Copy_of_College.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TusharPaul01/ChatBot-ML-LSTM-/blob/main/Copy_of_College.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKtlAr2TNUaH",
        "outputId": "6ec50e67-bfa0-4cc0-9a80-0fb025daea95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.7/536.7 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\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",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q -U datasets scipy ipywidgets matplotlib einops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "88WCy0gb0XR4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "bf2e723e8e4d48e69837ad27ef039a1d",
            "5a60db2fa57548dbad89acd64263be7e",
            "46aeee328ac342b28c1c1958445cd6d1",
            "a35bebfad44b4c4abf67fab7bb05346e",
            "576267ee0b034f7aaab99ba4a89acb6a",
            "8ffb849df1e24f7c98e89d4def13b57f",
            "fad9934245914531b3db0787c8857747",
            "49f40fdb3b5e42049c2cd3971fa08847",
            "19c80d1fc89f4a3395f1bd71e4c11291",
            "337fea4f4dbb4eec98cf80e08ed59000",
            "1a35f20fe9b4499cba8349d45196d93e"
          ]
        },
        "outputId": "f0b43ac7-1deb-4605-d93f-9dad89b0ae35"
      },
      "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": "bf2e723e8e4d48e69837ad27ef039a1d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_dataset = load_dataset('json', data_files='/content/juitcollegeslm.jsonl', split='train')\n",
        "eval_dataset = load_dataset('json', data_files='/content/juitcollegeslm.jsonl', split='train')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lMTEVusCTq1Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3006f1f3-8a37-4523-822c-bd1a128f8e54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.28.0.dev0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.20.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z0Lx-BuVNpPY"
      },
      "outputs": [],
      "source": [
        "from accelerate import FullyShardedDataParallelPlugin, Accelerator\n",
        "from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig\n",
        "\n",
        "fsdp_plugin = FullyShardedDataParallelPlugin(\n",
        "    state_dict_config=FullStateDictConfig(offload_to_cpu=True, rank0_only=False),\n",
        "    optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=False),\n",
        ")\n",
        "\n",
        "accelerator = Accelerator(fsdp_plugin=fsdp_plugin)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SsNXfIVhNqFT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "145442a4-4151-4e13-cbc1-11c17c28eed7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m195.4/195.4 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.5/258.5 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q wandb -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JZ_QvJliNuS9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "f8644295-0e64-4052-a6ec-957dd22b8160"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.Javascript object>"
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              "                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",
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              "                url: 'https://wandb.ai/authorize'\n",
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              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
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              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
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        {
          "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\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ],
      "source": [
        "import wandb, os\n",
        "wandb.login()\n",
        "#eae2072c781330577d4ddda6008d0abeef100837\n",
        "wandb_project = \"viggo-finetune\"\n",
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        "    os.environ[\"WANDB_PROJECT\"] = wandb_project"
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      "execution_count": null,
      "metadata": {
        "id": "odSs4iWlNwqS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bc763d9-335e-4693-839a-cc7cbe030ee5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.17.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.13.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (15.0.0)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.3)\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpXfkgeSNyOo"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "train_dataset = load_dataset('json', data_files='/content/juitcollegeslm.jsonl', split='train')\n",
        "eval_dataset = load_dataset('json', data_files='/content/juitcollegeslm.jsonl', split='train')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_XjItr5bN2bi"
      },
      "outputs": [],
      "source": [
        "def formatting_func(example):\n",
        "    text = f\"### Question: {example['input']}\\n ### Answer: {example['output']}\"\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K0eZgj3iN3va",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dfebe3f-a851-4bc2-9f39-e23cb0b8da2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (0.7.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install einops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_oMYjszbN5XB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcc796ca-b60e-4f8e-a100-8e2358665cff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
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            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.28.0.dev0)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
