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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/fine_tune_gemma_2b_qlora_codeAlpaca.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune GEMMA 2B on codeAlpaca using QLoRA\n",
        "\n",
        "> Created by [@mrm8488](http://twitter.com/mrm8488)\n"
      ],
      "metadata": {
        "id": "Px7RJX51wqc_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OHfZ3jXgbLw9",
        "outputId": "b92f34f0-70ca-4b99-b95b-6b24f571826b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Feb 21 22:22:02 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   63C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install required dependencies"
      ],
      "metadata": {
        "id": "qBdnT4e8xLRQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install \"torch>=2.1.1\" -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0nCDrhplRki",
        "outputId": "8cf88aa0-7484-4e53-8fc6-b7a91cfdbfc5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torch>=2.1.1\n",
            "  Downloading torch-2.2.0-cp310-cp310-manylinux1_x86_64.whl (755.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m755.5/755.5 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.1) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.1) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.1) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.1) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.1) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.1) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m71.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m72.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m101.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch>=2.1.1)\n",
            "  Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m29.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=2.1.1)\n",
            "  Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nccl-cu12==2.19.3 (from torch>=2.1.1)\n",
            "  Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch>=2.1.1)\n",
            "  Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting triton==2.2.0 (from torch>=2.1.1)\n",
            "  Downloading triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m167.9/167.9 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=2.1.1)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl (20.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m93.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2.1.1) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2.1.1) (1.3.0)\n",
            "Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-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, torch\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.1.0\n",
            "    Uninstalling triton-2.1.0:\n",
            "      Successfully uninstalled triton-2.1.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu121\n",
            "    Uninstalling torch-2.1.0+cu121:\n",
            "      Successfully uninstalled torch-2.1.0+cu121\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",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.3.101 nvidia-nvtx-cu12-12.1.105 torch-2.2.0 triton-2.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "\n",
        "! pip install -U \"transformers==4.38.0\" --upgrade\n",
        "! pip install git+https://github.com/huggingface/trl\n",
        "! pip install peft\n",
        "! pip install -U wandb\n",
        "! pip install accelerate\n",
        "! pip intall datasets\n",
        "! pip install bitsandbytes"
      ],
      "metadata": {
        "id": "ue-VCSTgbU2W"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from dataclasses import dataclass, field\n",
        "from typing import Optional\n",
        "import numpy as np\n",
        "\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    TrainingArguments,\n",
        "    Trainer\n",
        ")\n",
        "import wandb\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "7847SKm1bq64"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Log in HF to push our model and WandB for tracking"
      ],
      "metadata": {
        "id": "KNNUriITxR_f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "f875469048624d58b2708da223c81f63",
            "13dd87805b454f47946a9b86f9dc9b99",
            "f6f1d978053943e68db874e714b2cebb",
            "d30ba80b4c914b578db8d24e78a57cec",
            "9b34506174374030bb591d4f00d177eb",
            "b520ca34521b4d10a811d06fac325f0a",
            "724e6e0072a74ef99003497b2435ff78",
            "aead2e2e37b245ee97ddb54a70dabc2c",
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            "103f33b8fe394c3f8bf9225dfc004add",
            "b32e3c80eaea456897d961579801a305",
            "67c86a746e4c4cd4aee7dc5318d35719",
            "ca12aad49ba14cdc806ad6a4def80b6c",
            "5134f45708e046f291bda403ab42cff4",
            "0b444494030a4014acb802b804b5816d",
            "4849b441fe504fb4b0dbaa76c744205c",
            "af8f1eed7270426fa56abf5c1ce16fdc"
          ]
        },
        "id": "LuyRZVq1b7Re",
        "outputId": "b2e94fbe-6336-49fc-bf6f-65199c11e7b3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f875469048624d58b2708da223c81f63"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.login()\n",
        "\n",
        "wandb_project = \"gemma-2b-ft-codealpaca\"\n",
        "if len(wandb_project) > 0:\n",
        "    os.environ[\"WANDB_PROJECT\"] = wandb_project"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "_Cjkvwl1cjxs",
        "outputId": "cf829ebc-cb36-4013-ad5e-0056fb74df98"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load and format the dataset"
      ],
      "metadata": {
        "id": "sT01Ra_zxkdQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ds_id = \"HuggingFaceH4/CodeAlpaca_20K\"\n",
        "\n",
        "ds = load_dataset(ds_id)"
      ],
      "metadata": {
        "id": "UONY7MawcsC8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "dc0cb268933c4ab0a2aae07c9c4142d4",
