 || trainable%: 0.5196\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "max_seq_length = 5020\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name=\"unsloth/Meta-Llama-3.1-8B-bnb-4bit\",\n",
    "    max_seq_length=max_seq_length,\n",
    "    load_in_4bit=True,\n",
    "    dtype=None,\n",
    ")\n",
    "\n",
    "model = FastLanguageModel.get_peft_model(\n",
    "    model,\n",
    "    r=16,\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0,\n",
    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"up_proj\", \"down_proj\", \"o_proj\", \"gate_proj\"],\n",
    "    use_rslora=True,\n",
    "    use_gradient_checkpointing=\"unsloth\"\n",
    ")\n",
    "print(model.print_trainable_parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xRQzPI14awUt"
   },
   "source": [
    "### Prapare data for model feed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q88hLrOVawUt"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\"> Now its time to design format prompt for mental health analysis. This function analyzes the input text from a psychological perspective, identifying indicators of emotional distress, coping mechanisms, or overall mental well-being. It also highlights potential concerns or positive aspects, providing brief explanations for each observation. We are going to prepare this data for further processing by the model, ensuring that each input-output pair is clearly formatted for effective analysis.\n",
    "</div>\n",
    "\n",
    "<h3 style=\"color: #388e3c; font-family: Arial, sans-serif;\">Main points to remember:</h3>\n",
    "\n",
    "<ol style=\"margin-left: 20px;\">\n",
    "    <li><strong>Data Prompt Structure:</strong>\n",
    "        <p>The <code>data_prompt</code> is a formatted string template designed to guide the model in analyzing the provided text. It includes placeholders for the input text (the context) and the model's response. This template specifically prompts the model to identify mental health indicators, making it easier to fine-tune the model for mental health-related tasks.</p>\n",
    "    </li>\n",
    "    <li><strong>End-of-Sequence Token:</strong>\n",
    "        <p>The <code>EOS_TOKEN</code> is retrieved from the tokenizer to signify the end of each text sequence. This token is essential for the model to recognize when a prompt has ended, helping to maintain the structure of the data during training or inference.</p>\n",
    "    </li>\n",
    "    <li><strong>Formatting Function:</strong>\n",
    "        <p>The <code>formatting_prompt</code> used to take a batch of examples and formats them according to the <code>data_prompt</code>. It iterates over the input and output pairs, inserting them into the template and appending the EOS token at the end. The function then returns a dictionary containing the formatted text, ready for model training or evaluation.</p>\n",
    "    </li>\n",
    "    <li><strong>Function Output:</strong>\n",
    "        <p>The function outputs a dictionary where the key is <code>\"text\"</code> and the value is a list of formatted strings. Each string represents a fully prepared prompt for the model, combining the context, response and the structured prompt template.</p>\n",
    "    </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MtEqWVQOawUu"
   },
   "outputs": [],
   "source": [
    "data_prompt = \"\"\"Analyze the provided text from a mental health perspective. Identify any indicators of emotional distress, coping mechanisms, or psychological well-being. Highlight any potential concerns or positive aspects related to mental health, and provide a brief explanation for each observation.\n",
    "\n",
    "### Input:\n",
    "{}\n",
    "\n",
    "### Response:\n",
    "{}\"\"\"\n",
    "\n",
    "EOS_TOKEN = tokenizer.eos_token\n",
    "def formatting_prompt(examples):\n",
    "    inputs       = examples[\"Context\"]\n",
    "    outputs      = examples[\"Response\"]\n",
    "    texts = []\n",
    "    for input_, output in zip(inputs, outputs):\n",
    "        text = data_prompt.format(input_, output) + EOS_TOKEN\n",
    "        texts.append(text)\n",
    "    return { \"text\" : texts, }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LEz19boaawUu"
   },
   "source": [
    "### Format the data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Nd9zCTBFawUu"
   },
   "outputs": [],
   "source": [
    "training_data = Dataset.from_pandas(filtered_data)\n",
    "training_data = training_data.map(formatting_prompt, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNjTEgLXawUv"
   },
   "source": [
    "### Model training with custom parameters and data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BfSLRJZwawUv"
   },
   "source": [
    "> <p style=\"font-size: 14px; color: #333;\">\n",
    "        Using <code>sudo apt-get update</code> to refresh the list of available packages and <code>sudo apt-get install build-essential</code> to install essential tools. Only run this on shell if you get any error.\n",
    "    </p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gObTzUbkawUv"
   },
   "outputs": [],
   "source": [
    "#sudo apt-get update\n",
    "#sudo apt-get install build-essential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VTeMR1lvawUv"
   },
   "source": [
    "<h3 style=\"color: #388e3c; font-family: Arial, sans-serif;\">Training setup to start fine tuning!</h3>\n",
    "\n",
    "<ol style=\"margin-left: 20px;\">\n",
    "    <li><strong>Trainer Initialization:</strong>\n",
