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            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
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            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
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            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
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            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
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            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
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            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
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            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m91.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.6.0)\n",
            "Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.11/dist-packages (from accelerate) (2.0.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from accelerate) (24.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from accelerate) (6.0.2)\n",
            "Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from accelerate) (2.6.0+cu124)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.21.0->accelerate) (3.18.0)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.21.0->accelerate) (4.67.1)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (3.1.6)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (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>=2.0.0->accelerate) (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>=2.0.0->accelerate) (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>=2.0.0->accelerate) (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>=2.0.0->accelerate) (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>=2.0.0->accelerate) (12.3.1.170)\n",
            "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (0.6.2)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (3.2.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (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>=2.0.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=2.0.0->accelerate) (3.0.2)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.10)\n",
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            "Collecting bitsandBytes\n",
            "  Downloading bitsandbytes-0.45.5-py3-none-manylinux_2_24_x86_64.whl.metadata (5.0 kB)\n",
            "Requirement already satisfied: torch<3,>=2.0 in /usr/local/lib/python3.11/dist-packages (from bitsandBytes) (2.6.0+cu124)\n",
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            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (4.13.2)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (2025.3.2)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (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<3,>=2.0->bitsandBytes) (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<3,>=2.0->bitsandBytes) (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<3,>=2.0->bitsandBytes) (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<3,>=2.0->bitsandBytes) (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<3,>=2.0->bitsandBytes) (12.3.1.170)\n",
            "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (0.6.2)\n",
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            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch<3,>=2.0->bitsandBytes) (12.4.127)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch<3,>=2.0->bitsandBytes) (3.0.2)\n",
            "Downloading bitsandbytes-0.45.5-py3-none-manylinux_2_24_x86_64.whl (76.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.1/76.1 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandBytes\n",
            "Successfully installed bitsandBytes-0.45.5\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.51.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2025.3.2\n",
            "    Uninstalling fsspec-2025.3.2:\n",
            "      Successfully uninstalled fsspec-2025.3.2\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",
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            "\u001b[0mSuccessfully installed datasets-3.5.1 dill-0.3.8 fsspec-2025.3.0 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install peft\n",
        "!pip install accelerate\n",
        "!pip install bitsandBytes\n",
        "!pip install transformers\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install GPUtil"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v16t51CO_NR6",
        "outputId": "6f6ffbaa-ca0a-4a0e-cdd9-5d07cbb00a54",
        "collapsed": true
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting GPUtil\n",
            "  Downloading GPUtil-1.4.0.tar.gz (5.5 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: GPUtil\n",
            "  Building wheel for GPUtil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for GPUtil: filename=GPUtil-1.4.0-py3-none-any.whl size=7392 sha256=287a1fae505fac85db9c805d82ca2b5935c7471b291f5d6458668c8d605c555d\n",
            "  Stored in directory: /root/.cache/pip/wheels/2b/4d/8f/55fb4f7b9b591891e8d3f72977c4ec6c7763b39c19f0861595\n",
