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            "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.2.15)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (3.3.2)\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.11/dist-packages (0.45.3)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.3.0)\n",
            "Requirement already satisfied: unsloth_zoo>=2025.2.7 in /usr/local/lib/python3.11/dist-packages (from unsloth) (2025.2.7)\n",
            "Requirement already satisfied: torch>=2.4.0 in /usr/local/lib/python3.11/dist-packages (from unsloth) (2.6.0)\n",
            "Requirement already satisfied: xformers>=0.0.27.post2 in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.0.29.post3)\n",
            "Requirement already satisfied: triton>=3.0.0 in /usr/local/lib/python3.11/dist-packages (from unsloth) (3.2.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from unsloth) (24.2)\n",
            "Requirement already satisfied: tyro in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.9.16)\n",
            "Requirement already satisfied: sentencepiece>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from unsloth) (4.67.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from unsloth) (5.9.5)\n",
            "Requirement already satisfied: wheel>=0.42.0 in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.45.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from unsloth) (1.26.4)\n",
            "Requirement already satisfied: trl!=0.15.0,!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9 in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.15.2)\n",
            "Requirement already satisfied: peft!=0.11.0,>=0.7.1 in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.14.0)\n",
            "Requirement already satisfied: protobuf<4.0.0 in /usr/local/lib/python3.11/dist-packages (from unsloth) (3.20.3)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.28.1)\n",
            "Requirement already satisfied: hf_transfer in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.1.9)\n",
            "Requirement already satisfied: diffusers in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.32.2)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (from unsloth) (0.21.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.0)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.2)\n",
            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (17.0.0)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n",
            "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.10.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.12)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.6)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (25.1.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub->unsloth) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.1.31)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (3.1.5)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (9.1.0.70)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.4.5.8)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (11.2.1.3)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (10.3.5.147)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (11.6.1.9)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.3.1.170)\n",
            "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (0.6.2)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.4.127)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (12.4.127)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=2.4.0->unsloth) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=2.4.0->unsloth) (1.3.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from trl!=0.15.0,!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth) (13.9.4)\n",
            "Requirement already satisfied: cut_cross_entropy in /usr/local/lib/python3.11/dist-packages (from unsloth_zoo>=2025.2.7->unsloth) (25.1.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.11/dist-packages (from unsloth_zoo>=2025.2.7->unsloth) (11.1.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.11/dist-packages (from diffusers->unsloth) (8.6.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
            "Requirement already satisfied: docstring-parser>=0.15 in /usr/local/lib/python3.11/dist-packages (from tyro->unsloth) (0.16)\n",
            "Requirement already satisfied: shtab>=1.5.6 in /usr/local/lib/python3.11/dist-packages (from tyro->unsloth) (1.7.1)\n",
            "Requirement already satisfied: typeguard>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from tyro->unsloth) (4.4.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->trl!=0.15.0,!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->trl!=0.15.0,!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth) (2.18.0)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib-metadata->diffusers->unsloth) (3.21.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=2.4.0->unsloth) (3.0.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->trl!=0.15.0,!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install unsloth transformers datasets bitsandbytes accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade pip\n",
        "!pip install torch==2.5.1+cu118 --index-url https://download.pytorch.org/whl/cu118\n",