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            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
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            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Looking in indexes: https://test.pypi.org/simple/\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.42.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.12.0)\n",
            "Requirement already satisfied: numpy<1.29.0,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from scipy->bitsandbytes) (1.25.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install torch\n",
        "!pip install accelerate\n",
        "!pip install -i https://test.pypi.org/simple/ bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTByR97ZN8jB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280,
          "referenced_widgets": [
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        },
        "outputId": "0dfd1d55-2c61-42e3-aca1-07b038738006"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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,
              "version_minor": 0,
              "model_id": "27ba456282da421ebda35fda1bbdd0e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6cb73e78467045fd91c855ebeda3323d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
            "- configuration_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_phi.py:   0%|          | 0.00/62.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c13fb5222c94d9e9b1b9e8cd385fdfd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
            "- modeling_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n",
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/35.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93c4ac2ed90d44b9af2bdcb1e05b4c62"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97df44d485d442f4a2b9779c197f8ac2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f00d2df0532f441d869d7e41fb761805"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fae9883652854accaa2c665ca1f89758"
            }
          },
          "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": "000ac5633922471aa1e9fc72f9930055"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f20eaefd7ae0499c8de8a8cb7694fc12"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "base_model_id = \"microsoft/phi-2\"\n",
        "model = AutoModelForCausalLM.from_pretrained(base_model_id, trust_remote_code=True, torch_dtype=torch.float16, load_in_8bit=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7U477aIaOEEX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54,
          "referenced_widgets": [
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          ]
        },
        "outputId": "b7f931bd-e285-4950-cad7-bbfe4635a998"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e77b90d675884c3296547a669d9e7af1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd7482597c7d4263a425bb969fe96f23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea2b04da9dc345148ddbd42e9a2e413f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c379028f25b4ece9e344180d17f7bbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15095c88440b423cb9af82d3b91cfa47"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2bdd92d6ec80450e907e71ed7c15bb39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    base_model_id,\n",
        "    padding_side=\"left\",\n",
        "    add_eos_token=True,\n",
        "    add_bos_token=True,\n",
        "    use_fast=False,\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "def generate_and_tokenize_prompt(prompt):\n",
        "    return tokenizer(formatting_func(prompt))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pH45Hx5sOEj_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "72c48ee3133547c9b06199891c117528",
            "b9418dd477fb4a04a57bde13e1f575d4",
            "a4f4bd0709f240b7929863e741aa8de4",
            "e3e9957320714bedbf41bf6ae4184abf",
            "53f149bc7aed4a01828e7b226787e3cc",
            "c8ddf07d28f146f198369046e38f1096",
            "441863be7dce4dd486723b3de4b34b95",
            "be174b006aa1439fb88c6dcae06ec8c9",
            "5f74f3102b8f4c2daec5a9430872de0e",
            "01629aafa1454c97ac196ccf6df9224c",
            "f39a841b8b3b4194b72d0b52bc201e76"
          ]
        },
        "outputId": "1ae3ae88-8f7b-4f88-fab4-b21f4d621dc8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/491 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72c48ee3133547c9b06199891c117528"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)\n",
        "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-4PPtqP1OJzg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "acbb515c-5a1f-4185-b1d1-51529285e08c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "982\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_data_lengths(tokenized_train_dataset, tokenized_val_dataset):\n",
        "    lengths = [len(x['input_ids']) for x in tokenized_train_dataset]\n",
        "    lengths += [len(x['input_ids']) for x in tokenized_val_dataset]\n",
        "    print(len(lengths))\n",
        "\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.hist(lengths, bins=20, alpha=0.7, color='blue')\n",
        "    plt.xlabel('Length of input_ids')\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.title('Distribution of Lengths of input_ids')\n",
        "    plt.show()\n",
        "\n",
        "plot_data_lengths(tokenized_train_dataset, tokenized_val_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qKX6zXICOLDZ"
      },
      "outputs": [],
      "source": [