            "Successfully built GPUtil\n",
            "Installing collected packages: GPUtil\n",
            "Successfully installed GPUtil-1.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import GPUtil\n",
        "import os\n",
        "\n",
        "GPUtil.showUtilization()\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    print(\"GPU is available\")\n",
        "else:\n",
        "    print(\"GPU is not available, using CPU instead\")\n",
        "\n",
        "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZv4rCgtAFuQ",
        "outputId": "7646404e-2160-49a6-f585-f259608ad913"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "| ID | GPU | MEM |\n",
            "------------------\n",
            "|  0 |  0% |  0% |\n",
            "GPU is available\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, LlamaTokenizer\n",
        "from huggingface_hub import notebook_login\n",
        "from datasets import load_dataset\n",
        "from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model\n",
        "\n",
        "if \"COLAB_GPU\" in os.environ:\n",
        "  from google.colab import output\n",
        "  output.enable_custom_widget_manager()"
      ],
      "metadata": {
        "id": "Ub5Kq-xbAyqo"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if \"COLAB_GPU\" in os.environ:\n",
        "  !huggingface-cli login\n",
        "else:\n",
        "  notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84nZqOxhBb8C",
        "outputId": "516101b0-1b9f-49fe-c557-b635d7f34234"
      },
      "execution_count": 5,
      "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) n\n",
            "Token is valid (permission: fineGrained).\n",
            "The token `Ftune` has been saved to /root/.cache/huggingface/stored_tokens\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful.\n",
            "The current active token is: `Ftune`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_id = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
          "referenced_widgets": [
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        },
        "id": "AytCd6RuBj5u",
        "outputId": "c1769355-6f36-490f-feab-cff630aa7f33"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              }
            }
          }
        },
        {
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              "colab": {
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                }
              }
            }
          }
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17c826b3c9c94e4a86aeff69d5d34b15"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81b871994d954bc38080754f96c5cf88"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d27ecb28b17a47e58b7243669436de12"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95e50ca4363b472ea49972c9f828d245"
            }
          },
          "metadata": {
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            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "752a5a0d41d94f02a22b601243e1763a"
            }
          },
          "metadata": {
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              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJ6P1bqa9e3s",
        "outputId": "b693a364-6b01-4967-e727-70ae44e03529"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = load_dataset(\"text\", data_files={\"train\":\n",
        "                                                 [\"/content/drive/MyDrive/converted_finetune_rubric_data.txt\"]}, split=\"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "e8db44b8edf14855884e7c7ab4ac9344",
            "869d1cdb36f34cfc8beb13b8361a3bd1",
            "7bbf53798010444eb292e5d4ffdd3f44",
            "18b4820afe044889a66074406d3b6095",
            "ddaa147e34a84240a2ce44506fb83c5d",
            "1062011514344f1799f189a3956f496c",
            "8f614db7841f47448c8c03a4c075f437",
            "46b2388eca734f8faccdb7c90ca08b11",
            "ee37dd027d1c440883e0b27e87655451",
            "fbc9ab3b565142db9e8b27d7f4a9c467",
            "a37fa931de8643a792c4766978f1a129"
          ]
        },
        "id": "cNS9dNEhDPE3",
        "outputId": "6bee3f6b-8e47-418c-ea7b-18a93014c222"
      },
      "execution_count": 9,
      "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": "e8db44b8edf14855884e7c7ab4ac9344"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[\"text\"][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "cDZFH7XOD2JA",
        "outputId": "63b0c189-8b71-474a-ccd8-36cf93bb118c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'You are given a question, a sample answer, and the maximum marks for the question.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = LlamaTokenizer.from_pretrained(base_model_id, use_fast=False, trust_remote_code=True, add_eos_token=True)\n",
        "\n",
        "if tokenizer.pad_token is None:\n",
        "  tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})"
      ],
      "metadata": {
        "id": "W_c3rMnbEfYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "c31d3a1eb95440c59d58ee9cd70165e2",
            "e9370947e8934e2db6d78ba84ef83424",
