diff --git "a/TESTER.ipynb" "b/TESTER.ipynb"
new file mode 100644--- /dev/null
+++ "b/TESTER.ipynb"
@@ -0,0 +1,4085 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rqX9hLfEziis"
+      },
+      "source": [
+        "Further documentation for the libraries used can be found at https://unsloth.ai/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9eJjsg0_D2Dj"
+      },
+      "source": [
+        "For information on hugging face tokens go to https://huggingface.co/docs/hub/en/security-tokens"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pdymVP95XA4_",
+        "outputId": "d4095a9a-00b7-45a7-d268-055a0536e9a3"
+      },
+      "outputs": [],
+      "source": [
+        "# Add your hugging face token to secret keys or store your huggingface token as an environment variable.\n",
+        "# It is used to download or upload models to your account.\n",
+        "from google.colab import userdata\n",
+        "from huggingface_hub import login\n",
+        "login(userdata.get('TOKEN'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "cBEG5Pq1lG-G"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
+        "!pip install huggingface_hub\n",
+        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
+        "\n",
+        "# We have to check which Torch version for Xformers (2.3 -> 0.0.27)\n",
+        "from torch import __version__; from packaging.version import Version as V\n",
+        "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n",
+        "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aRz4XCANNy8b",
+        "outputId": "198de2aa-0c91-4960-8ebc-a0a9b773a89a"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth import FastLanguageModel\n",
+        "import torch\n",
+        "\n",
+        "max_seq_length = 2048\n",
+        "\n",
+        "def load_model(model_name, max_seq_length=max_seq_length, dtype=None, load_in_4bit=True):\n",
+        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
+        "        model_name=model_name,\n",
+        "        max_seq_length=max_seq_length,\n",
+        "        dtype=dtype,\n",
+        "        load_in_4bit=load_in_4bit,\n",
+        "    )\n",
+        "    return model, tokenizer"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2bW-xOYMw7iC"
+      },
+      "source": [
+        "#Load the base model and attach the trained LoRa adapters."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "urDIg3QRlCMx",
+        "outputId": "435a2e48-0da4-47d6-cd27-78ee7d6ee4fa"
+      },
+      "outputs": [],
+      "source": [
+        "from peft import PeftModel\n",
+        "\n",
+        "models = [\n",
+        "    'Phi-3.5-mini-instruct-bnb-4bit', # |Min Training Gpu : T4, Min Testing GPU: T4, Max Model size : 14.748 GB|\n",
+        "    'gemma-2-27b-it-bnb-4bit',  # |Min Training Gpu: A100, Min Testing GPU: T4, Max Model size: 39.564GB|\n",
+        "    'Meta-Llama-3.1-8B-Instruct-bnb-4bit' # |Min Training Gpu: T4, Min Testing GPU: T4, Max Model size : 22.168GB|\n",
+        "]\n",
+        "\n",
+        "chat_templates = [\n",
+        "    'phi-3',\n",
+        "    'alpaca', #Or gemma.\n",
+        "    'llama-3.1'\n",
+        "]\n",
+        "\n",
+        "# Select model and corresponding template.\n",
+        "selection = 0\n",
+        "model_name = models[selection]\n",
+        "chat_template = chat_templates[selection]\n",
+        "HfUsername = \"CooperW\"\n",
+        "\n",
+        "try:\n",
+        "  # This is the name of your model repository on hugging face.\n",
+        "  LoRa_Adapters = f\"{HfUsername}/{model_name.replace('-', '_').replace('.', '_')}_128Batch\"\n",
+        "except:\n",
+        "  print('No LoRa Adapters Found')\n",
+        "  exit()\n",
+        "\n",
+        "# Load base model and fit Adapters.\n",
+        "model = f\"unsloth/{model_name}\"\n",
+        "model, tokenizer = load_model(model)\n",
+        "model = PeftModel.from_pretrained(model, LoRa_Adapters)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dcill7J8v7mV"
+      },
+      "source": [
+        "#Data Mapping\n",
+        "This function will take the given dataset and map the contents to the [\"text\"] field, this format is what is needed to be input to the model.\n",
+        "\n",
+        "Example:\n",
+        "```\n",
+        "<|user|>\n",
+        "Hi!<|end|>\n",
+        "<|assistant|>\n",
+        "Hello! How are you?<|end|>\n",
+        "<|user|>\n",