        "max_length = 25 # This was an appropriate max length for my dataset\n",
        "\n",
        "def generate_and_tokenize_prompt2(prompt):\n",
        "    result = tokenizer(\n",
        "        formatting_func(prompt),\n",
        "        truncation=True,\n",
        "        max_length=max_length,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rT8WUZzfONV_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "1c7abf64e34847b29e0a3540e2bf8d5b",
            "8cc22af87c9b41e2b1c76d6841a583b3",
            "9037bbbe9029438ea7085cb1a2dcb868",
            "b0eecdc0a41b472f9c4f7a3a16710169",
            "e48efdbcd303473787add5e757cfa59c",
            "e28798aa9a294397b6fbf311a5d845d2",
            "ed5b285159b2497aa8c8dedf712ec4d5",
            "cefb85cc67f84d43b5f3cbb8a2a66acf",
            "aed054487fb549469a176171aacca494",
            "67e067e0779f4b7b8094967544d5a15f",
            "4788f803a4584e249a6654b8bdd0f0f8"
          ]
        },
        "outputId": "3efe885b-900c-4207-dc0e-3c464412657d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/491 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c7abf64e34847b29e0a3540e2bf8d5b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt2)\n",
        "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2OBiGeDhORBE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6712b046-8417-43b1-f980-552083b49575"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 21017, 18233, 25, 15902, 198, 44386, 23998, 25, 18435, 43179]\n"
          ]
        }
      ],
      "source": [
        "print(tokenized_train_dataset[0]['input_ids'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mVO-RozhORiR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "f5deeae8-410c-4142-ba1d-951e0fb2ec1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "982\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_data_lengths(tokenized_train_dataset, tokenized_val_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aFvgX5I7OUVl"
      },
      "outputs": [],
      "source": [
        "eval_prompt = \"\"\"what is the name of your creators\n",
        "\n",
        "\n",
        "### Output:\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TMIRQWQ_OV52"
      },
      "outputs": [],
      "source": [
        "eval_prompt = \" Students of JUIT: Tushar Paul (201007) & Anmol Bansal (201274) & Yash Tripathi (221030324) & Satuti Seth(21486) # \""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-1PRhfAhOYvU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d25da02-8ef8-4b49-ad00-4e7b01647139"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Students of JUIT: Tushar Paul (201007) & Anmol Bansal (201274) & Yash Tripathi (221030324) & Satuti Seth(21486) # \n",
            "\n",
            "# Introduction to the course :\n",
            "\n",
            "The purpose of this course is to provide students with a basic understanding of the principles and applications of engineering. The course covers topics such as mechanics, thermodynamics, electricity, magnetism, optics, acoustics, electronics, circuits, signals, systems, control, robotics, etc. The course also introduces students to some common tools and techniques used in engineering design and analysis, such as mathematical modeling, simulation, optimization, testing, measurement, etc. By completing this course, students will be able to apply their knowledge and skills to solve real-world problems and create innovative solutions.\n",
            "\n",
            "## Learning Objectives:\n",
            "\n",
            "By the end of this course, you should be able to:\n",
            "\n",
            "- Explain the concepts and laws of physics that govern various phenomena and processes in nature and technology.\n",
            "- Apply the fundamental principles of mathematics and logic to analyze and model complex systems and situations.\n",
            "- Use appropriate methods and instruments to measure physical quantities and parameters accurately and reliably.\n",
            "- Design and construct simple machines and devices using mechanical, electrical, or electronic components and materials.\n",
            "- Understand the basics of signal processing and communication systems and how they work.\n",
            "- Learn about different types of sensors, actuators, controllers, and\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "base_model_id = \"microsoft/phi-2\"\n",
        "eval_tokenizer = AutoTokenizer.from_pretrained(\n",
        "    base_model_id,\n",
        "    add_bos_token=True,\n",
        "    use_fast=False, # needed for now, should be fixed soon\n",
        ")\n",
        "\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    print(eval_tokenizer.decode(model.generate(**model_input, max_new_tokens=256, repetition_penalty=1.15)[0], skip_special_tokens=True))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yCKBMzXCOcHl"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-3dahpCZQrG5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edca61ec-5c6d-48ef-f139-0e704f43c7a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PhiForCausalLM(\n",
            "  (model): PhiModel(\n",
            "    (embed_tokens): Embedding(51200, 2560)\n",
            "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x PhiDecoderLayer(\n",
            "        (self_attn): PhiAttention(\n",
            "          (q_proj): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "          (k_proj): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "          (v_proj): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "          (dense): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "          (rotary_emb): PhiRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): PhiMLP(\n",