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            "4c3d0e8d5d054d8b9ef6ded8c209e178",
            "88def98df12541d79d5be449257b5e18",
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            "b7fcb9451eac413798ea6aa080fb34db",
            "1861797e316b4436b7c87c55c0162468",
            "70cbbd9d97844137928be8edc632fe1e",
            "def8e91cd2b7474c85387ebb48132a56",
            "6a5e96cd8a6448879273254057b9ac5c",
            "406e0a4846594afdaff5a386aeef254c",
            "96d6e2c89f104e2a88e3027dfc155ab6",
            "30827be368584c3c9246f8f74145bd81",
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            "25a2acd559d04d638ef170aa4bde88bd",
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            "93fd029f754d449780c5a41694d863f6"
          ]
        },
        "outputId": "da5818c8-c52e-4e16-ac4b-087f1af2a940"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/195 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc0cb268933c4ab0a2aae07c9c4142d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/756 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1861797e316b4436b7c87c55c0162468"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.01M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98880f5e7bfa4042beb63dbc4278c20b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/336k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74ab464164de4761bc254678f93758a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/18019 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90b70b3e64cb405ebc94a5f8c05526f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2003 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e4c39bd641347ed976acf50a06d8a7a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0xMGcQlcscW",
        "outputId": "ae9e17e8-0e40-41df-b7ab-51adfc36cca3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['prompt', 'completion'],\n",
              "        num_rows: 18019\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['prompt', 'completion'],\n",
              "        num_rows: 2003\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = ds['train']\n",
        "test_ds = ds['test']"
      ],
      "metadata": {
        "id": "kBLS8Ty8cuSo"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = \"google/gemma-2b\""
      ],
      "metadata": {
        "id": "ZY4_EG_Jc8V4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "id": "swrGpNgVdNAQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "6f033be817184cc5bd667f3689171168",
            "911ae05445a841d09ef472b56b95df0c",
            "769c9bd76fd94140ad9408b756654b7c",
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            "451a6685042e44b698b765803e70b3a8",
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            "86289115d10c4371a776c2a7d06931bb",
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            "c4675d3e92084f3ea0469abdf2216aba",
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            "9f2495310f594b5fafafd15356292284",
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          ]
        },
        "outputId": "21baaf66-d65a-4c8b-adb4-48b69ab90321"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f033be817184cc5bd667f3689171168"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86289115d10c4371a776c2a7d06931bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf8c656dc6b544cea8159166a26ebad0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/555 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c10a98feaac420bbba603edc15471c3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.special_tokens_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnYHww5iddKY",
        "outputId": "f3b7dcd9-02aa-48d4-d9c6-bf08806fded0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bos_token': '<bos>',\n",
              " 'eos_token': '<eos>',\n",
              " 'unk_token': '<unk>',\n",
              " 'pad_token': '<pad>'}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oj5w4lKdjO6",
        "outputId": "e50e91b6-56e0-40dc-c629-b3639e78d519"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'prompt': 'Create a Java class which sorts the given array of numbers.\\n[9, 2, 4, 3, 6, 1]',\n",
              " 'completion': 'class ArraySort { \\n  \\n    void sort(int arr[]) { \\n        int n = arr.length; \\n  \\n        // One by one move boundary of unsorted subarray \\n        for (int i = 0; i < n-1; i++) { \\n            \\n            // Find the minimum element in unsorted array \\n            int min_index = i; \\n            for (int j = i+1; j < n; j++) \\n                if (arr[j] < arr[min_index]) \\n                    min_index = j; \\n  \\n            // Swap the found minimum element with the first element \\n            int temp = arr[min_index]; \\n            arr[min_index] = arr[i]; \\n            arr[i] = temp; \\n        } \\n    } \\n  \\n    // Prints the array \\n    void printArray(int arr[]) { \\n        int n = arr.length; \\n        for (int i=0; i<n; ++i) \\n            System.out.print(arr[i] + \" \"); \\n        System.out.println(); \\n    } \\n  \\n    // Driver code to test above \\n    public static void main(String args[]) \\n    { \\n        ArraySort ob = new ArraySort(); \\n        int arr[] = {9, 2, 4, 3, 6, 1}; \\n        ob.sort(arr); \\n        System.out.println(\"Sorted array\"); \\n        ob.printArray(arr); \\n    } \\n}'}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_ds(ex):\n",
        "  prompt = ex['prompt']\n",
        "  completion = ex['completion']\n",
        "  system = f\"<bos><|system|>\\nYou are a helpful coding assistant.<eos>\\n\"\n",
        "  text = f\"{system}<|user|>\\n{prompt}<eos>\\n<|assistant|>\\n{completion}<eos>\"\n",
        "  return {'text': text}"
      ],
      "metadata": {
        "id": "uh97XWZmdvR_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(format_ds(train_ds[0])['text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfDO9zc9edFB",
        "outputId": "b91bb472-4a69-4673-a0ea-225f124034c9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bos><|system|>\n",