+        "I'm doing great! And you?<|end|>\n",
+        "\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "QVEYv-pnlh9s"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth.chat_templates import get_chat_template\n",
+        "\n",
+        "tokenizer = get_chat_template(\n",
+        "    tokenizer,\n",
+        "    chat_template = chat_template,\n",
+        "    mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"},\n",
+        ")\n",
+        "\n",
+        "def formatting_prompts_func(examples):\n",
+        "    convos = examples[\"conversations\"]\n",
+        "    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
+        "    return { \"text\" : texts, }\n",
+        "pass"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "iVqRqJrqlpvD",
+        "outputId": "d8151463-39c0-4c1f-a951-f42aad7d9265"
+      },
+      "outputs": [],
+      "source": [
+        "# This will load datasets from your hugging face repository, alternatively load locally stored datasets.\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset_repo = f\"{HfUsername}/jadidi\"\n",
+        "dataset_name = \"test_network.jsonl\"\n",
+        "\n",
+        "test_dataset = load_dataset(dataset_repo, data_files=dataset_name, split='train')\n",
+        "test_dataset = test_dataset.map(formatting_prompts_func, batched = True,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hTj1u2o6vSSn"
+      },
+      "source": [
+        "#Evaluation Loop\n",
+        "This function will send an input message to the model, the input will be a row from the testing dataset then recieve an output, complete the output if necessary, and provide a live accuracy update."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "6N1GoeHFY_cC"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "import sys\n",
+        "\n",
+        "def evaluate_model_on_dataset(model, tokenizer, test_dataset, num_samples=len(test_dataset)):\n",
+        "\n",
+        "    # Enable native 2x faster inference\n",
+        "    FastLanguageModel.for_inference(model)\n",
+        "\n",
+        "    # List to store predictions\n",
+        "    predictions = []\n",
+        "    ground_truths = []\n",
+        "    start_time = time.time()\n",
+        "\n",
+        "    # Metrics\n",
+        "    correct = 0\n",
+        "    incorrect = 0\n",
+        "    accuracy = 0\n",
+        "\n",
+        "    prompt = \"\"\n",
+        "\n",
+        "    # Function to map incomplete answers to full words (e.g., attac or norm)\n",
+        "    def complete_prediction(pred):\n",
+        "        # Dictionary of known classes in the ToN-IoT dataset with their first letters (or first two for dos and ddos)\n",
+        "        known_classes = {\n",
+        "            \"n\": \"normal\",            # Normal traffic\n",
+        "            \"m\": \"mitm\",              # Man in the Middle attack\n",
+        "            \"p\": \"password\",          # Password attacks (brute force, etc.)\n",
+        "            \"b\": \"backdoor\",          # Backdoor attacks\n",
+        "            \"i\": \"injection\",         # SQL injection, Command injection, etc.\n",
+        "            \"x\": \"xss\",               # Cross-Site Scripting (XSS) attacks\n",
+        "            \"r\": \"ransomware\",        # Ransomware\n",
+        "            \"s\": \"scanning\",          # Port scanning, vulnerability scanning\n",
+        "            \"a\": \"attack\"             # For Binary Classification\n",
+        "        }\n",
+        "\n",
+        "        pred_lower = pred.lower()  # Convert prediction to lowercase\n",
+        "\n",
+        "        # For \"dos\" and \"ddos\", use the first two letters; for others, use the first letter\n",
+        "        if pred_lower.startswith(\"dd\"):\n",
+        "            return \"ddos\"\n",
+        "        elif pred_lower.startswith(\"do\"):\n",
+        "            return \"dos\"\n",
+        "        else:\n",
+        "            # Use the first letter for other classes\n",
+        "            return known_classes.get(pred_lower[0], pred)  # Return original prediction if no match is found\n",
+        "\n",
+        "    # Loop over each item in the test dataset\n",
+        "    for item in test_dataset.select(range(num_samples)):\n",
+        "\n",
+        "        # Extract the human input from the item\n",
