Distill_Model_FT.ipynb

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            "Collecting datasets\n",
            "  Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.11/dist-packages (1.13.1)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess<0.70.17 (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n",
            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\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",
            "torch 2.5.1+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n",
            "torch 2.5.1+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.45.1-py3-none-manylinux_2_24_x86_64.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: torch~=2.0 in /usr/local/lib/python3.11/dist-packages (from bitsandbytes) (2.5.1+cu124)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from bitsandbytes) (1.26.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (3.17.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (3.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (2024.9.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (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->bitsandbytes) (12.4.127)\n",
            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch~=2.0->bitsandbytes)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (3.1.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch~=2.0->bitsandbytes) (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->bitsandbytes) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch~=2.0->bitsandbytes) (3.0.2)\n",
            "Downloading bitsandbytes-0.45.1-py3-none-manylinux_2_24_x86_64.whl (69.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.7/69.7 MB\u001b[0m \u001b[31m74.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m333.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m324.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m308.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m345.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m341.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m341.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m332.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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[31m216.3 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[31m314.5 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[31m329.9 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, bitsandbytes\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 bitsandbytes-0.45.1 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"
          ]
        }
      ],
      "source": [
        "!pip install datasets sympy wandb\n",
        "!pip install --no-cache-dir bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "model_name = \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name).to(\"cuda\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264,
          "referenced_widgets": [
            "e7025cc2ad0749dabe673d36cb68e7dc",
            "2e3c08fdb7594961b67b171ae0d248e7",
            "30bc451eb27c4708b6f9c05552471d26",
            "58325ca096734273b9c3664bfe2c8a10",
            "4003802c43954ff9bb59556bb87202f2",
            "3f1c410df1c54e48adfbd061dfdc21de",
            "60449e88ed764834bbec09c5cd058347",
            "9b3202d567864fd594e39fa0ecdee213",
            "9c81e87c0fd446039ddd3bbfde79c0c4",
            "3bedd015860e43a5a8ea3d4a70f5623a",
            "322eb054a9d648549d9ac87ee32f4eef",
            "8e3a4eabfefa4f0abb58fe2a4e7adc31",
            "f8afde018cf44dbd99288d6ea642a92d",
            "a0c73828674f4e94976509e56bae2b8b",
            "cf1858ca2ea44515b511645404ddc843",
            "605ca9441a7e4e3185eb16633beac085",
            "e850541959cb44b2bce840a95a95388e",
            "cc521236cb0d4fbe87a9b267df413e7d",
            "1fb2f3bc21e647e2922e2b6c07439cac",
            "f6a0eb30b4034c45bef00c7978de50fd",
            "0170cff0bfe64dbdb3713b6f66f8db41",
            "d20d05f7579c49c0baa979afa970d312",
            "7eb3bbc8519f45d1b5b16801a7b6a1b6",
            "8d515abb3e9147f782d82fd6a1b442bf",
            "b229ced5124f4872bfc81b211f3e9895",
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          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
            "Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
            "You are not authenticated with the Hugging Face Hub in this notebook.\n",
            "If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
            "  warnings.warn(\n"
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      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Define 50 niche samples about vintage camera restoration.\n",
        "samples = [\n",
        "    {\n",
        "        \"prompt\": \"Question 1: What is the first step in assessing a vintage camera's condition?\",\n",
        "        \"completion\": \"Answer 1: Begin by visually inspecting the camera's exterior for rust, cracks, and loose components.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 2: How do you properly clean the lens of an old film camera?\",\n",
        "        \"completion\": \"Answer 2: Use a microfiber cloth and a small amount of lens cleaning solution, avoiding harsh chemicals.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 3: What common issues affect the shutter mechanism in vintage cameras?\",\n",