            "You are a helpful coding assistant.<eos>\n",
            "<|user|>\n",
            "Create a Java class which sorts the given array of numbers.\n",
            "[9, 2, 4, 3, 6, 1]<eos>\n",
            "<|assistant|>\n",
            "class ArraySort { \n",
            "  \n",
            "    void sort(int arr[]) { \n",
            "        int n = arr.length; \n",
            "  \n",
            "        // One by one move boundary of unsorted subarray \n",
            "        for (int i = 0; i < n-1; i++) { \n",
            "            \n",
            "            // Find the minimum element in unsorted array \n",
            "            int min_index = i; \n",
            "            for (int j = i+1; j < n; j++) \n",
            "                if (arr[j] < arr[min_index]) \n",
            "                    min_index = j; \n",
            "  \n",
            "            // Swap the found minimum element with the first element \n",
            "            int temp = arr[min_index]; \n",
            "            arr[min_index] = arr[i]; \n",
            "            arr[i] = temp; \n",
            "        } \n",
            "    } \n",
            "  \n",
            "    // Prints the array \n",
            "    void printArray(int arr[]) { \n",
            "        int n = arr.length; \n",
            "        for (int i=0; i<n; ++i) \n",
            "            System.out.print(arr[i] + \" \"); \n",
            "        System.out.println(); \n",
            "    } \n",
            "  \n",
            "    // Driver code to test above \n",
            "    public static void main(String args[]) \n",
            "    { \n",
            "        ArraySort ob = new ArraySort(); \n",
            "        int arr[] = {9, 2, 4, 3, 6, 1}; \n",
            "        ob.sort(arr); \n",
            "        System.out.println(\"Sorted array\"); \n",
            "        ob.printArray(arr); \n",
            "    } \n",
            "}<eos>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IpVdfl3OegYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IX__X-YfS9pR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bd361eeab1054f539ea8396cf852c967",
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            "d6d7181199794b29a48ebc06349b2c95",
            "eb64ddfdd39e42098d500e1620e1303c"
          ]
        },
        "outputId": "df91fb8b-7d86-4edf-b7a3-72b56ac50c3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/18019 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd361eeab1054f539ea8396cf852c967"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_ds = train_ds.map(format_ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "PkDDl1SpTCdQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6aa62e2a8fb54512b925b5276204e48d",
            "4c7cf45113194058a2f28597b9ef2028",
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            "db3d17479d8d4f498af9b88802732118",
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            "09cc2dafc2e04296843dd68919a6c399",
            "15c3ccd30d57497d914c2798f4e138a5"
          ]
        },
        "outputId": "2195add0-379a-41fd-a9a9-20bf5f025c8d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2003 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6aa62e2a8fb54512b925b5276204e48d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_ds = test_ds.map(format_ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Examine the distribution of the lengths of the examples"
      ],
      "metadata": {
        "id": "4spv30ZMxq-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming token_lengths contains the lengths of tokens from your dataset\n",
        "token_lengths = [len(tokenizer.encode(ex['text'])) for ex in train_ds]\n",
        "\n",
        "# Plotting the histogram\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(token_lengths, bins=50, color='blue', alpha=0.7)\n",
        "plt.title('Token Length Distribution')\n",
        "plt.xlabel('Token Length')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Uep4HITje66o",
        "outputId": "cdaad279-ef98-4fdd-a2e1-16849005b50c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "[encoded data, 26324 characters]\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set the QLoRA params and Load the model"
      ],
      "metadata": {
        "id": "fBF0PQMBx-ij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=torch.float16,\n",
        "        bnb_4bit_use_double_quant=False,\n",
        "    )"
      ],
      "metadata": {
        "id": "RhcY7hr8fARG"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_id,\n",
        "        quantization_config = bnb_config\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258,
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        },
        "id": "q3MAxNNYfUWG",
        "outputId": "efd8de6d-ec06-4c3c-f9d7-9f5bcce0256a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/627 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a73a11591f084fe98f2be0e3987c58ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`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%|          | 0.00/13.5k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ef8ee555c8f479b801a65d90d7bb0b3"
            }
          },
          "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": "032723f4e48d40df877082387c0e3588"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75168933df7a40c0a83841320adb456e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "193049e1f68748878e025170a9d5a9c6"
            }
          },
          "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": "0cf009c32aa14079ac6f61f4c830acb5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69226de62e394b15b56c21ec49d53df0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IiQcbXdmgCt_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "    )"
      ],
      "metadata": {
        "id": "8FB19TNTJ5b5"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=32,\n",
