Phi3FineTune.ipynb

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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/ushakrishnan/Phi-x-Samples/blob/main/Phi3FineTune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finetune Phi-3"
      ],
      "metadata": {
        "id": "ZmM0GYx7oISh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qtT2e2MIX8s",
        "outputId": "49eb5e8c-71cd-42b1-d6f4-170608cc3b99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.29.3-py3-none-any.whl (297 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting peft\n",
            "  Downloading peft-0.10.0-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.0)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.40.1-py3-none-any.whl (9.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.0/9.0 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting einops\n",
            "  Downloading einops-0.7.0-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting datasets\n",
            "  Downloading datasets-2.19.0-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement 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) (24.0)\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) (2.2.1+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) (0.4.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.3.1,>=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) (3.9.5)\n",
            "Collecting huggingface-hub (from accelerate)\n",
            "  Downloading huggingface_hub-0.22.2-py3-none-any.whl (388 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\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) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\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",
            "Installing collected packages: xxhash, 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, einops, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface-hub, nvidia-cusolver-cu12, transformers, datasets, bitsandbytes, accelerate, peft\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.40.0\n",
            "    Uninstalling transformers-4.40.0:\n",
            "      Successfully uninstalled transformers-4.40.0\n",
            "Successfully installed accelerate-0.29.3 bitsandbytes-0.43.1 datasets-2.19.0 dill-0.3.8 einops-0.7.0 huggingface-hub-0.22.2 multiprocess-0.70.16 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-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 transformers-4.40.1 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install -U accelerate peft transformers einops datasets bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip freeze | egrep \"accelerate|peft|transformers|einops|datasets|^torch=|bitsandbytes\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRzzaT8KIc-8",
        "outputId": "9e522df7-d0aa-4a98-8450-1cdadb8ba9ce"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accelerate==0.29.3\n",
            "bitsandbytes==0.43.1\n",
            "datasets==2.19.0\n",
            "einops==0.7.0\n",
            "peft==0.10.0\n",
            "tensorflow-datasets==4.9.4\n",
            "transformers==4.40.1\n",
            "vega-datasets==0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import set_seed\n",
        "\n",
        "set_seed(42)"
      ],
      "metadata": {
        "id": "JBn62Tr3IipX"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "# Load model\n",
        "modelpath = \"microsoft/Phi-3-mini-128k-instruct\"\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    modelpath,\n",
        "    device_map=\"auto\",\n",
        "    quantization_config=BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "    ),\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    # FA2 does not work yet\n",
        "    # attn_implementation=\"flash_attention_2\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603,
          "referenced_widgets": [
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            "c4519211299445098896f8904b483483",
            "b24c7c9ab8644411b71d40ca65c5d874",
            "7c783c7bd01e411885e43369550edbc6",
            "b1cb5b553a9e48c881b01703e52f47f8",
            "e79dd96e1bc74f19adff4a21dac21928",
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            "0dac14c2230b4745b342f4a19885ef84",
            "baa89f067ce1474692dc3af249a36a2a",
            "7e57d1be039e42ee93f762fcdf75e1cf",
            "b94f8e717eec47c195fa7d1c1c2e0d1c",
            "9f8bfec676a54a72934e0a066009750e",
            "34adc38b180f49c1adbcc1d1c9ff5b65",
            "19e3cf6270cf4cb691106e3bcf566010",
            "3c145cb875b94bcebde1bfc0eceb3bc6",
            "f3c40645c6f14c96ab43c4e08ec92fd2",
            "b34e943d47564bcc83cbf1c19041a8fa",
            "21f156f8613444c3a9751e15e71bb325",
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            "8924c966b4544fdd928b2aa7a1f03012",
            "a496b09bc29546c39d2b24b31ce4210d",
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            "697fd5154eb140ae9ee1c863ae7db42f",
            "4f538393d0ae4b65add3597ed5f198d4"
          ]
        },
        "id": "nq58R8OgImMe",