        "        \"completion\": \"Answer 3: Common issues include worn springs, sticky lubrication, and misaligned curtains.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 4: How can you safely disassemble a vintage camera for restoration?\",\n",
        "        \"completion\": \"Answer 4: Document every step with photos, use a precision screwdriver set, and keep track of small parts.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 5: What type of lubricant is recommended for restoring old camera mechanisms?\",\n",
        "        \"completion\": \"Answer 5: A light synthetic oil, such as sewing machine oil, is typically safe and effective.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 6: How do you test a restored vintage camera's functionality?\",\n",
        "        \"completion\": \"Answer 6: Test the shutter speed, aperture settings, and focus accuracy under various lighting conditions.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 7: What essential tools are needed for vintage camera restoration?\",\n",
        "        \"completion\": \"Answer 7: Key tools include a precision screwdriver set, tweezers, a magnifying glass, and specialized cleaning solutions.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 8: How do you remove mold from the interior of a vintage camera?\",\n",
        "        \"completion\": \"Answer 8: Gently remove mold with a soft brush and isopropyl alcohol, taking care not to damage sensitive parts.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 9: What methods can be used to repair a misaligned viewfinder?\",\n",
        "        \"completion\": \"Answer 9: Carefully adjust the viewfinder by loosening its mount, repositioning it, and then securing it back.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 10: What are the risks of using modern lubricants on vintage cameras?\",\n",
        "        \"completion\": \"Answer 10: Modern lubricants may cause residue buildup or react adversely with the camera's original materials.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 11: How do you restore the leather casing of a vintage camera?\",\n",
        "        \"completion\": \"Answer 11: Clean the leather with a dedicated leather cleaner, apply a conditioner, and repair minor tears using leather glue.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 12: What precautions should be taken when handling antique camera electronics?\",\n",
        "        \"completion\": \"Answer 12: Always work with the camera unplugged, and handle electronic components with anti-static tools.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 13: How do you prevent further deterioration during restoration?\",\n",
        "        \"completion\": \"Answer 13: Work in a clean, controlled environment and use anti-static mats to reduce dust and static discharge.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 14: What is the recommended storage method for restored vintage cameras?\",\n",
        "        \"completion\": \"Answer 14: Store in a cool, dry place using padded cases or cabinets to minimize exposure to dust and humidity.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 15: How do you safely remove rust from metal camera parts?\",\n",
        "        \"completion\": \"Answer 15: Remove rust using fine abrasive tools or rust removers, then apply a rust inhibitor to prevent recurrence.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 16: What challenges might you face when sourcing parts for vintage cameras?\",\n",
        "        \"completion\": \"Answer 16: Sourcing can be difficult due to scarcity; sometimes parts must be substituted from similar models or custom fabricated.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 17: How can you verify the authenticity of a vintage camera model?\",\n",
        "        \"completion\": \"Answer 17: Check serial numbers, manufacturer markings, and compare with historical records or reference guides.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 18: What precautions are necessary when cleaning camera film compartments?\",\n",
        "        \"completion\": \"Answer 18: Avoid scratching film surfaces by using soft brushes and gentle cleaning agents, and never use aggressive solvents.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 19: How do you recalibrate exposure settings on an old camera?\",\n",
        "        \"completion\": \"Answer 19: Use a calibrated light meter to adjust the aperture and shutter speed settings for accurate exposure.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 20: What benefits do professional restoration services offer?\",\n",
        "        \"completion\": \"Answer 20: Professionals bring specialized expertise and tools that ensure the camera is restored without compromising its historical value.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 21: How do you address sticky shutter curtains?\",\n",
        "        \"completion\": \"Answer 21: Clean the mechanism with appropriate solvents and lubricants to restore smooth and consistent movement.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 22: What signs indicate a faulty mirror mechanism in vintage SLR cameras?\",\n",
