PolyLM_13B_Alpaca_Clean_Dutch_Qlora.ipynb

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        "<a href=\"https://colab.research.google.com/github/RobinSmits/Dutch-LLMs/blob/main/PolyLM_13B_Alpaca_Clean_Dutch_Qlora.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
        "colab": {
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        },
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        "outputId": "824d1d8e-d18d-4621-ef0c-c09642c0d47b"
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      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Mount Google Drive\n",
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Set Folder to use...\n",
        "WORK_DIR = '/content/drive/My Drive/LLM/PolyLM13BAlpacaCleanDutchQlora/'\n",
        "os.makedirs(WORK_DIR, exist_ok = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x-TtD0uIHxUt"
      },
      "outputs": [],
      "source": [
        "# Install required packages\n",
        "!pip install -q accelerate\n",
        "!pip install -q sentencepiece\n",
        "!pip install -q bitsandbytes\n",
        "!pip install -q transformers\n",
        "!pip install -q peft\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7R3VLs6PHZQX"
      },
      "outputs": [],
      "source": [
        "# Import Modules\n",
        "import bitsandbytes as bnb\n",
        "from datasets import load_dataset, load_from_disk\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model, TaskType\n",
        "from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GenerationConfig\n",
        "import torch\n",
        "import transformers"
      ]
    },
    {
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        "id": "J_CWF0Jxpv01",
        "outputId": "9fc3d9cf-56eb-41b3-8cb7-885200da0775"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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      ],
      "source": [
        "# Huggingface Hub Loging\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uhk1Rkk_gNCK"
      },
      "outputs": [],
      "source": [
        "# Set Name Constants\n",
        "model_name = 'DAMO-NLP-MT/polylm-13b-fine-grained-shards'\n",
        "hf_model_name = 'polylm_13b_ft_alpaca_clean_dutch'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/"
        },
        "id": "6Zu2NDXPERde",
        "outputId": "aa8c0510-75f0-48dc-c153-29610017f362"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using sep_token, but it is not set yet.\n",
            "Using cls_token, but it is not set yet.\n",
            "Using mask_token, but it is not set yet.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaTokenizer(name_or_path='DAMO-NLP-MT/polylm-13b-fine-grained-shards', vocab_size=256000, model_max_length=2048, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '</s>'}, clean_up_tokenization_spaces=False),  added_tokens_decoder={\n",
            "\t0: AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
            "\t1: AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
            "\t2: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
            "}\n",
            "pad_token_id: 2\n",
            "bos_token_id: 1\n",
            "eos_token_id: 2\n",
            "pad_token_id: </s>\n",
            "bos_token_id: <s>\n",
            "eos_token_id: </s>\n"
          ]
        }
      ],
      "source": [
        "# Create Tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name,\n",
        "                                          use_fast = False,\n",
        "                                          legacy = False)\n",
        "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Max Length\n",
        "MAX_LEN = 512\n",
        "\n",
        "# Tokenizer Summary\n",
        "print(tokenizer)\n",
        "\n",
        "# Special Tokens\n",
        "print(f'pad_token_id: {tokenizer.pad_token_id}')\n",
        "print(f'bos_token_id: {tokenizer.bos_token_id}')\n",
        "print(f'eos_token_id: {tokenizer.eos_token_id}')\n",
        "print(f'pad_token_id: {tokenizer.decode(tokenizer.pad_token_id)}')\n",
        "print(f'bos_token_id: {tokenizer.decode(tokenizer.bos_token_id)}')\n",
        "print(f'eos_token_id: {tokenizer.decode(tokenizer.eos_token_id)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZVrimoUDoEd",