+        "        human_input = item['conversations'][0]['value']\n",
+        "\n",
+        "        # Prepare messages with the current input\n",
+        "        message = [{\"from\": \"human\", \"value\": f\"{prompt}{human_input}\"}]\n",
+        "\n",
+        "        # Tokenize the input\n",
+        "        inputs = tokenizer.apply_chat_template(\n",
+        "            message,\n",
+        "            tokenize=True,\n",
+        "            add_generation_prompt=True,  # Must add for generation\n",
+        "            return_tensors=\"pt\",\n",
+        "        ).to(\"cuda\")\n",
+        "\n",
+        "        # Generate the output\n",
+        "        outputs = model.generate(input_ids=inputs, max_new_tokens=1, use_cache=True)\n",
+        "        answer = tokenizer.batch_decode(outputs)\n",
+        "\n",
+        "        # Process the prediction\n",
+        "        pred = complete_prediction(answer[0].split()[-1])\n",
+        "        truth = item['conversations'][1]['value'].strip()\n",
+        "\n",
+        "        # Append predictions and truths\n",
+        "        predictions.append(pred)\n",
+        "        ground_truths.append(truth)\n",
+        "\n",
+        "        # Update metrics\n",
+        "        if pred == truth:\n",
+        "            correct += 1\n",
+        "            color_code = \"\\033[32m\"  # Green\n",
+        "        else:\n",
+        "            incorrect += 1\n",
+        "            color_code = \"\\033[31m\"  # Red\n",
+        "\n",
+        "        # Calculate accuracy\n",
+        "        elapsed_time = time.time() - start_time\n",
+        "        accuracy = (correct / (correct + incorrect)) * 100\n",
+        "\n",
+        "        # Print current status\n",
+        "        sys.stdout.write(f\"\\rElapsed Time: {elapsed_time:.2f} seconds, Accuracy: {accuracy:.2f}%, Completed: {correct + incorrect}/{num_samples} Prediction: {color_code}{pred}\\033[0m, Ground Truth: {color_code}{truth}\\033[0m\")\n",
+        "        sys.stdout.flush()\n",
+        "\n",
+        "    print(\"\\n\\n\\nInference Complete\\n\\n\\n\")\n",
+        "\n",
+        "    return predictions, ground_truths"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EtpHf7bTvH_h"
+      },
+      "source": [
+        "#For Binary Classification Metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "mVmdz7LNJ1MO"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "def metrics(predictions, ground_truths):\n",
+        "    # Convert to numpy arrays\n",
+        "    y_true = np.array(ground_truths)\n",
+        "    y_pred = np.array(predictions)\n",
+        "\n",
+        "    # Convert string labels to numeric values\n",
+        "    label_mapping = {'attack': 1, 'normal': 0}\n",
+        "    y_true_numeric = np.array([label_mapping[label] for label in y_true])\n",
+        "    y_pred_numeric = np.array([label_mapping[label] for label in y_pred])\n",
+        "\n",
+        "    accuracy = accuracy_score(y_true, y_pred)*100\n",
+        "    print(f\"Accuracy: {accuracy:.2f}%\\n\")\n",
+        "\n",
+        "    # Print classification report\n",
+        "    class_report = classification_report(y_true, y_pred, labels=['attack', 'normal'])\n",
+        "    print(\"Classification Report:\")\n",
+        "    print(class_report, '\\n')\n",
+        "\n",
+        "    # Compute metrics\n",
+        "    precision = precision_score(y_true, y_pred, average=None, labels=['attack', 'normal'])\n",
+        "    recall = recall_score(y_true, y_pred, average=None, labels=['attack', 'normal'])\n",
+        "    f1 = f1_score(y_true, y_pred, average=None, labels=['attack', 'normal'])\n",
+        "\n",
+        "    metrics_df = pd.DataFrame({\n",
+        "        'Class': ['attack', 'normal'],\n",
+        "        'Precision': precision,\n",
+        "        'Recall': recall,\n",
+        "        'F1 Score': f1\n",
+        "    })\n",
+        "\n",
+        "    # Create subplots\n",
+        "    fig, axes = plt.subplots(1, 2, figsize=(18, 6))  # Two plots side by side\n",
+        "\n",
+        "    # Plot precision, recall, and F1 score\n",
+        "    metrics_df.plot(x='Class', kind='bar', ax=axes[0])\n",
+        "    axes[0].set_title('Precision, Recall, and F1 Score by Class')\n",
+        "    axes[0].set_xlabel('Class')\n",
+        "    axes[0].set_ylabel('Score')\n",
+        "    axes[0].set_ylim(0, 1)\n",
