Open_Llama_7B_Alpaca_Clean_Dutch_Qlora.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RobinSmits/Dutch-LLMs/blob/main/Open_Llama_7B_Alpaca_Clean_Dutch_Qlora.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "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/OpenLlama7BAlpacaCleanDutchQlora/'\n",
        "os.makedirs(WORK_DIR, exist_ok = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RE7rRxij3Pz",
        "outputId": "17be638a-fa7c-4e8c-a7c0-63fdbc079d58"
      },
      "execution_count": null,
      "outputs": [
        {
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-TtD0uIHxUt",
        "outputId": "db232154-b673-445b-ac43-b4508ec9ec44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q accelerate\n",
        "!pip install sentencepiece\n",
        "!pip install -q bitsandbytes\n",
        "!pip install -q transformers\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q datasets\n",
        "!pip install -q wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7R3VLs6PHZQX",
        "outputId": "ce460baf-3594-431e-a779-f146cd621508"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-3hunth0dq5rnu --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import sentencepiece\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GenerationConfig\n",
        "from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model, TaskType\n",
        "import transformers\n",
        "import wandb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# WandB logging\n",
        "reporting = \"wandb\"\n",
        "\n",
        "# WandB Login\n",
        "wandb.login()\n",
        "\n",
        "# WandB Settings\n",
        "wandb.init(project = 'OpenLlama7BAlpacaCleanDutch',\n",
        "           config = {\"model\": 'open_llama_7B', \"dataset\": 'alpaca-dutch-cleaned'})"
      ],
      "metadata": {
        "id": "J_CWF0Jxpv01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Name Constants\n",
        "model_name = \"openlm-research/open_llama_7b\"\n",
        "hf_model_name = 'open_llama_7b_alpaca_clean_dutch_qlora'"
      ],
      "metadata": {
        "id": "Uhk1Rkk_gNCK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Zu2NDXPERde",
        "outputId": "3097d3a6-b3f0-4b49-f8af-fd3356e8088e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaTokenizer(name_or_path='openlm-research/open_llama_7b', vocab_size=32000, model_max_length=2048, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'eos_token': AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'unk_token': AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=True), 'pad_token': '<unk>'}, clean_up_tokenization_spaces=False)\n",
            "pad_token_id:  0\n",
            "bos_token_id:  1\n",
            "eos_token_id:  2\n",
            "pad_token_id:  <unk>\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",
        "                                          add_eos_token = True)\n",
        "tokenizer.pad_token_id = 0\n",
        "\n",
        "# Max Length\n",
        "MAX_LEN = 512\n",
        "\n",
        "# Tokenizer Summary\n",
        "print(tokenizer)\n",
        "\n",
        "# Special Tokens\n",
        "print('pad_token_id: ',tokenizer.pad_token_id)\n",
        "print('bos_token_id: ',tokenizer.bos_token_id)\n",
        "print('eos_token_id: ',tokenizer.eos_token_id)\n",
        "print('pad_token_id: ',tokenizer.decode(tokenizer.pad_token_id))\n",
        "print('bos_token_id: ',tokenizer.decode(tokenizer.bos_token_id))\n",
        "print('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": "2315e101-8b32-46f5-aeeb-cb2395a4c825"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaConfig {\n",
              "  \"_name_or_path\": \"openlm-research/open_llama_7b\",\n",
              "  \"architectures\": [\n",
              "    \"LlamaForCausalLM\"\n",
              "  ],\n",
              "  \"bos_token_id\": 1,\n",
              "  \"eos_token_id\": 2,\n",
              "  \"hidden_act\": \"silu\",\n",
              "  \"hidden_size\": 4096,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 11008,\n",
              "  \"max_position_embeddings\": 2048,\n",
              "  \"model_type\": \"llama\",\n",
              "  \"num_attention_heads\": 32,\n",
              "  \"num_hidden_layers\": 32,\n",
              "  \"pad_token_id\": 0,\n",
              "  \"rms_norm_eps\": 1e-06,\n",
              "  \"tie_word_embeddings\": false,\n",
              "  \"torch_dtype\": \"float16\",\n",
              "  \"transformers_version\": \"4.30.2\",\n",
              "  \"use_cache\": false,\n",
              "  \"vocab_size\": 32000\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Create Config\n",
        "config = AutoConfig.from_pretrained(model_name,\n",
        "                                    use_cache = False)\n",
        "\n",
        "# Summary\n",
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-YpfgUWAEdRe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568,
          "referenced_widgets": [
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          ]
        },
        "outputId": "0f58a79b-3deb-46a1-c08d-fcb06feb0ad9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "989331125f1a4bbca6e3479272f76339"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, 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",
        "\n",
        "# Enable Gradient Checkpointing\n",
        "model.gradient_checkpointing_enable()\n",
        "\n",
        "# Show Model Summary\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LojhXAqEjeJ",
        "outputId": "98907472-2104-4b0d-8ccb-b8348d0fed4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4,194,304 || all params: 3,504,607,232 || trainable%: 0.11967971650867153\n",
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): LlamaForCausalLM(\n",
            "      (model): LlamaModel(\n",
            "        (embed_tokens): Embedding(32000, 4096, padding_idx=0)\n",
            "        (layers): ModuleList(\n",
            "          (0-31): 32 x LlamaDecoderLayer(\n",
            "            (self_attn): LlamaAttention(\n",
            "              (q_proj): Linear4bit(\n",
            "                in_features=4096, out_features=4096, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=4096, out_features=8, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=8, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "              (v_proj): Linear4bit(\n",
            "                in_features=4096, out_features=4096, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, inplace=False)\n",
            "                )\n",