        "outputId": "c856a928-81db-41c4-ea04-3179e8653cb3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/3.35k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b20f78b0cd342f3983517b7a68bbb9d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for microsoft/Phi-3-mini-128k-instruct contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/microsoft/Phi-3-mini-128k-instruct.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_phi3.py:   0%|          | 0.00/10.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b94f8e717eec47c195fa7d1c1c2e0d1c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/Phi-3-mini-128k-instruct:\n",
            "- configuration_phi3.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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for microsoft/Phi-3-mini-128k-instruct contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/microsoft/Phi-3-mini-128k-instruct.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_phi3.py:   0%|          | 0.00/73.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "928349fb26fb41bc982ecd7c412aa73e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/Phi-3-mini-128k-instruct:\n",
            "- modeling_phi3.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",
            "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.f10fb29b79f038c78229ab4dcd9234a9666a770f.modeling_phi3:`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.\n",
            "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.f10fb29b79f038c78229ab4dcd9234a9666a770f.modeling_phi3:Current `flash-attenton` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/16.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "099213b70cb44400ad139b7838ceb44a"
            }
          },
          "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": "1cdd49d485b24df1a76e10bf438c1465"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.97G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c62e317f3ac44e75a59cbf308c553f6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/2.67G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f87797237664b6d86380e487c4c31af"
            }
          },
          "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": "0dc3127510e849c5b1af73fdf7beed18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/172 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45f329ccae714e9d85d926c6df1eeee0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMkW5asgIpzZ",
        "outputId": "ed439b93-f15d-4e27-c9c7-2d2226287128"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Phi3ForCausalLM(\n",
              "  (model): Phi3Model(\n",
              "    (embed_tokens): Embedding(32064, 3072, padding_idx=32000)\n",
              "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x Phi3DecoderLayer(\n",
              "        (self_attn): Phi3Attention(\n",
              "          (o_proj): Linear4bit(in_features=3072, out_features=3072, bias=False)\n",
              "          (qkv_proj): Linear4bit(in_features=3072, out_features=9216, bias=False)\n",
              "          (rotary_emb): Phi3SuScaledRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): Phi3MLP(\n",
              "          (gate_up_proj): Linear4bit(in_features=3072, out_features=16384, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=8192, out_features=3072, bias=False)\n",
              "          (activation_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): Phi3RMSNorm()\n",
              "        (resid_attn_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (post_attention_layernorm): Phi3RMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): Phi3RMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=3072, out_features=32064, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(modelpath, use_fast=False)    # fast tokenizer sometimes ignores the added tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
          "referenced_widgets": [
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            "ed1f0c442ee841fc89f4299b38d1d470",
            "e336e6c26cb04bf28896158fb85a0359",
            "5d2dc1158e57431fb8f1e79e22d2f666",
            "0dc525af065f464887d9d2db5be59108",
            "57182de81e444cfba44ffd2b3a4af268",
            "61ed04c2611b466f86ce82ed65208e42",
            "543a4be60f63431291f51494a7759439",
            "de387b66aeb243738c350edd93426afa",
            "65d1a141ccfc452caff2e585bd70a9a9",
            "7ba94ebde78943b4b75d7028a8706e14",
            "3e48c41e8ef24b30a45fe8ec1cae2611",
            "9273d37833b14592a5cec457a4f5abb8",
            "dcbb142396a74b84b330e89d5702a817",
            "9a925e31da114c1e9fc2ef855913abba",
            "59cb2a5b60a348beba0986c2bb6b7fd4",
            "d89a5fa1251940ca90ad982523cfd661"
          ]
        },
        "id": "9qjYUA4wIqnw",
        "outputId": "91c54c0e-226a-4f7d-8c62-ce5869a5ac86"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/3.28k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c96cee9e336461dbf419cc2eb204545"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4fa7c120cad34a5bba6709565eb869a3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05b6fa365385410b8bfc01b8d91f00db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/568 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f2b023923a940e5b3bbaefddd855ba4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61ed04c2611b466f86ce82ed65208e42"