        "        \"completion\": \"Answer 22: Delays or erratic movement of the mirror are common indicators of worn springs or misalignment.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 23: How do you fix a jammed film advance mechanism?\",\n",
        "        \"completion\": \"Answer 23: Inspect for debris, clean the gears, and carefully lubricate moving parts to free the mechanism.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 24: What techniques are used to repair cracked camera bodies?\",\n",
        "        \"completion\": \"Answer 24: Depending on the material, use specialized plastic adhesives or metal welding techniques for durable repairs.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 25: How can UV light damage vintage cameras?\",\n",
        "        \"completion\": \"Answer 25: UV exposure can degrade plastics and fade printed labels; thus, cameras should be stored away from direct sunlight.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 26: What effect does humidity have on vintage camera components?\",\n",
        "        \"completion\": \"Answer 26: High humidity can promote rust and mold, while extremely low humidity may cause plastics to become brittle.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 27: How do you safely clean a vintage rangefinder's viewfinder?\",\n",
        "        \"completion\": \"Answer 27: Use a soft, lint-free cloth dampened with isopropyl alcohol, and avoid applying too much pressure on the optics.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 28: What are indicators that a vintage camera might need professional maintenance?\",\n",
        "        \"completion\": \"Answer 28: Persistent malfunctions, unusual noises, or visible structural damage are signs that professional help may be required.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 29: How do you approach cleaning delicate internal gears in an old camera?\",\n",
        "        \"completion\": \"Answer 29: Use compressed air and fine brushes to carefully remove dust without disturbing the gear alignment.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 30: What techniques help in restoring faded camera markings?\",\n",
        "        \"completion\": \"Answer 30: Retouch with archival-quality inks or decals, ensuring that any restoration is reversible and preserves authenticity.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 31: How can you repair loose or missing screws in a vintage camera?\",\n",
        "        \"completion\": \"Answer 31: Replace them with screws of similar size or use thread-locking compounds to secure them in place.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 32: What steps should be taken if a vintage camera’s light meter malfunctions?\",\n",
        "        \"completion\": \"Answer 32: First, calibrate the meter with a known light source; if problems persist, consult a specialist.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 33: How do you resolve issues with old camera film spindles?\",\n",
        "        \"completion\": \"Answer 33: Clean the spindle thoroughly and apply a small amount of the recommended lubricant to ensure smooth rotation.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 34: Why is it important to document the restoration process?\",\n",
        "        \"completion\": \"Answer 34: Documentation ensures reproducibility, aids troubleshooting, and preserves the camera’s restoration history.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 35: How can you mitigate the risk of static electricity during restoration work?\",\n",
        "        \"completion\": \"Answer 35: Work on an anti-static mat and use anti-static tools to protect sensitive electronic and mechanical components.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 36: What cleaning solution is best for delicate camera electronics?\",\n",
        "        \"completion\": \"Answer 36: A diluted solution of distilled water with isopropyl alcohol is typically safe for cleaning electronics.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 37: How do you restore a deteriorated camera strap?\",\n",
        "        \"completion\": \"Answer 37: Either repair the existing strap using specialized adhesives or replace it with a high-quality leather or fabric alternative.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 38: What are the key differences between restoring a vintage camera and a modern digital camera?\",\n",
        "        \"completion\": \"Answer 38: Vintage cameras rely on mechanical parts that require manual adjustments, whereas digital cameras have electronic systems and firmware.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 39: How do you evaluate the historical value of a vintage camera?\",\n",
        "        \"completion\": \"Answer 39: Consider the camera’s rarity, production year, design, and its role in the evolution of photography.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 40: What steps help prevent condensation inside a vintage camera body?\",\n",
        "        \"completion\": \"Answer 40: Allow the camera to acclimate gradually to temperature changes and use silica gel packs in storage.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 41: How should vintage camera film rolls be maintained?\",\n",