    "        <p>We are going to initialize <code>SFTTrainer</code> with the model and tokenizer, as well as the training dataset. The <code>dataset_text_field</code> parameter specifies the field in the dataset that contains the text to be used for training which we prepared above. The trainer is responsible for managing the fine-tuning process, including data handling and model updates.</p>\n",
    "    </li>\n",
    "    <li><strong>Training Arguments:</strong>\n",
    "        <p>The <code>TrainingArguments</code> class is used to define key hyperparameters for the training process. These include:</p>\n",
    "        <ul>\n",
    "            <li><code>learning_rate=3e-4</code>: Sets the learning rate for the optimizer.</li>\n",
    "            <li><code>per_device_train_batch_size=32</code>: Defines the batch size per device, optimizing GPU usage.</li>\n",
    "            <li><code>num_train_epochs=20</code>: Specifies the number of training epochs.</li>\n",
    "            <li><code>fp16=not is_bfloat16_supported()</code> and <code>bf16=is_bfloat16_supported()</code>: Enable mixed precision training to reduce memory usage, depending on hardware support.</li>\n",
    "            <li><code>optim=\"adamw_8bit\"</code>: Uses the 8-bit AdamW optimizer for efficient memory usage.</li>\n",
    "            <li><code>weight_decay=0.01</code>: Applies weight decay to prevent overfitting.</li>\n",
    "            <li><code>output_dir=\"output\"</code>: Specifies the directory where the trained model and logs will be saved.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li><strong>Training Process:</strong>\n",
    "        <p>Finally we called <code>trainer.train()</code> method to start the training process. It uses the defined parameters of our fine-tune the model, adjusting weights and learning from the provided dataset. The trainer also handles data packing and gradient accumulation, optimizing the training pipeline for better performance.</p>\n",
    "    </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "79f9e484ae554ab98df169f794b2cda5"
     ]
    },
    "id": "30dJa7yyawUw",
    "outputId": "d2083d14-962a-438e-a854-515d061bbecf"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79f9e484ae554ab98df169f794b2cda5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
      "   \\\\   /|    Num examples = 593 | Num Epochs = 20\n",
      "O^O/ \\_/ \\    Batch size per device = 32 | Gradient Accumulation steps = 4\n",
      "\\        /    Total batch size = 128 | Total steps = 80\n",
      " \"-____-\"     Number of trainable parameters = 41,943,040\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='80' max='80' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [80/80 49:24, Epoch 16/20]\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>2.337100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.130900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.112800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.045100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.048000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.936200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.096400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.974700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.904400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.892900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.982600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.873400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.815600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.935400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.713600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.866900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.871300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.920500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.808900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.835500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.857000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.751200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.639800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.775700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.763900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.620300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.815400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.750500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.632600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.647700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.592000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.722700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.624000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.548300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.552900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.566300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.538400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.443100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.547300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.412400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.438600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.321900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.395400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.309600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.277700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.429000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.233900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.283100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.298300