        "!pip install torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OX061ARbx-ME",
        "outputId": "066b023f-bcbb-4e93-fe6a-7a9711ab3ce0"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
            "Requirement already satisfied: torch==2.5.1+cu118 in /usr/local/lib/python3.11/dist-packages (2.5.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (3.17.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (3.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (2024.10.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.8.89 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.8.89)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.8.89 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.8.89)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.8.87 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.8.87)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (9.1.0.70)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.11.3.6 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.11.3.6)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.3.0.86 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (10.3.0.86)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.1.48 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.4.1.48)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.5.86 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.7.5.86)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.8.86 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (11.8.86)\n",
            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (3.1.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1+cu118) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch==2.5.1+cu118) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch==2.5.1+cu118) (3.0.2)\n",
            "Looking in indexes: https://download.pytorch.org/whl/cu118\n",
            "Collecting torchvision\n",
            "  Using cached https://download.pytorch.org/whl/cu118/torchvision-0.21.0%2Bcu118-cp311-cp311-linux_x86_64.whl.metadata (6.1 kB)\n",
            "Collecting torchaudio\n",
            "  Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.6.0%2Bcu118-cp311-cp311-linux_x86_64.whl.metadata (6.6 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision) (1.26.4)\n",
            "Collecting torch==2.6.0 (from torchvision)\n",
            "  Using cached https://download.pytorch.org/whl/cu118/torch-2.6.0%2Bcu118-cp311-cp311-linux_x86_64.whl.metadata (27 kB)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision) (11.1.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (3.17.0)\n",
            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (3.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (2024.10.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.8.89 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.8.89)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.8.89 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.8.89)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.8.87 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.8.87)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (9.1.0.70)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.11.3.6 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.11.3.6)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.3.0.86 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (10.3.0.86)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.1.48 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.4.1.48)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.5.86 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.7.5.86)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.8.86 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (11.8.86)\n",
            "Collecting triton==3.2.0 (from torch==2.6.0->torchvision)\n",
            "  Using cached https://download.pytorch.org/whl/triton-3.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.4 kB)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch==2.6.0->torchvision) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch==2.6.0->torchvision) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch==2.6.0->torchvision) (3.0.2)\n",
            "Using cached https://download.pytorch.org/whl/cu118/torchvision-0.21.0%2Bcu118-cp311-cp311-linux_x86_64.whl (6.5 MB)\n",
            "Downloading https://download.pytorch.org/whl/cu118/torch-2.6.0%2Bcu118-cp311-cp311-linux_x86_64.whl (848.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m848.7/848.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/triton-3.2.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (166.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.7/166.7 MB\u001b[0m \u001b[31m47.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu118/torchaudio-2.6.0%2Bcu118-cp311-cp311-linux_x86_64.whl (3.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m74.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: triton, torch, torchvision, torchaudio\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 3.1.0\n",
            "    Uninstalling triton-3.1.0:\n",
            "      Successfully uninstalled triton-3.1.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.5.1+cu118\n",
            "    Uninstalling torch-2.5.1+cu118:\n",