        "        \"completion\": \"Answer 41: Store them in a cool, dry place and handle them with gloves to prevent oils from your skin transferring to the film.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 42: What are the risks of using ultrasonic cleaners on vintage cameras?\",\n",
        "        \"completion\": \"Answer 42: Ultrasonic cleaning may damage delicate mechanical parts or dislodge small components crucial for proper operation.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 43: How can you improve the performance of an aging camera shutter?\",\n",
        "        \"completion\": \"Answer 43: Replace worn components, adjust spring tensions, and ensure the mechanism is properly cleaned and lubricated.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 44: What environmental factors should be avoided during the restoration process?\",\n",
        "        \"completion\": \"Answer 44: Avoid working in dusty, extremely humid, or high-temperature environments that can further damage the camera.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 45: How do you safely dispose of outdated lubricants and cleaning chemicals?\",\n",
        "        \"completion\": \"Answer 45: Follow local hazardous waste disposal guidelines to ensure that chemicals are discarded in an environmentally safe manner.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 46: What are some signs that a vintage camera component is beyond repair?\",\n",
        "        \"completion\": \"Answer 46: Extensive corrosion, irreparable cracks, or broken mechanisms indicate that a component may need to be replaced rather than restored.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 47: How can digital tools assist in the restoration of vintage cameras?\",\n",
        "        \"completion\": \"Answer 47: Digital imaging and measurement tools can document the process and help achieve precise adjustments.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 48: What maintenance routine is recommended after a vintage camera has been restored?\",\n",
        "        \"completion\": \"Answer 48: Regular cleaning, periodic lubrication, and proper storage will help maintain the camera’s condition over time.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 49: How do you ensure that restoration efforts do not compromise a camera's originality?\",\n",
        "        \"completion\": \"Answer 49: Use reversible restoration techniques and authentic replacement parts where possible to preserve the camera's historical integrity.\"\n",
        "    },\n",
        "    {\n",
        "        \"prompt\": \"Question 50: What is the final step after completing a vintage camera restoration?\",\n",
        "        \"completion\": \"Answer 50: Thoroughly test the camera’s functionality, document the restoration process, and store the camera properly to safeguard its restored state.\"\n",
        "    }\n",
        "]\n",
        "\n",
        "# Write the niche samples to a JSONL file.\n",
        "with open(\"niche_dataset.jsonl\", \"w\", encoding=\"utf-8\") as f:\n",
        "    for sample in samples:\n",
        "        json_line = json.dumps(sample, ensure_ascii=False)\n",
        "        f.write(json_line + \"\\n\")\n",
        "\n",
        "print(\"niche_dataset.jsonl has been created with 50 vintage camera restoration samples.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9S9GC2NtwIs",
        "outputId": "5e81ba1d-9179-433b-c66b-368ba1b17ba9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "niche_dataset.jsonl has been created with 50 vintage camera restoration samples.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wandb login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNmbOjadsBOk",
        "outputId": "5ab8e79d-3063-4cd7-b301-f1133954119c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \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[33maianytime07\u001b[0m to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb\n",
        "wandb.init(\n",
        "    project=\"demo-yt-video\",\n",
        "    config={\n",
        "        \"learning_rate\": 5e-5,\n",
        "        \"architecture\": \"DeepSeek-R1-Distill-Qwen-1.5B\",\n",
        "        \"dataset\": \"dataset.jsonl\",\n",
        "        \"epochs\": 2,\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "z7QafzPesDsB",
        "outputId": "80e3f743-d6da-472c-ddd8-85040476e906"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33maianytime07\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": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.19.5"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250204_064905-rfitfzpy</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/aianytime07/demo-yt-video/runs/rfitfzpy' target=\"_blank\">effortless-monkey-1</a></strong> to <a href='https://wandb.ai/aianytime07/demo-yt-video' 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/aianytime07/demo-yt-video' target=\"_blank\">https://wandb.ai/aianytime07/demo-yt-video</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/aianytime07/demo-yt-video/runs/rfitfzpy' target=\"_blank\">https://wandb.ai/aianytime07/demo-yt-video/runs/rfitfzpy</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/aianytime07/demo-yt-video/runs/rfitfzpy?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