        "outputId": "54318465-3c91-4da4-9050-8674e1b8ac92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPT2Config {\n",
            "  \"_name_or_path\": \"DAMO-NLP-MT/polylm-13b-fine-grained-shards\",\n",
            "  \"activation_function\": \"gelu_fast\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.0,\n",
            "  \"bos_token_id\": 255999,\n",
            "  \"embd_pdrop\": 0.0,\n",
            "  \"eos_token_id\": 255999,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_embd\": 5120,\n",
            "  \"n_head\": 40,\n",
            "  \"n_inner\": 20480,\n",
            "  \"n_layer\": 40,\n",
            "  \"n_positions\": 2048,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.0,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.0,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"tokenizer_class\": \"AutoTokenizer\",\n",
            "  \"transformers_version\": \"4.34.0\",\n",
            "  \"use_cache\": false,\n",
            "  \"vocab_size\": 256000\n",
            "}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Create Config\n",
        "config = AutoConfig.from_pretrained(model_name,\n",
        "                                    use_cache = False)\n",
        "\n",
        "# Summary\n",
        "print(config)"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
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          "output_type": "stream",
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          "text": [
            "GPT2LMHeadModel(\n",
            "  (transformer): GPT2Model(\n",
            "    (wte): Embedding(256000, 5120)\n",
            "    (wpe): Embedding(2048, 5120)\n",
            "    (drop): Dropout(p=0.0, inplace=False)\n",
            "    (h): ModuleList(\n",
            "      (0-39): 40 x GPT2Block(\n",
            "        (ln_1): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)\n",
            "        (attn): GPT2Attention(\n",
            "          (c_attn): Linear4bit(in_features=5120, out_features=15360, bias=True)\n",
            "          (c_proj): Linear4bit(in_features=5120, out_features=5120, bias=True)\n",
            "          (attn_dropout): Dropout(p=0.0, inplace=False)\n",
            "          (resid_dropout): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (ln_2): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)\n",
            "        (mlp): GPT2MLP(\n",
            "          (c_fc): Linear4bit(in_features=5120, out_features=20480, bias=True)\n",
            "          (c_proj): Linear4bit(in_features=20480, out_features=5120, bias=True)\n",
            "          (act): FastGELUActivation()\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ln_f): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (lm_head): Linear(in_features=5120, out_features=256000, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             config = config,\n",
        "                                             device_map = {\"\":0},\n",
        "                                             quantization_config = BitsAndBytesConfig(load_in_4bit = True,\n",
        "                                                                                      bnb_4bit_use_double_quant = True,\n",
        "                                                                                      bnb_4bit_quant_type = 'nf4',\n",
        "                                                                                      bnb_4bit_compute_dtype = torch.bfloat16))\n",
        "# Enable Gradient Checkpointing\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "# Show Model Summary\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LojhXAqEjeJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17aaa113-ff50-458a-e82d-84abe711fa89"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 6,553,600 || all params: 13,913,344,000 || trainable%: 0.047102982575576366\n",
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): GPT2LMHeadModel(\n",
            "      (transformer): GPT2Model(\n",
            "        (wte): Embedding(256000, 5120)\n",
            "        (wpe): Embedding(2048, 5120)\n",
            "        (drop): Dropout(p=0.0, inplace=False)\n",
            "        (h): ModuleList(\n",
            "          (0-39): 40 x GPT2Block(\n",
            "            (ln_1): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn): GPT2Attention(\n",