            "      Successfully uninstalled torch-2.5.1+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "fastai 2.7.18 requires torch<2.6,>=1.10, but you have torch 2.6.0+cu118 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-2.6.0+cu118 torchaudio-2.6.0+cu118 torchvision-0.21.0+cu118 triton-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check GPU\n",
        "import torch\n",
        "print(torch.cuda.is_available())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQHG5tpnpDRH",
        "outputId": "7cda0ea3-f46f-4b1c-e0e6-ce3475c3586d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.Model Loading with Unsloth:**"
      ],
      "metadata": {
        "id": "Cui2RQew1N9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from unsloth import FastLanguageModel\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/DeepSeek-R1-Distill-Qwen-14B\",\n",
        "    max_seq_length = 2048,\n",
        "    load_in_4bit = True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476,
          "referenced_widgets": [
            "3c6593eee0f847aeb28b322106a65bbf",
            "d4937d4d721d419bbace59f3e4f48edb",
            "ab0990e9ea7544d8b4477e4f5be666cb",
            "7dce4ec3f0e4440ca153655e28907c9b",
            "f5712b9f8f644abb843e189fb77c9c42",
            "de397480a1f847cabcbb8f4be8d7e11e",
            "50af5f00c30646a2a67589a664fa2add",
            "5e035f8c842042488d2c9ebd2791ce0d",
            "da45f32488d94ef89a31316eec51246b",
            "b5cb50708fb4435ea2adef72121785ba",
            "3f5039c6d8aa44549771d44356a093db",
            "41b4fb679ae54e64bbc2a01a9d199638",
            "c72e72b348624981a159dde86bb6e5c0",
            "13e1a381a65543d198b0839e9cbe1478",
            "e285c8f5fbf94b6f81e7cacfa9eed0c9",
            "bbc86c51c688452e8d7e474631fdcb5d",
            "1428f76863084209b50b8c03d08d4b72",
            "3f1401927f904b6ea4c0fd6e0dc49fc2",
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        },
        "id": "A1RHr96ko0eX",
        "outputId": "d3e50e4c-d8ee-4dbb-9d4a-b42f6ced7862"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "==((====))==  Unsloth 2025.2.15: Fast Qwen2 patching. Transformers: 4.48.3.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.6.0+cu118. CUDA: 7.5. CUDA Toolkit: 11.8. Triton: 3.2.0\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
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              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
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          "data": {
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              "model-00003-of-00003.safetensors:   0%|          | 0.00/4.35G [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "6d3ee295d6824258b43cd54f56b70356"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.LoRA Configuration:**"
      ],
      "metadata": {
        "id": "vETQdLOy0_O3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r=16,  # Adjust the LoRA rank as needed\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],  # Specify key modules\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.05,  # You can adjust dropout to prevent overfitting\n",
        "    bias=\"none\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRjKT-W0091C",
        "outputId": "4d33f8bb-bdfb-4d00-c714-365d1380a25f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = 0.05.\n",
            "Unsloth will patch all other layers, except LoRA matrices, causing a performance hit.\n",
            "Unsloth 2025.2.15 patched 48 layers with 0 QKV layers, 0 O layers and 0 MLP layers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "***Load DataSet***"
      ],
      "metadata": {
        "id": "7yVrjnrD4L-F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"json\", data_files=\"Set.json\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "de98b41ff5344e84a1e6a27229ed75bb",
            "a016fcb464864b51a5e2efa71514df83",
            "9e6b81be8a6345299f311b5681973466",
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            "bab7c47b65c843fa8883e7b0fa9b6783"
          ]
        },
        "id": "4DpWxJHs4Syz",
        "outputId": "7e24574b-f2c6-4840-9ef1-751249b363a7"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de98b41ff5344e84a1e6a27229ed75bb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the text template\n",
        "prompt_template = \"\"\"### Instruction:\n",
        "{instruction}\n",
        "\n",
        "### Response:\n",
        "{response}\"\"\"\n",
        "\n",
        "# Get the EOS token from the tokenizer\n",
        "EOS_TOKEN = tokenizer.eos_token\n",
        "\n",
        "# Data formatting function\n",
        "def formatting_func(examples):\n",
        "    texts = []\n",
        "    for inst, resp in zip(examples[\"instruction\"], examples[\"Response\"]):\n",
        "        text = prompt_template.format(instruction=inst, response=resp) + EOS_TOKEN\n",
        "        texts.append(text)\n",
        "    return {\"text\": texts}\n",
        "\n",
        "# Apply formatting to the dataset\n",
        "dataset = dataset.map(formatting_func, batched=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1bcf5b18384d4231ad7a5ce5333dee57",
            "76618c988d304d259577ac29f8ad26b6",
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            "33c27fdf2fb14acfaea77058dccefca3"