            }
          },
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Q-Ev_D1It8f",
        "outputId": "3e6ae60d-30b2-4e0b-898e-cb47f722bf60"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32011"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.add_tokens([\"<|im_start|>\", \"<PAD>\"])\n",
        "tokenizer.pad_token = \"<PAD>\"\n",
        "tokenizer.add_special_tokens(dict(eos_token=\"<|im_end|>\"))\n",
        "model.config.eos_token_id = tokenizer.eos_token_id"
      ],
      "metadata": {
        "id": "m6iefx8cIwqO"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.model.embed_tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7ogt6AbI1DK",
        "outputId": "1ab52942-54b8-4e08-cdf9-c7ddc250a15a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ModulesToSaveWrapper(\n",
              "  (original_module): Embedding(32064, 3072, padding_idx=32000)\n",
              "  (modules_to_save): ModuleDict(\n",
              "    (default): Embedding(32064, 3072, padding_idx=32000)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model\n",
        "\n",
        "model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=32,\n",
        "    lora_alpha=32,\n",
        "    modules_to_save = [\"lm_head\", \"embed_tokens\"],\n",
        "    target_modules = [ \"o_proj\" , \"qkv_proj\" , \"gate_up_proj\", \"down_proj\" ],\n",
        "    lora_dropout=0.1,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = get_peft_model(model, lora_config)\n",
        "\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "id": "qcTGK0zSI10W"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"g-ronimo/riddles_evolved\")\n",
        "dataset = dataset[\"train\"].train_test_split(test_size=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "f06ee76ba41a403482f6420014cf77d8",
            "5b85e21e517a46bbb9f14d008bf4ffb3",
            "f67b41b32c9d4a69bb8b438b833d7821",
            "4febc4a858204b5bbc1c72ae9aa0439c",
            "32f7af6494734e53af5c75bcb5d0d3b8",
            "356f597bcf2f41b6beb10b905d95a734",
            "d4ad593b53174acca71a60143fa06b0a",
            "b0cbc89856a94e1d9bd17b2c8fee1c3e",
            "246b5abe7b13468793323ba5b21628fb",
            "5f681110a5a840dfb9ed578a36d1b2ae",
            "4c14b2849afe43c3bba197f20c87a3d6",
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            "4205de6c94d34582aa7aab609e2386e5",
            "00ecdc9b99be47c19d6fe120f6c50d58",
            "44e3186ad14d4fe88161285a60a474f7",
            "c1069eae8c0443d1b3ad405040c07a80",
            "4d3ecc6d9cba4084959eb5dc9c4ea75b",
            "244f32a7708542d88eb7c573f70f4646",
            "62c8cf6aedbb417ea3e808452af376b6",
            "0859aa856fd3411280583809abb117e9",
            "b9eb4cdd448440788e62958f85c54419",
            "c63abfa2c7524863874916f223728284",
            "dcadc93fa13d4fb79bf2d23246aab0cd",
            "3a6a7b86cda6422988f26cfc1857ba4c",
            "f948eb201ef045b4856b07f629d9bd85",
            "c89a75dbc2d04ac38fd01518a51e212a",
            "8eb23110f79b4ca7bd14d2848d3fdfc4",
            "ff140c1e60274ccc9e292c21d0dc452c",
            "b4ff71d75712401bb1999d8404e23971",
            "fd643b36c55740479ca1af8eaabf8b67",
            "615f0b13c0094ba7851dee4498ec0081",
            "c2682b62eb82483387688e0defb0b758",
            "842c55eb67464a1391a6699c35062392"
          ]
        },
        "id": "WASMWc0YI4eo",
        "outputId": "bf73caa3-92d1-4c4e-cbda-0fdb9f00d82f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/938 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f06ee76ba41a403482f6420014cf77d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.20M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3348e6f4062245298ac773bae6a4767a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1682 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcadc93fa13d4fb79bf2d23246aab0cd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tm7uW8MCI6t5",
        "outputId": "b91da828-1561-4ff5-b4f5-24eb25b1ef2e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['number', 'messages'],\n",
              "        num_rows: 1513\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['number', 'messages'],\n",
              "        num_rows: 169\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZJ2bQHWI8YA",
        "outputId": "927a1edb-bd7b-4aa3-bfb5-0fd95969c20a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'number': 1509,\n",
              " 'messages': [\"What starts with a 'C' is covered with keys, but we use only one. What is it?\",\n",
              "  'The answer to the riddle is a \"coconut.\" While a coconut is covered with many small \"keys\" or eyes, we typically open it with just one tool, such as a coconut crusher or a large nail.',\n",
              "  \"Cool, so what's that thing we eat that looks like a big brown ball and has lots of little bumps on it, but we only use one thing to open it? I guess it's not a basketball?\",\n",
              "  'I believe you\\'re referring to an artichoke. While an artichoke looks like a large brown ball with small bumps on it, we typically use a tool called a \"dip tool\" or a \"lemon tool\" with a sharp tip or a pair of scissors to remove the small, sharp thorns covering the artichoke\\'s bracts, allowing us to eat the tender heart inside.']}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from functools import partial\n",