            "          (activation_fn): NewGELUActivation()\n",
            "          (fc1): Linear8bitLt(in_features=2560, out_features=10240, bias=True)\n",
            "          (fc2): Linear8bitLt(in_features=10240, out_features=2560, bias=True)\n",
            "        )\n",
            "        (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
            "        (resid_dropout): Dropout(p=0.1, inplace=False)\n",
            "      )\n",
            "    )\n",
            "    (final_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (lm_head): Linear(in_features=2560, out_features=51200, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aQkC8cSuQzA3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88fa3d0b-b19e-43a4-9a02-d8f8886a2134"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 26214400 || all params: 2805898240 || trainable%: 0.9342605382581515\n"
          ]
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=32,\n",
        "    lora_alpha=64,\n",
        "    target_modules=[\n",
        "        \"Wqkv\",\n",
        "        \"fc1\",\n",
        "        \"fc2\",\n",
        "    ],\n",
        "    bias=\"none\",\n",
        "    lora_dropout=0.05,  # Conventional\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DZqjGPqFQ6dy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eedbdc0-6eac-4f54-f6f5-1da5d79c4669"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): PhiForCausalLM(\n",
            "      (model): PhiModel(\n",
            "        (embed_tokens): Embedding(51200, 2560)\n",
            "        (embed_dropout): Dropout(p=0.0, inplace=False)\n",
            "        (layers): ModuleList(\n",
            "          (0-31): 32 x PhiDecoderLayer(\n",
            "            (self_attn): PhiAttention(\n",
            "              (q_proj): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "              (k_proj): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "              (v_proj): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "              (dense): Linear8bitLt(in_features=2560, out_features=2560, bias=True)\n",
            "              (rotary_emb): PhiRotaryEmbedding()\n",
            "            )\n",
            "            (mlp): PhiMLP(\n",
            "              (activation_fn): NewGELUActivation()\n",
            "              (fc1): lora.Linear8bitLt(\n",
            "                (base_layer): Linear8bitLt(in_features=2560, out_features=10240, bias=True)\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=2560, out_features=32, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=32, out_features=10240, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (fc2): lora.Linear8bitLt(\n",
            "                (base_layer): Linear8bitLt(in_features=10240, out_features=2560, bias=True)\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=10240, out_features=32, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=32, out_features=2560, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "            )\n",
            "            (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
            "            (resid_dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (final_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "      (lm_head): Linear(in_features=2560, out_features=51200, bias=True)\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UCoGCMiVQ7CI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1580364e-73cc-4391-991b-5128cb1a8feb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git@main\n",
            "  Cloning https://github.com/huggingface/transformers.git (to revision main) to /tmp/pip-req-build-zfsdg0b0\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-zfsdg0b0\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit f7ef7cec6c6c162087421f36a17eabdbb223579d\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.28.0.dev0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (0.4.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (4.66.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.0.dev0) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.28.0.dev0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.20.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "!pip install git+https://github.com/huggingface/transformers.git@main accelerate\n",
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dhaKsNGyQ_cK"
      },
      "outputs": [],
      "source": [
        "from accelerate import Accelerator\n",
        "model = accelerator.prepare_model(model)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tvD9qEaWRBWB"
      },
      "outputs": [],
      "source": [
        "if torch.cuda.device_count() > 1: # If more than 1 GPU\n",
        "    model.is_parallelizable = True\n",
        "    model.model_parallel = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GTAeeV8tRF1V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "outputId": "0e59dda0-8561-4ec8-ea72-ea61b9ef587d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:437: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href='https://wandb.me/wandb-init' target=\"_blank\">the W&B docs</a>."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mtusharpaul2001\u001b[0m (\u001b[33mtusharpaul\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 0.16.3"
            ]
          },