            "              (c_attn): Linear4bit(\n",
            "                in_features=5120, out_features=15360, bias=True\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.1, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=5120, out_features=8, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=8, out_features=15360, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (c_proj): Linear4bit(in_features=5120, out_features=5120, bias=True)\n",
            "              (attn_dropout): Dropout(p=0.0, inplace=False)\n",
            "              (resid_dropout): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "            (ln_2): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)\n",
            "            (mlp): GPT2MLP(\n",
            "              (c_fc): Linear4bit(in_features=5120, out_features=20480, bias=True)\n",
            "              (c_proj): Linear4bit(in_features=20480, out_features=5120, bias=True)\n",
            "              (act): FastGELUActivation()\n",
            "              (dropout): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (ln_f): LayerNorm((5120,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "      (lm_head): Linear(in_features=5120, out_features=256000, bias=False)\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create LoRA config\n",
        "loraconfig = LoraConfig(r = 8,\n",
        "                        lora_alpha = 16,\n",
        "                        lora_dropout = 0.10,\n",
        "                        bias = 'none',\n",
        "                        task_type = TaskType.CAUSAL_LM,\n",
        "                        fan_in_fan_out = True)\n",
        "\n",
        "# Prep for Training\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "\n",
        "# Create LoRA Model\n",
        "model = get_peft_model(model, loraconfig)\n",
        "model.print_trainable_parameters()\n",
        "\n",
        "# Show Model Summary\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UqXl1nW5dk6Z"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "# Alpaca Translated\n",
        "datasets = load_dataset('BramVanroy/alpaca-cleaned-dutch')\n",
        "\n",
        "# Summary\n",
        "print(datasets)\n",
        "\n",
        "# Validation size\n",
        "VAL_SIZE = 2048\n",
        "\n",
        "# Split in Train and Val datasets\n",
        "dataset_split = datasets[\"train\"].train_test_split(test_size = VAL_SIZE,\n",
        "                                                   shuffle = True,\n",
        "                                                   seed = 42)\n",
        "\n",
        "# Train and Val datasets\n",
        "train_data = dataset_split[\"train\"]\n",
        "val_data = dataset_split[\"test\"]\n",
        "\n",
        "# Save for later use\n",
        "val_data.save_to_disk(f'{WORK_DIR}val_data')\n",
        "train_data.save_to_disk(f'{WORK_DIR}train_data')\n",
        "\"\"\"\n",
        "\n",
        "# Load Train and Validation sets from earlier training run...use code above to generate new train and val datasets.\n",
        "val_data = load_from_disk(f'{WORK_DIR}val_data')\n",
        "train_data = load_from_disk(f'{WORK_DIR}train_data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jhAmxWFPmh3f"
      },
      "outputs": [],
      "source": [
        "# Model Training Prompt\n",
        "def generate_training_prompt(data_point):\n",
        "    if data_point[\"input\"]:\n",
        "        return f\"\"\"\n",
        "### Instructie:\n",
        "{data_point[\"instruction\"]}\n",
        "\n",
        "### Invoer:\n",
        "{data_point[\"input\"]}\n",
        "\n",
        "### Antwoord:\n",
        "{data_point[\"output\"]}\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"\n",
        "### Instructie:\n",
        "{data_point[\"instruction\"]}\n",
        "\n",
        "### Antwoord:\n",
        "{data_point[\"output\"]}\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IAS45lwFP_KI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9f0be7d-4587-41fc-c9cc-b60d7f724e2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=======================================================================\n",
            "\n",
            "### Instructie:\n",
            "Maak een zin met 5 uitdrukkingen.\n",