          ]
        },
        "id": "WfyRj5eO4p_x",
        "outputId": "d3443923-42f6-42b9-c9b5-0ab38421a42b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3824 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1bcf5b18384d4231ad7a5ce5333dee57"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Splitting the dataset***"
      ],
      "metadata": {
        "id": "nSbGuw7M8XQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
        "train_dataset = dataset[\"train\"]\n",
        "eval_dataset = dataset[\"test\"]"
      ],
      "metadata": {
        "id": "W9v5_UcB8ffl"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 5 examples from the training dataset\n",
        "for i in range(5):\n",
        "    print(\"Example\", i, \":\\n\", train_dataset[i][\"text\"], \"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_jyQxu0cet5",
        "outputId": "b5d5a07a-c748-481e-b18d-57da8b8cf0e9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example 0 :\n",
            " ### Instruction:\n",
            "Could you describe potential exploitation strategies related to CVE-2023-27555?\n",
            "\n",
            "### Response:\n",
            "Exploitation of CVE-2023-27555 could involve unauthorized individuals executing a sequence of actions to destabilize the IBM Db2 server via its ACR client affinity feature. These individuals might leverage their access to the network to transmit malformed packets or otherwise interfere with the ACR communication protocols, leading to a disruption of service. Such interference could culminate in the cessation of service to valid users or a slowing down of the system's response times. This vulnerability may serve as one component in a wider scheme to hamper company activities or might be tactically employed to soften defenses ahead of a more calculated assault.<｜end▁of▁sentence｜> \n",
            "\n",
            "Example 1 :\n",
            " ### Instruction:\n",
            "What potential consequences exist if a cybercriminal leverages the security weakness identified by the CVE identifier CVE-2023-38268?\n",
            "\n",
            "### Response:\n",
            "If the security flaw CVE-2023-38268 were to be manipulated, it could allow a cybercriminal to carry out nefarious activities that seem to originate from a legitimate user, thereby threatening the integrity and confidentiality of the affected application and its stored information.<｜end▁of▁sentence｜> \n",
            "\n",
            "Example 2 :\n",
            " ### Instruction:\n",
            "In what way does the issue described in CVE-2015-10060 present itself?\n",
            "\n",
            "### Response:\n",
            "The issue recognized in CVE-2015-10060 is displayed through the alteration of the id1/id2 parameter within unspecified operations in the Data/views.py file, which creates a vulnerability for an SQL injection.<｜end▁of▁sentence｜> \n",
            "\n",
            "Example 3 :\n",
            " ### Instruction:\n",
            "Does the exploitation of CVE-2023-20992 necessitate the involvement of a user?\n",
            "\n",
            "### Response:\n",
            "Exploiting CVE-2023-20992 does not necessitate the participation of the user, as the security flaw can be leveraged with zero user interaction.<｜end▁of▁sentence｜> \n",
            "\n",
            "Example 4 :\n",
            " ### Instruction:\n",
            "In what way would a BOLA vulnerability present itself within a car company's mobile application programming interface?\n",
            "\n",
            "### Response:\n",
            "In the given context, the car company's mobile API is designed to enable remote control of vehicles through the API, necessitating a Vehicle Identification Number (VIN) for every request. The BOLA (Broken Object Level Authorization) issue emerges if the API fails to verify if the VIN in a user's request actually matches a vehicle registered to that user's account. By submitting requests with various VINs, a malicious actor could potentially gain control over vehicles unassociated with their account, thus exploiting the BOLA weakness.<｜end▁of▁sentence｜> \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**4. Configure Training Arguments**"
      ],
      "metadata": {
        "id": "X-0UZPJF10bB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_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=True,\n",
        "    logging_steps=10,\n",
        "    output_dir=\"outputs\",\n",
        "    optim=\"adamw_8bit\",\n",
        "    eval_strategy=\"steps\",  # Use eval_strategy instead of evaluation_strategy\n",
        "    eval_steps=20,\n",
        ")"
      ],
      "metadata": {
        "id": "NuEGtuls8xWq"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=2048,\n",
        "    args=training_args,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
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        "outputId": "0b6eef60-8127-4919-f66a-5df8e289b0b3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Converting train dataset to ChatML (num_proc=2):   0%|          | 0/3441 [00:00<?, ? examples/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Applying chat template to train dataset (num_proc=2):   0%|          | 0/3441 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