+        "\n",
+        "    # Plot confusion matrix\n",
+        "    cm = confusion_matrix(y_true, y_pred, labels=['attack', 'normal'])\n",
+        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['attack', 'normal'], yticklabels=['attack', 'normal'], ax=axes[1])\n",
+        "    axes[1].set_xlabel('Predicted')\n",
+        "    axes[1].set_ylabel('True')\n",
+        "    axes[1].set_title('Confusion Matrix')\n",
+        "\n",
+        "    # Adjust layout\n",
+        "    plt.subplots_adjust(wspace=0.3)\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "thGuQIEPYJog",
+        "outputId": "c23b6086-9adf-4948-b86b-c716a4888d9b"
+      },
+      "outputs": [],
+      "source": [
+        "# Call the evaluation function\n",
+        "pred, truth = evaluate_model_on_dataset(model, tokenizer, test_dataset, num_samples=1000)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IGfDpQJCwQFA"
+      },
+      "outputs": [],
+      "source": [
+        "metrics(pred, truth)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2vagHjHGvEK8"
+      },
+      "source": [
+        "#For Multi-Class Classification Metrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j1M3mCf8fogx"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3w4hCfkuC6G3"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "def metrics(predictions, ground_truths):\n",
+        "    # Convert to numpy arrays\n",
+        "    y_true = np.array(ground_truths)\n",
+        "    y_pred = np.array(predictions)\n",
+        "\n",
+        "    # List of all known ToN-IoT classes\n",
+        "    class_labels = [\"normal\", \"ddos\", \"dos\", \"mitm\", \"password\", \"backdoor\", \"injection\", \"xss\", \"ransomware\", \"scanning\"]\n",
+        "\n",
+        "    # Compute and print accuracy\n",
+        "    accuracy = accuracy_score(y_true, y_pred) * 100\n",
+        "    print(f\"Accuracy: {accuracy:.2f}%\\n\")\n",
+        "\n",
+        "    # Print classification report\n",
+        "    class_report = classification_report(y_true, y_pred, labels=class_labels, zero_division=0)\n",
+        "    print(\"Classification Report:\")\n",
+        "    print(class_report, '\\n')\n",
+        "\n",
+        "    # Compute precision, recall, and F1 score for each class\n",
+        "    precision = precision_score(y_true, y_pred, average=None, labels=class_labels, zero_division=0)\n",
+        "    recall = recall_score(y_true, y_pred, average=None, labels=class_labels, zero_division=0)\n",
+        "    f1 = f1_score(y_true, y_pred, average=None, labels=class_labels, zero_division=0)\n",
+        "\n",
+        "    metrics_df = pd.DataFrame({\n",
+        "        'Class': class_labels,\n",
+        "        'Precision': precision,\n",
+        "        'Recall': recall,\n",
+        "        'F1 Score': f1\n",
+        "    })\n",
+        "\n",
+        "    # Create subplots\n",
+        "    fig, axes = plt.subplots(1, 2, figsize=(18, 6))  # Two plots side by side\n",
+        "\n",
+        "    # Plot precision, recall, and F1 score\n",
+        "    metrics_df.plot(x='Class', kind='bar', ax=axes[0], color=['blue', 'orange', 'green'])\n",
+        "    axes[0].set_title('Precision, Recall, and F1 Score by Class')\n",
+        "    axes[0].set_xlabel('Class')\n",
+        "    axes[0].set_ylabel('Score')\n",
+        "    axes[0].set_ylim(0, 1)\n",
+        "    axes[0].legend([\"Precision\", \"Recall\", \"F1 Score\"], loc='upper left')\n",
+        "\n",
+        "    # Plot confusion matrix\n",
+        "    cm = confusion_matrix(y_true, y_pred, labels=class_labels)\n",
+        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels, ax=axes[1])\n",
+        "    axes[1].set_xlabel('Predicted')\n",
+        "    axes[1].set_ylabel('True')\n",
+        "    axes[1].set_title('Confusion Matrix')\n",
+        "\n",
+        "    # Adjust layout\n",
+        "    plt.subplots_adjust(wspace=0.3)\n",
+        "    plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jqJ56550huU2"
+      },
+      "outputs": [],
+      "source": [
+        "metrics(pred, truth)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hbI9xSd65zdg"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import runtime\n",
+        "# runtime.unassign()"
+      ]
+    }
+  ],
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+    "accelerator": "GPU",
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