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            "bf035f8a37234ed0b77f6817c7a5b1f1",
            "3a3d6878c308499da97aad1f53895296",
            "de21c31e8cc242a58f56b5fbcb6b4738",
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            "60b70d767d2c45aea65dee8c02543aed",
            "561199d725584ae18aa662c12db45724"
          ]
        },
        "outputId": "ac46c75c-cba7-4829-fadd-31c2fdbe6ee7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c31d3a1eb95440c59d58ee9cd70165e2"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c583f897ee14998875d5797c6196399"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd3e1baa044b4655bab06006a2b2ec85"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "561199d725584ae18aa662c12db45724"
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          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train_dataset = []\n",
        "for phrase in train_dataset:\n",
        "  tokenized_train_dataset.append(tokenizer(phrase[\"text\"]))"
      ],
      "metadata": {
        "id": "T941FWBIG8DB"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train_dataset[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqXip5GPHWQ7",
        "outputId": "4fd4fa5b-8c79-4ff6-fa80-eab96bb06cb1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [1, 887, 526, 2183, 263, 1139, 29892, 263, 4559, 1234, 29892, 322, 278, 7472, 17997, 363, 278, 1139, 29889, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train_dataset[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFJzKBcVHX4N",
        "outputId": "5cc273d0-93f8-43d0-89a2-2152d39420d3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [1, 13355, 1598, 278, 4100, 3291, 297, 278, 1234, 322, 5706, 263, 26654, 14051, 2200, 29889, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.eos_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "2Y1SDgStHpOi",
        "outputId": "68e0d5b9-07f1-41fb-8a81-68b306b54059"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'</s>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=64,\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    bias=\"none\",\n",
        "    lora_dropout=0.05,\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)"
      ],
      "metadata": {
        "id": "fmW4KgZlHucb"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        output_dir=\"./finetunedModel\",\n",
        "        per_device_train_batch_size=2,\n",
        "        gradient_accumulation_steps=2,\n",
        "        num_train_epochs=3,\n",
        "        learning_rate=1e-4,\n",
        "        max_steps=20,\n",
        "        bf16=False,\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        logging_dir=\"./log\",\n",
        "        save_strategy=\"epoch\",\n",
        "        save_steps=50,\n",
        "        logging_steps=10\n",
        "\n",
        "),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "model.config.use_cache=False\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "02gX--VxILA3",
        "outputId": "e3cbe96c-5a3f-44ba-ea47-bc52edf1510f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No label_names provided for model class `PeftModelForCausalLM`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\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",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "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?ref=models\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: No netrc file found, creating one.\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[33mmriganksingh005\u001b[0m (\u001b[33mmriganksingh005-upes\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.19.10"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250502_104400-kso1egno</code>"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/mriganksingh005-upes/huggingface/runs/kso1egno' target=\"_blank\">./finetunedModel</a></strong> to <a href='https://wandb.ai/mriganksingh005-upes/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/mriganksingh005-upes/huggingface' target=\"_blank\">https://wandb.ai/mriganksingh005-upes/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/mriganksingh005-upes/huggingface/runs/kso1egno' target=\"_blank\">https://wandb.ai/mriganksingh005-upes/huggingface/runs/kso1egno</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/torch/_dynamo/eval_frame.py:745: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [20/20 01:55, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>3.428900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.547200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=20, training_loss=2.988047218322754, metrics={'train_runtime': 133.5822, 'train_samples_per_second': 0.599, 'train_steps_per_second': 0.15, 'total_flos': 211703496474624.0, 'train_loss': 2.988047218322754, 'epoch': 0.27586206896551724})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from transformers import BitsAndBytesConfig, LlamaTokenizer\n",
        "from peft import PeftModel\n",
        "\n",
        "base_model_id = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "\n",
        "nf4Config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(base_model_id, use_fast=False, trust_remote_code=True, add_eos_token=True)\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model_id,\n",