            "\n",
            "### Antwoord:\n",
            "Hij was zo boos dat hij stond te trappelen, op het verkeerde spoor zat, een appeltje te schillen had, te vroeg was begonnen, en zijn woorden moest inslikken.\n",
            "\n",
            "=======================================================================\n",
            "\n",
            "### Instructie:\n",
            "Voeg 3 interessante feiten over Colombia toe.\n",
            "\n",
            "### Antwoord:\n",
            "1. Colombia heeft na Brazilië de op één na grootste biodiversiteit ter wereld, met meer dan 79.000 diersoorten en 40.000 plantensoorten.\n",
            "2. Colombia produceert meer koffie dan welk ander land ter wereld dan ook.\n",
            "3. Colombia is het enige Zuid-Amerikaanse land met een kustlijn aan zowel de Stille Oceaan als de Caribische Zee.\n",
            "\n",
            "=======================================================================\n",
            "\n",
            "### Instructie:\n",
            "Geef een voorbeeld van een gebruikstoepassing voor Machine Learning\n",
            "\n",
            "### Antwoord:\n",
            "Een voorbeeld van een gebruikstoepassing voor Machine Learning is een spamfilteringsysteem. Het maakt gebruik van ML-algoritmen om spam-e-mails te detecteren en uit de inbox van een gebruiker te filteren.\n",
            "\n",
            "=======================================================================\n",
            "\n",
            "### Instructie:\n",
            "Herschrijf de zin om de wezelwoorden te vermijden en de verklaring specifieker te maken.\n",
            "\n",
            "### Invoer:\n",
            "Sommige mensen geloven dat het eten van bananen gezond is.\n",
            "\n",
            "### Antwoord:\n",
            "Onderzoek toont aan dat het eten van bananen veel gezondheidsvoordelen kan bieden.\n",
            "\n",
            "=======================================================================\n",
            "\n",
            "### Instructie:\n",
            "Herschrijf de volgende zin op een objectieve manier.\n",
            "\n",
            "### Invoer:\n",
            "De president van het bedrijf was een vreselijke leider.\n",
            "\n",
            "### Antwoord:\n",
            "Onder leiding van de president was de prestatie van het bedrijf onbevredigend.\n",
            "\n",
            "=======================================================================\n",
            "\n",
            "### Instructie:\n",
            "Gegeven een dialoog, ontdek welk personage een hoger niveau van respect heeft getoond\n",
            "\n",
            "### Invoer:\n",
            "Persoon A: Ik denk dat dit de juiste weg is\n",
            "Persoon B: Dat is een interessant idee, ik wil er meer over horen\n",
            "\n",
            "### Antwoord:\n",
            "Persoon B heeft een hoger niveau van respect getoond; ze hebben het idee van Persoon A erkend en zijn in gesprek om er meer over te leren.\n"
          ]
        }
      ],
      "source": [
        "# Plot some samples\n",
        "for index, item_data in enumerate(train_data):\n",
        "    print('\\n=======================================================================')\n",
        "    print(generate_training_prompt(item_data))\n",
        "\n",
        "    if index > 4:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SfkFLnQJrhn7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "5a10ac13f12744ccb2cb0603888685ac",
            "44d5823f48ea4631acb622e7dd4135b8",
            "1c98ba4f5f4347c8aea6c358ec425f8c",
            "2decd23e4e6f4ac2aeb56fb44fcae099",
            "efdaa467dd084ccf8960213e6ea9b3d8",
            "d75080f2373f4373bde6621dd6abeab7",
            "905b23750aab41b09747d66f241bf213",
            "4ed51c6b800641a48f12edef0720e0db",
            "ce09467544dc42faab9d50bde6a2ccf5",
            "85f413e0dd6c4efd8850bb82ed5bc3a3",
            "209f02d9ecf147238a37125cfe9e0b60",
            "3c1dd1a321e04e39866d9af2074a5f5d",
            "d37456fa7c994ddbb570233feec04bf5",
            "dd89017b637041da8fa15a092bfa6152",
            "44325398eac0457daa6f1d103f6d6084",
            "53e86afc9cad42b2b1750f735fe084d1",
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            "0c707ccacf2f4334af009a57fc47f69d",