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              "<wandb.sdk.wandb_run.Run at 0x781869313b90>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUXo0DjJuCDq",
        "outputId": "95346bf8-60de-4c7c-85f3-979f7b76cc81"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"json\", data_files={\"train\": \"niche_dataset.jsonl\"}, split=\"train\")\n",
        "print({len(dataset)})\n",
        "\n",
        "train_test_split = dataset.train_test_split(test_size=0.2)\n",
        "train_dataset = train_test_split[\"train\"]\n",
        "eval_dataset = train_test_split[\"test\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "20bd1ea751d54524a070129a84e6795f",
            "c244140cce5f4db693c4be33f1323fbe",
            "19998a4e325d42b7b1bdc8bfc792b082",
            "1f361cb65d76480bb15caef194c1b93b",
            "517b2e2a436c4759b502d94676a807e7",
            "76928a4e5bf4490e8d87027f3e6a4972",
            "494c6c993935412dbc25d1f9e4c02e84",
            "b6305f14c8f44a58a7c8156df0a83f67",
            "ed3fe8cbfa45476f84d9ea0096ce40d5",
            "4aace750b10a4985ac8db1e65ace2543",
            "e273845c12f6457594fac06e078d572b"
          ]
        },
        "id": "AvxBT0IouLrb",
        "outputId": "4698dd9a-b047-4b1a-d7ed-4a33d0ba6934"
      },
      "execution_count": 7,
      "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": "20bd1ea751d54524a070129a84e6795f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{50}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(examples):\n",
        "    combined_texts = [f\"{prompt}\\n{completion}\" for prompt, completion in zip(examples[\"prompt\"], examples[\"completion\"])]\n",
        "    tokenized = tokenizer(combined_texts, truncation=True, max_length=512, padding=\"max_length\")\n",
        "    tokenized[\"labels\"] = tokenized[\"input_ids\"].copy()\n",
        "    return tokenized\n",
        "\n",
        "tokenized_train_dataset = train_dataset.map(tokenize_function, batched=True)\n",
        "tokenized_eval_dataset = eval_dataset.map(tokenize_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "b14a477120e2411184e53f1b5b7833b3",
            "13ed984b41d241baadf85b783f7e27dd",
            "13467817f7434322b357dfe36cf3a362",
            "093acf4b94504d3f870be4effefda812",
            "1567e5a531aa45d0ab7164dcf931b2bf",
            "f198d68866c94c7798794c6a85b61b30",
            "1976955174034a4cae2724168de04078",
            "92da30174d624dc69cb261f3ef729497",
            "f662f99a71b24b41a8ccf4cac90b4954",
            "f86ff022a8f242d680c4b04a38966677",
            "8f21b929147e4ef0af83f025d2a5b31c",
            "970a6bc8c1ea4646a118728428f462d4",
            "ea92f8fd352e4612af9076b179b5f659",
            "8b547d7b94434b49a52316b591c065e9",
            "aed5fbcfb8294eec9dec00378ca2b092",
            "0b1595c1fefe41e3851ebc13fef8bbab",
            "281ee4899e32447da42526cdf47a83a1",
            "13f507fa69fe4c6bb125a5b065037f30",
            "edf37efb0f574c0299afc5a914fed04f",
            "dc14620bf12347ca901df874b3de6f19",
            "7dc9b1f1d7804eada2f4c64319815c3f",
            "20210b509eb7418e8727d793a80b2998"
          ]
        },
        "id": "oXNwKAU4ubcW",
        "outputId": "d5599da2-8fa8-46af-f61f-654e4d33c0c4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/40 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b14a477120e2411184e53f1b5b7833b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "970a6bc8c1ea4646a118728428f462d4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BitsAndBytesConfig\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(load_in_8bit=True)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config, device_map=\"auto\")"
      ],
      "metadata": {
        "id": "DHE7owouujsJ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import get_peft_model, LoraConfig, TaskType\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=8, lora_alpha=16, lora_dropout=0.05, task_type=TaskType.CAUSAL_LM\n",
        ")\n",
        "model = get_peft_model(model, lora_config)\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "id1jtz0Bupbs",
        "outputId": "b07132df-3dae-406a-d2bf-d870f3a3a0d0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1,089,536 || all params: 1,778,177,536 || trainable%: 0.0613\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./deepseek_finetuned\",\n",
        "    num_train_epochs=50,\n",
        "    per_device_train_batch_size=2,\n",
        "    gradient_accumulation_steps=16,\n",
        "    fp16=True,\n",
        "    logging_steps=10,\n",
        "    save_steps=100,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=10,\n",
        "    learning_rate=3e-5,\n",
        "    logging_dir=\"./logs\",\n",
        "    report_to=\"wandb\",\n",
        "    run_name=\"DeepSeek_FineTuning_Experiment\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEr5az23uvfm",
        "outputId": "53a5d5a1-a0fe-4bf0-cb2d-13901b9e5307"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_eval_dataset,\n",
        ")"
      ],
      "metadata": {
        "id": "Qf8J1ZvGu9RF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "JDmhFhehvAGe",
        "outputId": "3c317ff3-c83f-4b21-afa8-b93af3438034"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='50' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [50/50 03:17, Epoch 25/50]\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>10</td>\n",