        "\n",
        "# Format (chatML) and tokenize dataset\n",
        "templates=[\n",
        "    \"<|im_start|>assistant\\n{msg}<|im_end|>\",\n",
        "    \"<|im_start|>user\\n{msg}<|im_end|>\"\n",
        "]\n",
        "IGNORE_INDEX=-100\n",
        "\n",
        "def tokenize(input, max_length):\n",
        "    input_ids, attention_mask, labels = [], [], []\n",
        "\n",
        "    for i,msg in enumerate(input[\"messages\"]):\n",
        "        isHuman = i%2==0\n",
        "        msg_chatml=templates[isHuman].format(msg=msg)\n",
        "        msg_tokenized=tokenizer(msg_chatml, truncation=False, add_special_tokens=False)\n",
        "\n",
        "        input_ids+=msg_tokenized[\"input_ids\"]\n",
        "        attention_mask+=msg_tokenized[\"attention_mask\"]\n",
        "        labels+=[IGNORE_INDEX]*len(msg_tokenized[\"input_ids\"]) if isHuman else msg_tokenized[\"input_ids\"]\n",
        "\n",
        "    return {\n",
        "        \"input_ids\": input_ids[:max_length],\n",
        "        \"attention_mask\": attention_mask[:max_length],\n",
        "        \"labels\": labels[:max_length],\n",
        "    }\n",
        "\n",
        "dataset_tokenized = dataset.map(\n",
        "    partial(tokenize, max_length=1024), # max sample length 1024 tokens, enough for this dataset\n",
        "    batched=False,\n",
        "    num_proc=os.cpu_count(),    # multithreaded\n",
        "    remove_columns=dataset[\"train\"].column_names  # don't need this anymore, we have tokens from here on\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "e6cd3216e5634592975f2e7e8ca0b92f",
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            "fc1da4b5b49a4f049eee2aca350d7ada",
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            "e940257e568c4c69b7a056696accffa9",
            "7ea21be4c0f14337870a5d6fb7827f85",
            "9192672975374ee3a774c9b7f0e4239c",
            "eff883478736498293257bbf555961ba",
            "32fd6eafb1e84e199d9f244f0e4ba3f5",
            "a5f21b4857b740b99bb2919e8112496e",
            "5701c26298684f31ab72cedecaa7dc59",
            "b1e758f64d8c461496d605ce27c93f39",
            "9cb04e92218148c49e165ccbcf7c686a",
            "58b2d44ace63429fab0666a027e8bc4b",
            "f59eb965c3de41c2b121a768a49e3d49",
            "ad074b597f2f4aa29d94016c1e940d48",
            "11df35a981904550a73a5aab09f67662"
          ]
        },
        "id": "UhS5wKc_JAk4",
        "outputId": "26dc3958-a898-45eb-80d7-12dd5d177a1f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/1513 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6cd3216e5634592975f2e7e8ca0b92f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/169 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9192672975374ee3a774c9b7f0e4239c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample size distribution\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data = [len(tok) for tok in (dataset_tokenized[\"train\"][\"input_ids\"]+dataset_tokenized[\"test\"][\"input_ids\"])]\n",
        "print(f\"longest sample: {max(data)} tokens\")\n",
        "\n",
        "plt.hist(data, bins=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "T0y_zjqGJC6X",
        "outputId": "dbdb899f-0777-4f9f-f91d-089964324628"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "longest sample: 1024 tokens\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# collate function - to transform list of dictionaries [ {input_ids: [123, ..]}, {.. ] to single batch dictionary { input_ids: [..], labels: [..], attention_mask: [..] }\n",
        "def collate(elements):\n",
        "    tokens=[e[\"input_ids\"] for e in elements]\n",
        "    tokens_maxlen=max([len(t) for t in tokens])\n",
        "\n",
        "    for i,sample in enumerate(elements):\n",
        "        input_ids=sample[\"input_ids\"]\n",
        "        labels=sample[\"labels\"]\n",
        "        attention_mask=sample[\"attention_mask\"]\n",
        "\n",
        "        pad_len=tokens_maxlen-len(input_ids)\n",
        "\n",
        "        input_ids.extend( pad_len * [tokenizer.pad_token_id] )\n",
        "        labels.extend( pad_len * [IGNORE_INDEX] )\n",
        "        attention_mask.extend( pad_len * [0] )\n",
        "\n",
        "    batch={\n",
        "        \"input_ids\": torch.tensor( [e[\"input_ids\"] for e in elements] ),\n",
        "        \"labels\": torch.tensor( [e[\"labels\"] for e in elements] ),\n",
        "        \"attention_mask\": torch.tensor( [e[\"attention_mask\"] for e in elements] ),\n",
        "    }\n",
        "\n",
        "    return batch"
      ],
      "metadata": {
        "id": "WBSacRqtJDrk"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --force-reinstall -q torch torchvision"
      ],
      "metadata": {
        "id": "RlqkPRwVPLaw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "print('Device:', torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fyMyePMSPQDA",
        "outputId": "665eea8a-1333-41a3-ff73-df61ec156bf5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "# dataset-specific parameters\n",