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            "text/plain": [
              "Tokenizing train dataset (num_proc=2):   0%|          | 0/3441 [00:00<?, ? examples/s]"
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          "data": {
            "text/plain": [
              "Truncating train dataset (num_proc=2):   0%|          | 0/3441 [00:00<?, ? examples/s]"
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          "data": {
            "text/plain": [
              "Converting eval dataset to ChatML (num_proc=2):   0%|          | 0/383 [00:00<?, ? examples/s]"
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          "data": {
            "text/plain": [
              "Applying chat template to eval dataset (num_proc=2):   0%|          | 0/383 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "f2834328db794d7aad383062631e4a2e"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing eval dataset (num_proc=2):   0%|          | 0/383 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a74422d64bb4d92bf1f4a15a3746dee"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Truncating eval dataset (num_proc=2):   0%|          | 0/383 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "1e9729cfa55147978f08a5c702c8896f"
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    {
      "cell_type": "markdown",
      "source": [
        "**5. Start Train**"
      ],
      "metadata": {
        "id": "t5tfC6ir-9Bc"
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    {
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      "source": [
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          "height": 686
        },
        "id": "-fd0hID5_B9l",
        "outputId": "de805f78-aeca-479d-d7d9-ea871c2dfdc3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 3,441 | 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 = 25,165,824\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
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              "            })\n",
              "        });\n",
              "    "
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          "metadata": {}
        },
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          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mpeteradel\u001b[0m (\u001b[33mpeteradel-h\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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              "Tracking run with wandb version 0.19.6"
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              "Run data is saved locally in <code>/content/wandb/run-20250226_065049-ou14jgdw</code>"
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          "data": {
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              "<IPython.core.display.HTML object>"
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              "Syncing run <strong><a href='https://wandb.ai/peteradel-h/huggingface/runs/ou14jgdw' target=\"_blank\">outputs</a></strong> to <a href='https://wandb.ai/peteradel-h/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/peteradel-h/huggingface' target=\"_blank\">https://wandb.ai/peteradel-h/huggingface</a>"
            ]
          },
          "metadata": {}
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          "data": {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/peteradel-h/huggingface/runs/ou14jgdw' target=\"_blank\">https://wandb.ai/peteradel-h/huggingface/runs/ou14jgdw</a>"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 25:14, 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",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.269200</td>\n",
              "      <td>2.034655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.980600</td>\n",
              "      <td>1.829682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.882600</td>\n",
              "      <td>1.667268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.716800</td>\n",
              "      <td>1.640834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.649600</td>\n",
              "      <td>1.632152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: Not an error, but Qwen2ForCausalLM does not accept `num_items_in_batch`.\n",
            "Using gradient accumulation will be very slightly less accurate.\n",
            "Read more on gradient accumulation issues here: https://unsloth.ai/blog/gradient\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=1.9439043045043944, metrics={'train_runtime': 2148.1051, 'train_samples_per_second': 0.372, 'train_steps_per_second': 0.047, 'total_flos': 8054068769931264.0, 'train_loss': 1.9439043045043944})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "pEBU0v5tHjr_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "**6. Model Evaluation:**"
      ],
      "metadata": {
        "id": "IC3brkwJIppB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "iFwL6QbrIol6",
        "outputId": "6cfa4240-8911-4c13-b1d3-f2781830a728"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='192' max='192' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [192/192 02:20]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 1.6321518421173096,\n",
              " 'eval_runtime': 140.8926,\n",
              " 'eval_samples_per_second': 2.718,\n",