          "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-20240220_100335-t0saqbyh</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/tusharpaul/viggo-finetune/runs/t0saqbyh' target=\"_blank\">phi2-journal-finetune-2024-02-20-10-03</a></strong> to <a href='https://wandb.ai/tusharpaul/viggo-finetune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' 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/tusharpaul/viggo-finetune' target=\"_blank\">https://wandb.ai/tusharpaul/viggo-finetune</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/tusharpaul/viggo-finetune/runs/t0saqbyh' target=\"_blank\">https://wandb.ai/tusharpaul/viggo-finetune/runs/t0saqbyh</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='962' max='1965' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 962/1965 48:32 < 50:42, 0.33 it/s, Epoch 31.25/66]\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",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-7b1612d452e8>\u001b[0m in \u001b[0;36m<cell line: 36>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1616\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1617\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1618\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1619\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1620\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1954\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1955\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1957\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2903\u001b[0m                 \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2904\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2905\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2907\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_accumulation_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   2000\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2001\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2002\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2003\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2004\u001b[0m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             )\n\u001b[0;32m--> 492\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    493\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    252\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import transformers\n",
        "from datetime import datetime\n",
        "\n",
        "project = \"journal-finetune\"\n",
        "base_model_name = \"phi2\"\n",
        "run_name = base_model_name + \"-\" + project\n",
        "output_dir = \"./\" + run_name\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_val_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        output_dir=output_dir,\n",
        "        warmup_steps=2,\n",
        "        per_device_train_batch_size=4,  # Adjust batch size based on GPU memory\n",
        "        gradient_accumulation_steps=4,  # Accumulate gradients over 4 steps\n",
        "        max_steps=1965,\n",
        "        learning_rate=2e-5,\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        logging_steps=1965,\n",
        "        logging_dir=\"./logs\",\n",
        "        save_strategy=\"steps\",\n",
        "        save_steps=1965,\n",
        "        evaluation_strategy=\"steps\",\n",
        "        eval_steps=1965,\n",
        "        do_eval=True,\n",
        "        report_to=\"wandb\",\n",
        "        run_name=f\"{run_name}-{datetime.now().strftime('%Y-%m-%d-%H-%M')}\",\n",
        "        fp16=True  # Enable mixed-precision training\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "trainer.train()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eevXUN92RI5r"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "base_model_id = \"microsoft/phi-2\"\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model_id,  # Phi2, same as before\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        "    load_in_8bit=True,\n",
        "    torch_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True, use_fast=False)\n",
        "eval_tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L35KB7eJRNR8"
      },
      "outputs": [],
      "source": [
        "from peft import PeftModel\n",
        "\n",
        "ft_model = PeftModel.from_pretrained(base_model, \"phi2-journal-finetune/checkpoint-1965\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "filename = 'finalized_model.sav'\n",
        "pickle.dump(ft_model, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "hDSks9krIXJ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0NTsbj0rRN9T"
      },
      "outputs": [],
      "source": [
        "eval_prompt = \"what is the name of your creators #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JDlTJglfyfJA"
      },
      "outputs": [],
      "source": [
        "eval_prompt = \"placement #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"What should I do if I encounter issues with Wi-Fi connectivity?  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "ccm4phqhQGyA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"Who is principal ?  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "kLdZs3j-liAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"University location ?  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "08EUY1ZknN1-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"government scholarship ?  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "BybfW9LnpnF2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"Main events of JUIT ?  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "6DMtWrIVn6uc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"What are the hostel facilities in JUIT ?  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "lgAsld2qojs4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = \"I need placement details  #  Output \"\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "ft_model.eval()\n",