        "bs=1     # batch size for training\n",
        "bs_eval=16    # batch size for evaluation\n",
        "ga_steps=16  # gradient accumulation steps\n",
        "lr=0.00002  # learning rate\n",
        "epochs=20\n",
        "\n",
        "steps_per_epoch=len(dataset_tokenized[\"train\"])//(bs*ga_steps)\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"out\",\n",
        "    per_device_train_batch_size=bs,\n",
        "    per_device_eval_batch_size=bs_eval,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    logging_steps=1,\n",
        "    eval_steps=steps_per_epoch//2,    # 2 evals per epoch\n",
        "    save_steps=steps_per_epoch,     # save once per epoch\n",
        "    gradient_accumulation_steps=ga_steps,\n",
        "    num_train_epochs=epochs,\n",
        "    lr_scheduler_type=\"constant\",\n",
        "    optim=\"paged_adamw_32bit\",      # val_loss will go nan with paged_adamw_8bit\n",
        "    learning_rate=lr,\n",
        "    group_by_length=False,\n",
        "    fp16=True,\n",
        "    ddp_find_unused_parameters=False,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    args=args,\n",
        "    data_collator=collate,\n",
        "    train_dataset=dataset_tokenized[\"train\"],\n",
        "    eval_dataset=dataset_tokenized[\"test\"],\n",
        ")"
      ],
      "metadata": {
        "id": "bnuM_Z3dJGio"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SawvU9A_QX_r",
        "outputId": "eab512b6-5b84-48df-880d-d2bf13ab7110"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading sentry_sdk-2.0.0-py2.py3-none-any.whl (266 kB)\n",
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            "\u001b[?25hCollecting docker-pycreds>=0.4.0 (from wandb)\n",
            "  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n",
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            "Collecting setproctitle (from wandb)\n",
            "  Downloading setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2024.2.2)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb)\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, GitPython, wandb\n",
            "Successfully installed GitPython-3.1.43 docker-pycreds-0.4.0 gitdb-4.0.11 sentry-sdk-2.0.0 setproctitle-1.3.3 smmap-5.0.1 wandb-0.16.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# log to a specific wandb project\n",
        "import wandb\n",
        "run = wandb.init(\n",
        "    project=\"phi3\",\n",
        "    name=\"testrun\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "CQ1-Y5fiJKsM",
        "outputId": "e7921b4c-30a4-45b9-d7a8-ee1bdb314482"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.16.6"
            ]
          },
          "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-20240426_021330-hyhv20vw</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/andavan/phi3/runs/hyhv20vw' target=\"_blank\">testrun</a></strong> to <a href='https://wandb.ai/andavan/phi3' 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/andavan/phi3' target=\"_blank\">https://wandb.ai/andavan/phi3</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/andavan/phi3/runs/hyhv20vw' target=\"_blank\">https://wandb.ai/andavan/phi3/runs/hyhv20vw</a>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Ls8lMahcJLMf",
        "outputId": "9a501be4-78f7-4223-b7fc-9db403fb0b23"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.f10fb29b79f038c78229ab4dcd9234a9666a770f.modeling_phi3:You are not running the flash-attention implementation, expect numerical differences.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='41' max='1880' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [  41/1880 16:49 < 13:13:33, 0.04 it/s, Epoch 0.42/20]\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-32-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\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   1857\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   1858\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-> 1859\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1860\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   1861\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   2201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2202\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-> 2203\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   2204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2205\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   3145\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   3146\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-> 3147\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   3148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3149\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   2009\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2010\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-> 2011\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   2012\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   2013\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    520\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    521\u001b[0m             )\n\u001b[0;32m--> 522\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    523\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    524\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    264\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    265\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--> 266\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    267\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    268\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "# base model\n",