            "53cc1833a8394e07806d4879a3fd9e6c",
            "cf84fd4f8cce4f818221a164488a4aec",
            "db11d2bd7e8442c6be0413f3113b7cd4",
            "cfa0101100f2475b86dfb21911a15016"
          ]
        },
        "outputId": "7b2a7a95-e804-46e6-dfc2-ede6ee9f84bd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/49664 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a10ac13f12744ccb2cb0603888685ac"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2048 [00:00<?, ? examples/s]"
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              "model_id": "3c1dd1a321e04e39866d9af2074a5f5d"
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          },
          "metadata": {}
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      ],
      "source": [
        "# Custom Tokenize method\n",
        "def tokenize(prompt):\n",
        "    # Add EOS token\n",
        "    prompt += tokenizer.eos_token\n",
        "\n",
        "    # Tokenize\n",
        "    tokenized_prompt = tokenizer.encode_plus(prompt,\n",
        "                                             truncation = True,\n",
        "                                             max_length = MAX_LEN,\n",
        "                                             add_special_tokens = True)\n",
        "\n",
        "    return tokenized_prompt\n",
        "\n",
        "# Tokenize Train Data\n",
        "train_data = train_data.shuffle().map(lambda x: tokenize(generate_training_prompt(x)))\n",
        "\n",
        "# Tokenize Val Data\n",
        "val_data = val_data.shuffle().map(lambda x: tokenize(generate_training_prompt(x)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9QY5dX-l00-l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5654d9b-3778-402e-c981-85afad968747"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': 7474, 'instruction': 'Genereer een lijst met buzzwords in data science', 'input': '', 'output': '- Kunstmatige intelligentie\\n- Machine learning\\n- Diep leren\\n- Natuurlijke taalverwerking\\n- Data mining\\n- Big data\\n- Data visualisatie\\n- Data-analyse\\n- Voorspellende analyse\\n- Algoritmes\\n- Datastructuren\\n- Cloud computing\\n- Regressieanalyse\\n- Classificatie\\n- Hyperparameters\\n- Feature engineering\\n- Automatisering\\n- Neurale netwerken\\n- Versterkend leren', 'input_ids': [460, 213, 88334, 196908, 1725, 475, 213, 216771, 701, 1648, 168544, 1868, 192031, 472, 473, 819, 4422, 213, 213, 88334, 47021, 163437, 475, 213, 474, 23295, 175157, 544, 15403, 1725, 213, 474, 24725, 2409, 213, 474, 1274, 752, 105742, 213, 474, 208874, 544, 460, 95284, 4538, 87922, 213, 474, 5621, 17501, 213, 474, 6050, 819, 213, 474, 5621, 8204, 193920, 213, 474, 5621, 474, 38556, 213, 474, 27263, 137231, 12091, 36961, 213, 474, 83988, 192498, 770, 213, 474, 5621, 38255, 580, 213, 474, 13616, 20974, 213, 474, 197466, 22908, 38556, 213, 474, 6430, 50716, 19260, 213, 474, 39025, 15850, 213, 474, 34727, 9497, 213, 474, 90679, 24179, 719, 213, 474, 185027, 544, 205846, 630, 213, 474, 4126, 211670, 4503, 105742, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
            "{'id': 4656, 'instruction': 'Maak een algoritme om te bepalen of twee strings anagrammen zijn.', 'input': '', 'output': 'Het algoritme kan twee strings als input nemen en geeft true terug als ze anagrammen zijn en false als ze dat niet zijn. Om dit te doen, kunnen we elke string nemen, deze converteren naar een gesorteerde array van karakters en vervolgens de arrays vergelijken. Als de arrays identiek zijn, dan zijn de strings anagrammen en geven we true terug, anders false.', 'input_ids': [460, 213, 88334, 196908, 1725, 475, 213, 24710, 7615, 1648, 240422, 544, 4752, 1374, 501, 181810, 470, 30709, 6564, 523, 109401, 5873, 4958, 461, 213, 213, 88334, 47021, 163437, 475, 213, 104004, 240422, 544, 9491, 30709, 6564, 1537, 1880, 65746, 545, 75575, 1088, 42157, 1537, 5906, 523, 109401, 5873, 4958, 545, 1658, 1537, 5906, 4330, 5874, 4958, 461, 33208, 6277, 1374, 32195, 459, 17798, 535, 75648, 1143, 65746, 459, 10457, 27810, 630, 9591, 1648, 3874, 10107, 58118, 1626, 1337, 75320, 472, 545, 149669, 489, 15936, 146559, 630, 461, 10014, 489, 15936, 43933, 47790, 4958, 459, 1327, 4958, 489, 6564, 523, 109401, 5873, 545, 51140, 535, 1088, 42157, 459, 23458, 1658, 461, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