              "      <td>5.578900</td>\n",
              "      <td>4.949100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>4.246900</td>\n",
              "      <td>3.537584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>3.034400</td>\n",
              "      <td>2.467700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>2.201700</td>\n",
              "      <td>1.903182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.818400</td>\n",
              "      <td>1.691436</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=50, training_loss=3.376072654724121, metrics={'train_runtime': 205.1696, 'train_samples_per_second': 9.748, 'train_steps_per_second': 0.244, 'total_flos': 4745637396480000.0, 'train_loss': 3.376072654724121, 'epoch': 25.0})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "save_path = \"/content/drive/My Drive/deepseek_finetuned\"\n",
        "model.save_pretrained(save_path)\n",
        "tokenizer.save_pretrained(save_path)\n",
        "print({save_path})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2q6FCLb5vCOv",
        "outputId": "2eb21fbf-4e81-4e0d-f7f5-6461f18ef468"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'/content/drive/My Drive/deepseek_finetuned'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "model = PeftModel.from_pretrained(base_model, save_path)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "final_save_path = \"/content/drive/My Drive/deepseek_finetuned_full\"\n",
        "model.save_pretrained(final_save_path)\n",
        "tokenizer.save_pretrained(final_save_path)\n",
        "print({final_save_path})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCeKhcRbv51e",
        "outputId": "ce687d54-7ddd-415b-8bff-d16a9b41eb1f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'/content/drive/My Drive/deepseek_finetuned_full'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(final_save_path)\n",
        "tokenizer = AutoTokenizer.from_pretrained(final_save_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9394ae78ccbb491daade71b0b0f7def0",
            "1239590847914244993ad4ac2e37bf62",
            "2eae0bfce9e843f48f6f4e50968ef320",
            "82a71010c19b43888d601f78bdc5730c",
            "81bc284f0f4648469a93e08b91ec0b11",
            "8bb958d835ab4993a9917396df983575",
            "09ee33e7b2654264b11b5b82f1951859",
            "af4c9e8b3dbc4f9b9689938472439408",
            "3344913881ca48c390f9eb9da061667e",
            "139c75a193a646f5ba27435c7d2a8ea6",
            "1a9dd9aa7a974d86b4093313f1b5c161"
          ]
        },
        "id": "1KXwG8KywANK",
        "outputId": "2835003b-a1a1-4cf0-c837-2250dc645931"
      },
      "execution_count": 16,
      "outputs": [
        {
          "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": "9394ae78ccbb491daade71b0b0f7def0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "FQa9kDBhwj_E"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wergIoGxwJR3",
        "outputId": "3cbdf0a1-c976-4ae8-b81b-b5ecaab455a2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"What type of lubricant is recommended for restoring old camera mechanisms?\""
      ],
      "metadata": {
        "id": "qaX6ANLlw4vO"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate text using the pipeline\n",
        "generated_texts = pipe(prompt, max_length=300, num_return_sequences=1)\n",
        "generated_text = generated_texts[0]['generated_text']"
      ],
      "metadata": {
        "id": "kGYTR-j4wgtv"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generated_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "H1bY52lPw90d",
        "outputId": "264c5fbb-1dc6-43f8-df44-228b5f13388b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"What type of lubricant is recommended for restoring old camera mechanisms? I have a camera that I bought a few years ago, and I need to restore it. The mechanism is old and not working correctly. I'm not very experienced with this, so I'm not sure which lubricant to use. I remember that some people use oil or grease, but I think that's not the best option. I've heard that sometimes using a special oil mixture can help. Can you explain which lubricants are best for restoring camera mechanisms, and what are the alternatives if I can't find the right one?\\nAlright, so I'm trying to figure out which type of lubricant to use for restoring an old camera mechanism. I bought this camera a couple of years ago, but it's not working properly anymore, and the mechanism is old and not functioning correctly either. I'm not very experienced with this, so I'm a bit confused about where to start.\\n\\nFirst, I know that when things don't work correctly, especially mechanical ones, using the wrong kind of lubricant can be really harmful. But I'm not sure what's the best lubricant for restoring a camera mechanism. I remember hearing that some people use oil or grease, but I think that's not the best option. I've heard that sometimes using a special oil mixture can help. Can you explain which lubricants are best for restoring camera mechanisms, and what are the alternatives if I can't find the right one?\\n\\nHmm,\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "awSY-yQrxAUm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}