        "base_path=\"microsoft/Phi-3-mini-128k-instruct\"\n",
        "\n",
        "# adapters: path to folder with adapter_model.safetensors\n",
        "adapter_path=\"out/checkpoint-1880\"\n",
        "\n",
        "# where to save merged model\n",
        "save_to=\"trained_model\"\n",
        "\n",
        "# Load model and tokenizer\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_path,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_path)\n",
        "\n",
        "# Add/set tokens same tokens to base model before merging, like we did before training\n",
        "tokenizer.add_tokens([\"<|im_start|>\", \"<PAD>\"])\n",
        "tokenizer.pad_token = \"<PAD>\"\n",
        "tokenizer.add_special_tokens(dict(eos_token=\"<|im_end|>\"))\n",
        "\n",
        "# Add ChatML template to tokenizer\n",
        "tokenizer.chat_template=\"{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}\"\n",
        "\n",
        "base_model.config.eos_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Set a default Generation configuration: Llama precise\n",
        "generation_config = GenerationConfig(\n",
        "    max_new_tokens=100,\n",
        "    temperature=0.7,\n",
        "    top_p=0.1,\n",
        "    top_k=40,\n",
        "    repetition_penalty=1.18,\n",
        "    do_sample=True,\n",
        "    pad_token_id=tokenizer.pad_token_id,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "# Load LoRA and merge\n",
        "model = PeftModel.from_pretrained(base_model, adapter_path)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "model.save_pretrained(save_to, safe_serialization=True, max_shard_size='4GB')\n",
        "tokenizer.save_pretrained(save_to)\n",
        "generation_config.save_pretrained(save_to)"
      ],
      "metadata": {
        "id": "bO8nkHjcJM3y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825,
          "referenced_widgets": [
            "d98ed142bea344c09c41dd255ba7dd25",
            "f84711f77d9e4c4dafdaa649a054ffe9",
            "57d3a8bf0cbf450188e3b653ce44385e",
            "43104bc7fbbf4d3684b0354447546f4b",
            "8a508a2540724a5294cdb43172729703",
            "b4ba867ff61d4faaacf4720537994568",
            "ba614dd2d79a4e70b721f1ba9319bce9",
            "38e29070ef7c421a991d7a0baa4e4fb6",
            "d920bce6764445cf95b9d07e37e56da7",
            "41b0205c36e54ce19d7941cc37a8087d",
            "be5f8ee777694137bde03ebcba075e4a"
          ]
        },
        "outputId": "ec19235e-20b1-4179-b871-5dfcf5177d2e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for microsoft/Phi-3-mini-128k-instruct contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/microsoft/Phi-3-mini-128k-instruct.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n",
            "The repository for microsoft/Phi-3-mini-128k-instruct contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/microsoft/Phi-3-mini-128k-instruct.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.f10fb29b79f038c78229ab4dcd9234a9666a770f.modeling_phi3:`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.\n",
            "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.f10fb29b79f038c78229ab4dcd9234a9666a770f.modeling_phi3:Current `flash-attenton` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d98ed142bea344c09c41dd255ba7dd25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Can't find 'adapter_config.json' at 'out/checkpoint-1880'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\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[1;32m    305\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\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/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\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   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/out/checkpoint-1880/resolve/main/adapter_config.json",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/config.py\u001b[0m in \u001b[0;36m_get_peft_type\u001b[0;34m(cls, model_id, **hf_hub_download_kwargs)\u001b[0m\n\u001b[1;32m    196\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m                 config_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    198\u001b[0m                     \u001b[0mmodel_id\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/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, headers, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m   1402\u001b[0m             \u001b[0;31m# Repo not found or gated => let's raise the actual error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1403\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mhead_call_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1404\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/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, headers, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m   