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.244600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.079900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.246000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.182800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.079700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.169200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.931700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.108200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.995700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.095500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.023300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.906100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.010400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.989300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.957600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.870900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.864400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.031500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.963500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.780400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.840600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.855200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.873100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.864400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.774500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.927100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.763100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.774800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.868500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=80, training_loss=1.4284833692014218, metrics={'train_runtime': 3007.0031, 'train_samples_per_second': 3.944, 'train_steps_per_second': 0.027, 'total_flos': 9.2735910445056e+17, 'train_loss': 1.4284833692014218, 'epoch': 16.842105263157894})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer=SFTTrainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    train_dataset=training_data,\n",
    "    dataset_text_field=\"text\",\n",
    "    max_seq_length=max_seq_length,\n",
    "    dataset_num_proc=2,\n",
    "    packing=True,\n",
    "    args=TrainingArguments(\n",
    "        learning_rate=3e-4,\n",
    "        lr_scheduler_type=\"linear\",\n",
    "        per_device_train_batch_size=32,\n",
    "        gradient_accumulation_steps=4,\n",
    "        num_train_epochs=20,\n",
    "        fp16=not is_bfloat16_supported(),\n",
    "        bf16=is_bfloat16_supported(),\n",
    "        logging_steps=1,\n",
    "        optim=\"adamw_8bit\",\n",
    "        weight_decay=0.01,\n",
    "        warmup_steps=10,\n",
    "        output_dir=\"output\",\n",
    "        seed=0,\n",
    "    ),\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6-qIx5ZdawUw"
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zxl-iBhRawUx"
   },
   "outputs": [],
   "source": [
    "text=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here. I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it. How can I change my feeling of being worthless to everyone?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cT3M9mzOawUx"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">\n",
    "    <strong>Note:</strong> Lets use the fine-tuned model for inference in order to generate responses based on mental health-related prompts !\n",
    "</div>\n",
    "\n",
    "<h3 style=\"color: #388e3c; font-family: Arial, sans-serif;\">Here is some keys to note:</h3>\n",
    "\n",
    "<ol style=\"margin-left: 20px;\">\n",
    "        <p>The <code>model = FastLanguageModel.for_inference(model)</code> configures the model specifically for inference, optimizing its performance for generating responses.</p>\n",
    "    </li>\n",
    "        <p>The input text is tokenized using the <code>tokenizer</code>, it convert the text into a format that model can process. We are using <code>data_prompt</code> to format the input text, while the response placeholder is left empty to get response from model. The <code>return_tensors = \"pt\"</code> parameter specifies that the output should be in PyTorch tensors, which are then moved to the GPU using <code>.to(\"cuda\")</code> for faster processing.</p>\n",
    "    </li>\n",
    "        <p>The <code>model.generate</code> method generating response based on the tokenized inputs. The parameters <code>max_new_tokens = 5020</code> and <code>use_cache = True</code> ensure that the model can produce long and coherent responses efficiently by utilizing cached computation from previous layers.</p>\n",
    "    </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O0qk3PutawUx",
    "outputId": "881f4a0d-9dde-4581-ede3-06537bd8ee4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer of the question is: \n",
      "Hi there, I want to acknowledge you for reaching out to ask for help. That's the first step! It sounds like you are experiencing some depression symptoms and it is important to seek help for them as they can worsen over time. You are not worthless and this thought is only a thought, it's not the truth. A professional counsellor can help you change negative thought patterns, build coping skills and learn to challenge negative thoughts. I recommend you to contact a professional counsellor, your family doctor or a crisis hot-line to discuss your symptoms. You are not alone and you can get help! Take care. Rosanne Bermudez, CCVP, RCCWS <|im_end|>\n"