            "                (lora_A): ModuleDict(\n",
            "                  (default): Linear(in_features=4096, out_features=8, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=8, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "              (rotary_emb): LlamaRotaryEmbedding()\n",
            "            )\n",
            "            (mlp): LlamaMLP(\n",
            "              (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "              (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
            "              (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
            "              (act_fn): SiLUActivation()\n",
            "            )\n",
            "            (input_layernorm): LlamaRMSNorm()\n",
            "            (post_attention_layernorm): LlamaRMSNorm()\n",
            "          )\n",
            "        )\n",
            "        (norm): LlamaRMSNorm()\n",
            "      )\n",
            "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create LoRA config\n",
        "loraconfig = LoraConfig(r = 8,\n",
        "                        lora_alpha = 16,\n",
        "                        target_modules = ['q_proj', 'v_proj'],\n",
        "                        lora_dropout = 0.05,\n",
        "                        bias = 'none',\n",
        "                        task_type = TaskType.CAUSAL_LM)\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196,
          "referenced_widgets": [
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        "id": "UqXl1nW5dk6Z",
        "outputId": "3a09f917-daed-4917-b56b-bd1d7c57c3d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/BramVanroy___json/BramVanroy--alpaca-cleaned-dutch-985ba444dec30e05/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "084ffdaff9bf4e1a86ce938752af5880"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['id', 'instruction', 'input', 'output'],\n",
            "        num_rows: 51712\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Alpaca Translated\n",
        "datasets = load_dataset('BramVanroy/alpaca-cleaned-dutch')\n",
        "\n",
        "# Summary\n",
        "print(datasets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BXliS4gCEi1V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55,
          "referenced_widgets": [
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            "3047e171948843baa9ee9cb927cd8f91",
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          ]
        },
        "outputId": "26f1fdff-a35d-4c9e-84c7-cb5240f40991"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached split indices for dataset at /root/.cache/huggingface/datasets/BramVanroy___json/BramVanroy--alpaca-cleaned-dutch-985ba444dec30e05/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-0f7260bc068c7e5e.arrow and /root/.cache/huggingface/datasets/BramVanroy___json/BramVanroy--alpaca-cleaned-dutch-985ba444dec30e05/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-40ef988d605ceaae.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/2048 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6cbf9aaaf46941f8808d4bcaf10b37ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/49664 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "223f5171f38243f3bfcbfa2291e4bcb1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Validation size\n",
        "VAL_SIZE = 2048\n",
        "\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')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(data_point):\n",
        "    if data_point[\"input\"]:\n",
        "        return f\"\"\"### Instructie:\n",
        "{data_point[\"instruction\"]}\n",
        "\n",
        "### Invoer:\n",
        "{data_point[\"input\"]}\n",
        "\n",
        "### Antwoord:\n",
        "{data_point[\"output\"]}\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"### Instructie:\n",
        "{data_point[\"instruction\"]}\n",
        "\n",
        "### Antwoord:\n",
        "{data_point[\"output\"]}\"\"\""
      ],
      "metadata": {
        "id": "jhAmxWFPmh3f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counter = 0\n",
        "for item_data in train_data:\n",
        "    print('\\n=======================================================================')\n",
        "    print(generate_prompt(item_data))\n",
        "\n",
        "    counter += 1\n",
        "    if counter > 4:\n",
        "        break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAS45lwFP_KI",
        "outputId": "2eef5c0c-41f7-41da-ad57-8c32d4e8a0b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\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",
            "### 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",
            "### 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",
            "### 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",
            "### 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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            "474b203ecea8419cbb6e29cb2f87963f",
            "ada1b5802d064beba989e320ff4890e5",
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            "4c8cd08405a849ee8318eceded246bef",
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            "c528c7868ec14535bc0d1090cf3f5bab",
            "74266246d4bb4af3a3f4c5efd04370b6",
            "63ada5474cd44897a606f701f31ce65a"
          ]
        },
        "id": "KAgTy7mbjGCV",
        "outputId": "245e978a-55e2-4f7d-ac1e-f6dd3311a910"
      },
      "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": "541e39d241e94f669db00452b5846629"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_data = train_data.shuffle().map(lambda x: tokenizer.encode_plus(generate_prompt(x), truncation = True, max_length = MAX_LEN, padding = \"max_length\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "9d6a94db07aa4b80933ebf08d881ed46",
            "7a4f362ab0ae44cda3e4fd8bddcef882",
            "e99e27479b5848b7be1df93f332ac8a1",
            "3aed104bc20a41c4ba05e241e9cccf62",
            "6171c1fffaf040eca90c5e036350cd5e",
            "b96c927408e643bc941eddf9dd2ff074",
            "e6202550e0944b579a5b8e820fb8d0a7",
            "1ce4b4be6cd84cc2a8b369a5546bd4a8",
            "cc3d002f0a2c4c35a70d729f2d1b9799",
            "fc1317e52feb4c1ea905abf63457e283",
            "aa91f69a66704424b42c80f426a3e2da"
          ]
        },
        "id": "TN9RYHOIZjpa",