1260\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1261\u001b[0;31m                 metadata = get_hf_file_metadata(\n\u001b[0m\u001b[1;32m   1262\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\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/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout, library_name, library_version, user_agent, headers)\u001b[0m\n\u001b[1;32m   1673\u001b[0m     \u001b[0;31m# Retrieve metadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1674\u001b[0;31m     r = _request_wrapper(\n\u001b[0m\u001b[1;32m   1675\u001b[0m         \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"HEAD\"\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/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    368\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfollow_relative_redirects\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 369\u001b[0;31m         response = _request_wrapper(\n\u001b[0m\u001b[1;32m    370\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\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/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m     \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\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    394\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\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/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    351\u001b[0m             )\n\u001b[0;32m--> 352\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRepositoryNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-662b122f-688d6b8c3a0934237c2f19d0;69e47a0e-f8e7-4839-a61d-f7d358859d04)\n\nRepository Not Found for url: https://huggingface.co/out/checkpoint-1880/resolve/main/adapter_config.json.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-9f83dbd379ed>\u001b[0m in \u001b[0;36m<cell line: 45>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;31m# Load LoRA and merge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPeftModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_path\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     46\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge_and_unload\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     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, model, model_id, adapter_name, is_trainable, config, **kwargs)\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;32mis\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[1;32m    327\u001b[0m             config = PEFT_TYPE_TO_CONFIG_MAPPING[\n\u001b[0;32m--> 328\u001b[0;31m                 PeftConfig._get_peft_type(\n\u001b[0m\u001b[1;32m    329\u001b[0m                     \u001b[0mmodel_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                     \u001b[0msubfolder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"subfolder\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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/usr/local/lib/python3.10/dist-packages/peft/config.py\u001b[0m in \u001b[0;36m_get_peft_type\u001b[0;34m(cls, model_id, **hf_hub_download_kwargs)\u001b[0m\n\u001b[1;32m    201\u001b[0m                 )\n\u001b[1;32m    202\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Can't find '{CONFIG_NAME}' at '{model_id}'\"\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    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mloaded_attributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_json_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Can't find 'adapter_config.json' at 'out/checkpoint-1880'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "model_path=\"trained_model\"\n",
        "question=\"Hello, who are you?\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_path,\n",
        "        torch_dtype=torch.bfloat16,\n",
        "        device_map=\"auto\"\n",
        "    )\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "\n",
        "messages=[\n",
        "    {\"role\": \"user\", \"content\": question}\n",
        "]\n",
        "\n",
        "input_tokens = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    add_generation_prompt=True,\n",
        "    return_tensors=\"pt\"\n",
        ").to(\"cuda\")\n",
        "\n",
        "output_tokens = model.generate(input_tokens)\n",
        "output = tokenizer.decode(\n",
        "    output_tokens[0][len(input_tokens[0]):],\n",
        "    skip_special_tokens=True\n",
        "    )\n",
        "\n",
        "print(output)"
      ],
      "metadata": {
        "id": "8IkrJxWLJV-i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from transformers import\n",
        "\n",
        "# Load model and tokenizer\n",
        "modelpath=\"trained_model\"\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    modelpath,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.bfloat16,\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(modelpath)\n",
        "\n",
        "# Upload\n",
        "model.push_to_hub(\"andavan/phi-3_riddles-evolved\")\n",
        "tokenizer.push_to_hub(\"andavan/phi-3_riddles-evolved\")"
      ],
      "metadata": {
        "id": "0COvfx12JWti"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}