        "    quantization_config=nf4Config,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        "    use_auth_token=True\n",
        "  )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "0fef2de6240f43a2a01c40071ed5cd71",
            "d17224f8c19149e286cd9dbc1cba165e",
            "fabe7d65562f45ebacde6ecf56ad2fb2",
            "9390b25f216243559f84b8879234eb3b",
            "6dd09e1cbc8143fdb318b52259a1e5f4",
            "fb3e02568a4e4934a176c139e76ce43a",
            "0acc4fcf002b46de94937217c5c7345e",
            "9fa16b7f4d4c41f38a88e4a4d0fc9eae",
            "06b0d6e6d3e84f43b811dd879cbaf9a5",
            "16ea826f137e40f3bb6ca0ec8c669b13",
            "80f4a44cc2b44b6a9c28185a33d23d9e"
          ]
        },
        "id": "BMOLdfgmKQ02",
        "outputId": "8c95289d-5b9b-447e-86ad-3c389073287e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py:476: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0fef2de6240f43a2a01c40071ed5cd71"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = LlamaTokenizer.from_pretrained(base_model_id, use_fast=False, trust_remote_code=True, add_eos_token=True\n",
        "\n",
        "                              )\n",
        "\n",
        "modelFinetuned = PeftModel.from_pretrained(base_model, \"finetunedModel/checkpoint-20\")"
      ],
      "metadata": {
        "id": "lOIhR2C2MISD"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "eval_prompt = \"\"\"<s>[INST] <<SYS>>\n",
        "You are given a question, a sample answer, and the maximum marks for the question.\n",
        "Identify the important points in the answer and generate a scoring rubric.\n",
        "The scoring rubric is a JSON object containing the criteria and maximum marks for having that point in the answer.\n",
        "You can have as many criteria as you want.\n",
        "The sum of max_marks should be equal to the total marks for the question.\n",
        "\n",
        "Your response should be in this JSON format:\n",
        "{ \"rubric\": [ { \"criteria\": \"<criteria description>\", \"max_marks\": <marks> }, { \"criteria\": \"<criteria description>\", \"max_marks\": <marks> } ] }\n",
        "<</SYS>>\n",
        "\n",
        "-Question: What are the functions of the human heart?\n",
        "-Total Marks: 3\n",
        "-Answer: The heart pumps blood throughout the body. It supplies oxygen and nutrients to tissues and removes carbon dioxide and other wastes.\n",
        "[/INST]\"\"\"\n",
        "\n",
        "promptTokenized = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "modelFinetuned.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "  print(tokenizer.decode(modelFinetuned.generate(**promptTokenized, max_new_tokens=1024)[0], skip_special_tokens=True))\n",
        "  torch.cuda.empty_cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsl5SpQrMblq",
        "outputId": "6d826f7d-37bd-4495-92bb-50162648e03f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST] <<SYS>>\n",
            "You are given a question, a sample answer, and the maximum marks for the question.\n",
            "Identify the important points in the answer and generate a scoring rubric.\n",
            "The scoring rubric is a JSON object containing the criteria and maximum marks for having that point in the answer.\n",
            "You can have as many criteria as you want.\n",
            "The sum of max_marks should be equal to the total marks for the question.\n",
            "\n",
            "Your response should be in this JSON format:\n",
            "{ \"rubric\": [ { \"criteria\": \"<criteria description>\", \"max_marks\": <marks> }, { \"criteria\": \"<criteria description>\", \"max_marks\": <marks> } ] }\n",
            "<</SYS>>\n",
            "\n",
            "-Question: What are the functions of the human heart?\n",
            "-Total Marks: 3\n",
            "-Answer: The heart pumps blood throughout the body. It supplies oxygen and nutrients to tissues and removes carbon dioxide and other wastes.\n",
            "[/INST]t is important to identify the key points in the answer and assign marks accordingly. Here is a scoring rubric for the answer:\n",
            "\n",
            "{\n",
            "\"rubric\": [\n",
            "{\n",
            "\"criteria\": \"Supplying oxygen and nutrients to tissues\",\n",
            "\"max_marks\": 1\n",
            "},\n",
            "{\n",
            "\"criteria\": \"Removing carbon dioxide and other wastes\",\n",
            "\"max_marks\": 1\n",
            "},\n",
            "{\n",
            "\"criteria\": \"Pumping blood throughout the body\",\n",
            "\"max_marks\": 1\n",
            "}\n",
            "]\n",
            "}\n",
            "\n",
            "In this rubric, the answer is awarded marks for each of the three functions of the heart mentioned in the answer. The maximum marks for each function are also mentioned. The total marks for the answer are 3, which is the same as the total marks for the question.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oSP6lR1YNWnJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}