        "    lora_alpha=64,\n",
        "    target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj'],\n",
        "    bias=\"none\",\n",
        "    lora_dropout=0.05,\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ],
      "metadata": {
        "id": "oHxe4913J5b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32305648-efd6-4c43-9139-d0e1117722a1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 7372800 || all params: 1522640896 || trainable%: 0.4842113474929285\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W4aMc0TRge0-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set the training arguments"
      ],
      "metadata": {
        "id": "0BClWQ79yLRm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MICRO_BATCH_SIZE = 2\n",
        "BATCH_SIZE = 8\n",
        "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE"
      ],
      "metadata": {
        "id": "48HNpsCwKGVi"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "        output_dir=\"gemma-2b-ft-codeAlpaca\",\n",
        "        push_to_hub=True,\n",
        "        hub_private_repo=True,\n",
        "        hub_model_id=\"mrm8488/gemma-2b-ft-codeAlpaca\",\n",
        "        per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
        "        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
        "        num_train_epochs=2,\n",
        "        learning_rate=2.5e-5,\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        logging_steps=5,\n",
        "        seed=66,\n",
        "        load_best_model_at_end=True,\n",
        "        save_strategy=\"steps\",\n",
        "        save_steps=50,\n",
        "        evaluation_strategy=\"steps\",\n",
        "        eval_steps=50,\n",
        "        save_total_limit=2,\n",
        "        report_to=\"tensorboard\", # set to wandb if you wish\n",
        "        remove_unused_columns=True,\n",
        "        fp16=True,\n",
        "        bf16=False\n",
        ")"
      ],
      "metadata": {
        "id": "DhKdB8XCKI5i"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hDRrEYthg4_d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set the trainer params"
      ],
      "metadata": {
        "id": "yO4otntEynIj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    peft_config=config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=512,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_args,\n",
        "    dataset_kwargs={\n",
        "        \"add_special_tokens\": False,  # We template with special tokens\n",
        "        \"append_concat_token\": False, # No need to add additional separator token\n",
        "    },\n",
        "    packing=True\n",
        ")"
      ],
      "metadata": {
        "id": "1FFhSno62QET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
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        },
        "outputId": "5bbf9ce6-ada9-4006-edd5-1bfbe43aff01"
      },
      "execution_count": 24,
      "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": "0295613a333541b0a1e8f63b1f39009f"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:294: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Launch Tensorboard"
      ],
      "metadata": {
        "id": "A7rUSc2YyrCI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard"
      ],
      "metadata": {
        "id": "YbrZXbIxKI5n"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
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      "source": [
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      ],
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              "headers": [
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                  "content-type",
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              "status_text": ""
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            "https://localhost:6006/data/plugin/scalars/scalars?run=Feb21_22-28-59_7b4cb4c51ef5&tag=train%2Fepoch": {
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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/json"
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              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Feb21_22-28-59_7b4cb4c51ef5&tag=train%2Fgrad_norm": {
              "data": "W1sxNzA4NTU0NTYzLjEyMjMyNCwgNSwgMi45MjU4Nzc1NzExMDU5NTddXQ==",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
                ]
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              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Feb21_22-28-59_7b4cb4c51ef5&tag=train%2Floss": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/json"
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              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/scalars/scalars?run=Feb21_22-28-59_7b4cb4c51ef5&tag=train%2Flearning_rate": {
              "data": "W1sxNzA4NTU0NTYzLjEyMjM0NywgNSwgMi40ODgwNDk3MjIxNjAyMTI3ZS0wNV1d",
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                [
                  "content-type",
                  "application/json"
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              "status": 200,
              "status_text": ""
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          },
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "v-JPv8sJbmLj",
        "outputId": "2978bd3c-3aa2-49dc-bf6a-5f0b323238aa"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "stfIo1BAbu6_",
        "outputId": "d16ea074-5073-44ac-86bc-b5b7557a41a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='1046' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [  25/1046 01:07 < 49:59, 0.34 it/s, Epoch 0.05/2]\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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Push the model (adapter) to the hub"
      ],
      "metadata": {
        "id": "RASrsAppzAG1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "id": "zsTZr2tSjeCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vOwB8ymnmzMS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}