            "{'id': 8344, 'instruction': \"Vergelijk en contrasteer de begrippen 'behoefte' en 'wens'.\", 'input': '', 'output': 'Behoeften en wensen zijn beide gewenste uitkomsten. Hoewel ze vaak door elkaar worden gebruikt, zijn er duidelijke verschillen. Behoeften zijn essentieel voor overleving en welzijn - denk aan voedsel, onderdak, kleding. Wensen daarentegen zijn verlangens die niet essentieel zijn voor overleving, hoewel ze het leven aangenamer kunnen maken - denk aan een luxe auto, dure sieraden en andere status- of plezierartikelen. Het is belangrijk om het verschil tussen behoeften en wensen te begrijpen; dit kan ons helpen om onze uitgaven te prioriteren en ervoor te zorgen dat we de essentiële zaken voor onszelf en onze families kunnen bieden.', 'input_ids': [460, 213, 88334, 196908, 1725, 475, 213, 14406, 166143, 545, 126563, 701, 489, 57098, 184182, 702, 3437, 138793, 544, 484, 545, 702, 32453, 472, 484, 461, 213, 213, 88334, 47021, 163437, 475, 213, 11129, 138793, 630, 545, 173605, 4958, 32952, 217093, 544, 7643, 126863, 630, 461, 11451, 158644, 5906, 54658, 2475, 49827, 8219, 61518, 459, 4958, 2137, 81547, 544, 208671, 461, 2008, 138793, 630, 4958, 55398, 19260, 2694, 3222, 676, 188481, 545, 15978, 121408, 532, 98323, 4924, 51389, 793, 11406, 459, 19573, 63922, 459, 460, 142461, 461, 609, 121001, 30255, 22408, 4773, 4958, 123453, 472, 616, 5874, 55398, 19260, 2694, 4958, 3222, 676, 188481, 459, 29514, 53674, 5906, 1870, 57989, 85741, 812, 675, 17798, 21139, 532, 98323, 4924, 1648, 70247, 3498, 459, 82815, 1937, 139674, 545, 7027, 2791, 474, 470, 194764, 55146, 630, 461, 9057, 478, 91150, 4752, 1870, 196846, 38926, 218441, 580, 545, 173605, 1374, 207412, 630, 524, 6277, 9491, 499, 472, 101788, 4752, 19608, 243800, 19933, 1374, 103150, 231626, 545, 128362, 1374, 89116, 4330, 535, 489, 55398, 3348, 125579, 120307, 3222, 499, 472, 116745, 545, 19608, 3650, 17798, 89525, 461, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
            "{'id': 5270, 'instruction': 'Genereer een verhaal dat begint met de zin: \"Het was de koudste nacht van het jaar\".', 'input': '', 'output': 'Het was de koudste nacht van het jaar. De wind wakkerde buiten aan tot een storm, waardoor de ramen en deuren van het oude huis rammelden. Binnen danste het vuur in de woonkamer en wierp spookachtige schaduwen tegen de muren. Ik zat plotseling rechtop, staarde in het duister en zag hoe er een figuur uit de duisternis opdook. Het was lang en dun en droeg een dikke lange jas die zijn beste tijd had gehad. Terwijl het dichterbij kwam, keek ik het recht in de ogen en was geschokt door de intensiteit van de haat die ik in zijn blik voelde. Het stopte plotseling en onze ogen bleven vergrendeld voor wat aanvoelde als een eeuwigheid. Ik had het gevoel dat er iets ergs stond te gebeuren en dat de koude nacht nog veel kouder zou worden.', 'input_ids': [460, 213, 88334, 196908, 1725, 475, 213, 216771, 701, 1648, 120577, 4330, 170062, 1868, 489, 103769, 475, 520, 104004, 525, 489, 2066, 73632, 7710, 127880, 1337, 1870, 18608, 1761, 213, 213, 88334, 47021, 163437, 475, 213, 104004, 525, 489, 2066, 73632, 7710, 127880, 1337, 1870, 18608, 461, 1296, 7086, 882, 211097, 1294, 62265, 4924, 465, 504, 1648, 11830, 459, 101555, 489, 135798, 545, 489, 41397, 1337, 1870, 113104, 41325, 41033, 182861, 461, 136794, 1209, 1363, 1870, 17467, 3859, 473, 489, 92585, 86905, 545, 1917, 22862, 137369, 4479, 72080, 243153, 202146, 29399, 489, 38807, 630, 461, 21904, 65747, 6967, 11406, 719, 20824, 5308, 459, 15579, 81826, 473, 1870, 858, 52705, 545, 81455, 29514, 2137, 1648, 43273, 48568, 7643, 489, 858, 239891, 1045, 1772, 1493, 7796, 461, 9057, 525, 14221, 545, 858, 580, 545, 460, 60136, 16860, 1648, 644, 120848, 14915, 4589, 472, 616, 4958, 22344, 28113, 632, 140373, 461, 20124, 168154, 1175, 1870, 85713, 193552, 104182, 459, 3885, 5845, 8189, 1870, 20824, 473, 489, 145218, 545, 525, 63756, 7796, 504, 2475, 489, 84100, 64906, 1337, 489, 1285, 2685, 616, 8189, 473, 4958, 460, 174858, 120380, 1294, 461, 9057, 1932, 1363, 6967, 11406, 719, 545, 19608, 145218, 1423, 81053, 132476, 87369, 27964, 3222, 13894, 4924, 9416, 205635, 1537, 1648, 154011, 104362, 461, 21904, 632, 1870, 124130, 4330, 2137, 43660, 45270, 472, 145845, 1374, 172042, 630, 545, 4330, 489, 14564, 44560, 127880, 12788, 17752, 14564, 93752, 34808, 8219, 461, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ],