              " 'eval_steps_per_second': 1.363}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Save form and tokenizer:**"
      ],
      "metadata": {
        "id": "PcYX9FaUI2Mb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"CyberBrain_model\")\n",
        "tokenizer.save_pretrained(\"CyberBrain_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5PCyZ24I5HM",
        "outputId": "c859d3a3-e7d4-4609-934b-5ae86615a06f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('CyberBrain_model/tokenizer_config.json',\n",
              " 'CyberBrain_model/special_tokens_map.json',\n",
              " 'CyberBrain_model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "***Upload the model to Hugging Face Hub :***"
      ],
      "metadata": {
        "id": "sYy-y0wWJKlm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KERQkqyKMvod",
        "outputId": "98a67e54-d513-498f-fee6-c69fc85a27c0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.28.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (3.17.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2024.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.67.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2025.1.31)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Login***"
      ],
      "metadata": {
        "id": "BuerbRugR1KC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_dEWHoV8M6Vb",
        "outputId": "94f7a077-e247-4e77-91a7-c96448d46af9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "The token `Colab` has been saved to /root/.cache/huggingface/stored_tokens\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful.\n",
            "The current active token is: `Colab`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***publish***"
      ],
      "metadata": {
        "id": "7OV-hjIrRtvS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"PeterAdel/CyberBrain_R1\")\n",
        "tokenizer.push_to_hub(\"PeterAdel/CyberBrain_R1\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
          "referenced_widgets": [
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        "id": "RFJCRZiTJOfW",
        "outputId": "0a86d1c1-076b-4c0e-89e4-c5a5b5449481"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/628 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00fdeda1880747f4a6dcc1d3c9113181"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "956afb7d82a747daa2014dd1e3c856a2"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/50.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to https://huggingface.co/PeterAdel/CyberBrain_R1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2b9ada03a87427fbe0eec3359ad3289"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "**Test Prompt After Train**"
      ],
      "metadata": {
        "id": "MRgnENOISZ1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a test script using the template\n",
        "prompt = \"\"\"### Instruction:\n",
        "ما هي العواقب المحتملة لاستغلال ثغرة CVE-2023-29351؟\n",
        "\n",
        "### Response:\"\"\"\n",
        "\n",
        "# Convert text to token format and move input to GPU\n",
        "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
        "\n",
        "# Generate answer with specified number of new tokens\n",
        "outputs_prompt = model.generate(input_ids, max_new_tokens=200)\n",
        "\n",
        "# Decode the generated tokens and print the answer\n",
        "print(tokenizer.decode(outputs_prompt[0], skip_special_tokens=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ej0CIHJ9Sgr4",
        "outputId": "54d1e753-606c-4164-def5-07ceb2b117c6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### Instruction:\n",
            "ما هي العواقب المحتملة لاستغلال ثغرة CVE-2023-29351؟\n",
            "\n",
            "### Response: CVE-2023-29351 يُمكن أن يستخدم لشن هجمات عبر بريد إلكتروني مزور، حيث يمكن للهاكر استغلال هذه الثغرة لخداع الضحية في إجراء تغييرات في إعدادات حسابه على ويب سكواير، مما يشمل إنشاء أو تعديل أو حذف حسابات أو تغيير إعدادات الدفع أو تغيير معلومات الاتصال.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a test script using the template\n",
        "prompt = \"\"\"### Instruction:\n",
        "كيف تنفذ لاستغلال ثغرة CVE-2023-29351؟\n",
        "\n",
        "### Response:\"\"\"\n",
        "\n",
        "# Convert text to token format and move input to GPU\n",
        "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
        "\n",
        "# Generate answer with specified number of new tokens\n",
        "outputs_prompt = model.generate(input_ids, max_new_tokens=200)\n",
        "\n",
        "# Decode the generated tokens and print the answer\n",
        "print(tokenizer.decode(outputs_prompt[0], skip_special_tokens=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQkvrMt4T7B5",
        "outputId": "d2f775a6-0a91-48a8-f476-ef7ffc45c9e5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### Instruction:\n",
            "كيف تنفذ لاستغلال ثغرة CVE-2023-29351؟ \n",
            "\n",
            "### Response: \n",
            "لتنفيذ استغلال لثغرة CVE-2023-29351، يُنصح لقراص النت بانشاء مسح للكمبيوتر المستخدم عن طريق استغلال الثغرة. يمكن للقراص النت استغلال هذه الثغرة لتنفيذ مسح محدد للكمبيوتر المستخدم، مما ينطوي على تهديدات جسيمة للنظام المستخدم.\n"
          ]
        }
      ]
    }
  ]
}