     ]
    }
   ],
   "source": [
    "model = FastLanguageModel.for_inference(model)\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    data_prompt.format(\n",
    "        #instructions\n",
    "        text,\n",
    "        #answer\n",
    "        \"\",\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens = 5020, use_cache = True)\n",
    "answer=tokenizer.batch_decode(outputs)\n",
    "answer = answer[0].split(\"### Response:\")[-1]\n",
    "print(\"Answer of the question is:\", answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Sohb0kxawUy"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">   <strong>Note:</strong> Here is how we can securely push a fine-tuned model and its tokenizer to the Hugging Face Hub so any body can use it. It can be accessed on my account <strong><a href=\"https://huggingface.co/ImranzamanML/finetuned_llama3.1\" target=\"_blank\">ImranzamanML/finetuned_llama3.1</a></strong>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "b0aec8988cbf4206b0e6cdfcfcf15fc0",
      "6415fa4e334c44b6913bf9a2db4e1452"
     ]
    },
    "id": "OEBmHxlxawUy",
    "outputId": "a8de077b-98ce-49c8-d545-ef2436e341e9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0aec8988cbf4206b0e6cdfcfcf15fc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/31.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6415fa4e334c44b6913bf9a2db4e1452",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to https://huggingface.co/ImranzamanML/finetuned_llama3.1\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"HF_TOKEN\"] = \"hugging face token key, you can create from your HF account.\"\n",
    "model.push_to_hub(\"ImranzamanML/finetuned_llama3.1\", use_auth_token=os.getenv(\"HF_TOKEN\"))\n",
    "tokenizer.push_to_hub(\"ImranzamanML/finetuned_llama3.1\", use_auth_token=os.getenv(\"HF_TOKEN\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40wFvLmhawUy"
   },
   "source": [
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\">   <strong>Note:</strong> We can also save fine-tuned model and its tokenizer locally on the machine.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TtoC1t66awUz"
   },
   "outputs": [],
   "source": [
    "model.save_pretrained(\"model/finetuned_llama3.1\")\n",
    "tokenizer.save_pretrained(\"model/finetuned_llama3.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xi13VN69awUz"
   },
   "source": [
    "\n",
    "        \n",
    "<div style=\"background-color: #f2f2f2; border-left: 5px solid #4CAF50; padding: 15px; margin: 20px 0;\"> Still 👀 for something?\n",
    "</div>\n",
    "Ok let me show you how you can load your saved model and use it!\n",
    "\n",
    "<pre style=\"background-color: #e0f7fa; border: 1px solid #009688; padding: 10px; border-radius: 5px; font-family: Consolas, 'Courier New', monospace; font-size: 14px;\">\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "model_name = \"model/finetuned_llama3.1\",\n",
    "max_seq_length = 5020,\n",
    "dtype = None,\n",
    "load_in_4bit = True)\n",
    "</pre>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WkgDfYgVawUz"
   },
   "source": [
    "<p style=\"font-family: Comic Sans MS, cursive, sans-serif; color: #333; font-size: 16px;\">\n",
    "    No way, still searching for something? 😄 No worries! You can use the prompt format and code above to get response for mental peace 🧠✨\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Happy to Connect 😊\n",
    "<div style=\"background-color: #f9f9f9; border: 1px solid #ddd; padding: 20px; text-align: center; font-family: Arial, sans-serif;\">\n",
    "    <h3 style=\"margin-bottom: 20px;\">Muhammad Imran Zaman</h3>\n",
    "    <a href=\"https://www.kaggle.com/muhammadimran112233\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://www.kaggle.com/static/images/site-logo.svg\" width=\"100\" height=\"100\" alt=\"Kaggle Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://www.linkedin.com/in/muhammad-imran-zaman\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/LinkedIn_logo_initials.png/600px-LinkedIn_logo_initials.png\" width=\"50\" height=\"50\" alt=\"LinkedIn Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://scholar.google.com/citations?user=ulVFpy8AAAAJ&hl=en\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c7/Google_Scholar_logo.svg/768px-Google_Scholar_logo.svg.png\" width=\"50\" height=\"50\" alt=\"Google Scholar Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://www.youtube.com/@consolioo\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/7/72/YouTube_social_white_square_%282017%29.svg/640px-YouTube_social_white_square_%282017%29.svg.png\" width=\"50\" height=\"50\" alt=\"YouTube Channel\">\n",
    "    </a>\n",
    "    <a href=\"https://github.com/Imran-ml\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png\" width=\"50\" height=\"50\" alt=\"GitHub Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://imranzaman-5202.medium.com/\" style=\"text-decoration: none; margin-right: 20px;\">\n",
    "        <img src=\"data:image/png;base64,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\" width=\"50\" height=\"50\" alt=\"Medium Profile\">\n",
    "    </a>\n",
    "    <a href=\"https://huggingface.co/ImranzamanML\" style=\"text-decoration: none;\">\n",
    "        <img src=\"https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-with-title.svg\" width=\"200\" height=\"100\" alt=\"Hugging Face Profile\">\n",
    "    </a>\n",
    "</div>\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
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 },
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}