      "source": [
        "# Show tokenized samples\n",
        "for index, item in enumerate(train_data):\n",
        "    print(item)\n",
        "\n",
        "    if index > 2:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NQmjWednBkgM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c8580c7c-1fa6-43d0-d550-43bfa6519c6e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='776' max='776' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [776/776 4:08:41, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>1.462600</td>\n",
              "      <td>1.461327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>256</td>\n",
              "      <td>1.402700</td>\n",
              "      <td>1.423527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>384</td>\n",
              "      <td>1.400200</td>\n",
              "      <td>1.405416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>512</td>\n",
              "      <td>1.385700</td>\n",
              "      <td>1.395065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>1.379800</td>\n",
              "      <td>1.386992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>768</td>\n",
              "      <td>1.362900</td>\n",
              "      <td>1.383944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/robinsmits/polylm_13b_ft_alpaca_clean_dutch/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Set Steps\n",
        "eval_steps = 128\n",
        "logging_steps = 16\n",
        "\n",
        "# Config Trainer\n",
        "trainer = transformers.Trainer(model = model,\n",
        "                               train_dataset = train_data,\n",
        "                               eval_dataset = val_data,\n",
        "                               args = transformers.TrainingArguments(num_train_epochs = 1,\n",
        "                                                                     learning_rate = 5.0e-5,\n",
        "                                                                     evaluation_strategy = \"steps\",\n",
        "                                                                     logging_steps = logging_steps,\n",
        "                                                                     save_strategy = \"epoch\",\n",
        "                                                                     eval_steps = eval_steps,\n",
        "                                                                     save_total_limit = 1,\n",
        "                                                                     per_device_train_batch_size = 4,\n",
        "                                                                     per_device_eval_batch_size = 8,\n",
        "                                                                     gradient_accumulation_steps = 16,\n",
        "                                                                     warmup_steps = 64,\n",
        "                                                                     bf16 = True,\n",
        "                                                                     output_dir = hf_model_name,\n",
        "                                                                     hub_model_id = hf_model_name,\n",
        "                                                                     push_to_hub = True,\n",
        "                                                                     hub_private_repo = False,\n",
        "                                                                     optim = \"paged_adamw_8bit\",\n",
        "                                                                     report_to = \"tensorboard\"),\n",
        "                               data_collator = transformers.DataCollatorForLanguageModeling(tokenizer,\n",
        "                                                                                            mlm = False))\n",
        "\n",
        "# Perform Training\n",
        "trainer.train()\n",
        "\n",
        "# Push model to hub\n",
        "trainer.push_to_hub()"
      ]
    }
  ],
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      "authorship_tag": "ABX9TyNkNRRZMzONeOdm+/eo25zM",
      "include_colab_link": true
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