        "outputId": "66e93564-1019-4b17-d31c-e6d9982bc311"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2048 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d6a94db07aa4b80933ebf08d881ed46"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "val_data = val_data.shuffle().map(lambda x: tokenizer.encode_plus(generate_prompt(x), truncation = True, max_length = MAX_LEN, padding = \"max_length\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "NQmjWednBkgM",
        "outputId": "22da7a4d-da15-4acd-ac25-556a552f5b2c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1246' max='1552' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1246/1552 5:01:09 < 1:14:04, 0.07 it/s, Epoch 1.60/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>1.245300</td>\n",
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              "    </tr>\n",
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              "      <td>256</td>\n",
              "      <td>1.214800</td>\n",
              "      <td>1.199918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>384</td>\n",
              "      <td>1.185100</td>\n",
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              "    </tr>\n",
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              "      <td>512</td>\n",
              "      <td>1.152300</td>\n",
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              "      <td>640</td>\n",
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              "      <td>1.063300</td>\n",
              "      <td>1.102551</td>\n",
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              "      <td>1152</td>\n",
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1552' max='1552' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1552/1552 6:17:13, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>1.245300</td>\n",
              "      <td>1.246110</td>\n",
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              "      <td>1.199918</td>\n",
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              "      <td>384</td>\n",
              "      <td>1.185100</td>\n",
              "      <td>1.175393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>512</td>\n",
              "      <td>1.152300</td>\n",
              "      <td>1.151609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>1.129800</td>\n",
              "      <td>1.137925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>768</td>\n",
              "      <td>1.124000</td>\n",
              "      <td>1.122694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>896</td>\n",
              "      <td>1.065500</td>\n",
              "      <td>1.112084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1024</td>\n",
              "      <td>1.063300</td>\n",
              "      <td>1.102551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1152</td>\n",
              "      <td>1.051900</td>\n",
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              "      <td>1.010000</td>\n",
              "      <td>1.071030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1536</td>\n",
              "      <td>1.017700</td>\n",
              "      <td>1.064457</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1552, training_loss=1.124638299352115, metrics={'train_runtime': 22647.8727, 'train_samples_per_second': 4.386, 'train_steps_per_second': 0.069, 'total_flos': 1.0293857511140229e+18, 'train_loss': 1.124638299352115, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Set Steps\n",
        "eval_steps = 128\n",
        "save_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 = 2,\n",
        "                                                                     learning_rate = 2.0e-4,\n",
        "                                                                     evaluation_strategy = \"steps\",\n",
        "                                                                     logging_steps = logging_steps,\n",
        "                                                                     save_strategy = \"steps\",\n",
        "                                                                     eval_steps = eval_steps,\n",
        "                                                                     save_steps = save_steps,\n",
        "                                                                     save_total_limit = 3,\n",
        "                                                                     per_device_train_batch_size = 32,\n",
        "                                                                     per_device_eval_batch_size = 32,\n",
        "                                                                     gradient_accumulation_steps = 2,\n",
        "                                                                     warmup_steps = 64,\n",
        "                                                                     fp16 = True,\n",
        "                                                                     output_dir = \"outputs\",\n",
        "                                                                     optim = \"paged_adamw_8bit\",\n",
        "                                                                     report_to = reporting),\n",
        "                               data_collator = transformers.DataCollatorForLanguageModeling(tokenizer,\n",
        "                                                                                            mlm = False))\n",
        "\n",
        "# Perform Training\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cFul80rnlFel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9f8dcef-3631-410c-f88b-6aa92290ea68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/My Drive/LLM/OpenLlama7BAlpacaCleanDutchQlora/tokenizer_config.json',\n",
              " '/content/drive/My Drive/LLM/OpenLlama7BAlpacaCleanDutchQlora/special_tokens_map.json',\n",
              " '/content/drive/My Drive/LLM/OpenLlama7BAlpacaCleanDutchQlora/tokenizer.model',\n",
              " '/content/drive/My Drive/LLM/OpenLlama7BAlpacaCleanDutchQlora/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Save Finetuned Model\n",
        "trainer.model.save_pretrained(f'{WORK_DIR}')\n",
        "tokenizer.save_pretrained(f'{WORK_DIR}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "LYtkOOOEYEQ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
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            "6576af3653d14d57ab5e8ee831550069",
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            "b40b739f19cb4dafa51bb0b05debc06a",
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            "ee5f36c2b0f44aada4eb8bf28835df90",
            "797ab5393fc84af8a1668cf23368d612",
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