        "with torch.no_grad():\n",
        "    response = (eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0], skip_special_tokens=True))\n",
        "    print(response)"
      ],
      "metadata": {
        "id": "vHy8IswyrGDI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the index of the last occurrence of '.', '!', or '?'\n",
        "last_dot_index = response.rfind('.')\n",
        "last_exclamation_index = response.rfind('!')\n",
        "last_question_index = response.rfind('?')\n",
        "\n",
        "# Find the minimum of the three indices\n",
        "last_punctuation_index = max(last_dot_index, last_exclamation_index, last_question_index)\n",
        "\n",
        "# Trim the text to include only up to the last occurrence of '.', '!', or '?'\n",
        "trimmed_text = response[:last_punctuation_index + 1]\n",
        "\n",
        "# Print the final generated and trimmed response\n",
        "print(trimmed_text)\n"
      ],
      "metadata": {
        "id": "oKZROaqszPKZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jyYckaypRSX9"
      },
      "outputs": [],
      "source": [
        "print(response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XJPXtMcm3ael"
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      "outputs": [],
      "source": [
        "# Find the index of the first occurrence of '.'\n",
        "first_dot_index = response.find('.')\n",
        "\n",
        "# Trim the text to include only up to the first occurrence of '.'\n",
        "trimmed_text = response[:first_dot_index + 1]\n",
        "\n",
        "# Print the final generated and trimmed response\n",
        "print(trimmed_text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sYiUL-0IRVAW"
      },
      "outputs": [],
      "source": [
        "# Find the index of the last full stop in the generated text\n",
        "last_full_stop_index = response.rfind('.')\n",
        "\n",
        "# Trim the text to include only up to the last full stop\n",
        "trimmed_text = response[:last_full_stop_index + 1]\n",
        "\n",
        "# Print the final generated and trimmed response\n",
        "print(trimmed_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xw3MbobORVoS"
      },
      "outputs": [],
      "source": [
        "from getpass import getpass\n",
        "# uplink_key = getpass('Enter your Uplink key: ')\n",
        "\n",
        "uplink_key = 'server_4HCT7OIKTCSW4YS4RTQKYH5S-YFHDOPQNBUEHPK5M'"
      ]
    },
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      "execution_count": null,
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        "id": "SHRMgshyRZ7L"
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      "source": [
        "!pip install anvil-uplink"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "id": "tiePL8w4RcxI"
      },
      "outputs": [],
      "source": [
        "import anvil.server\n",
        "anvil.server.connect('server_75MFDSZ7TEXH6JBEEIFWH73S-RDAIQA43BJPOQXPX')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vb7RndVFRgva"
      },
      "outputs": [],
      "source": [
        "import anvil.server\n",
        "\n",
        "@anvil.server.callable\n",
        "def Server(sentence):\n",
        "    while True:\n",
        "      #system_prompt = \" \"\n",
        "      #eval_prompt = f\"{system_prompt} {user_input_prompt} #  Output \"\n",
        "      eval_prompt = sentence\n",
        "      model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "      ft_model.eval()\n",
        "      with torch.no_grad():\n",
        "          generated_tokens = ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0]\n",
        "\n",
        "      response = eval_tokenizer.decode(generated_tokens, skip_special_tokens=True)\n",
        "      last_full_stop_index = response.rfind('.')\n",
        "      res = response[:last_full_stop_index + 1]\n",
        "      return res\n",
        "      break\n"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jW8VypOKRi48"
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        "anvil.server.wait_forever()"
      ]
    },
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      "execution_count": null,
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        "# Take user input for the prompt\n",
        "user_input_prompt = input(\"Enter your prompt: \")\n",
        "\n",
        "# Define the system prompt\n",
        "system_prompt = \"Only give answers of the questions\"\n",
        "\n",
        "# Concatenate the system and user prompts\n",
        "eval_prompt = f\"{system_prompt} {user_input_prompt} #  Output \"\n",
        "\n",
        "# Tokenize the prompt and move it to the GPU\n",
        "model_input = eval_tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "# Set the model to evaluation mode\n",
        "ft_model.eval()\n",
        "\n",
        "# Generate a response\n",
        "with torch.no_grad():\n",
        "    generated_tokens = ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.11)[0]\n",
        "\n",
        "# Decode the generated tokens and skip special tokens\n",
        "response = eval_tokenizer.decode(generated_tokens, skip_special_tokens=True)\n",
        "\n",
        "last_full_stop_index = response.rfind('.')\n",
        "\n",
        "# Trim the text to include only up to the last full stop\n",
        "res = response[:last_full_stop_index + 1]\n",
        "\n",
        "# Print the generated response\n",
        "print(res)\n"
      ]
    }
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