Open_Llama_13B_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_13B_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",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RE7rRxij3Pz",
        "outputId": "7e0b7db2-915f-438c-d95d-d548650b4ec8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\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/OpenLlama13BAlpacaCleanDutchQlora/'\n",
        "os.makedirs(WORK_DIR, exist_ok = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-TtD0uIHxUt",
        "outputId": "16b571ea-aeaa-4058-f84a-9cab27257639"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "# Install required packages\n",
        "!pip install accelerate\n",
        "!pip install sentencepiece\n",
        "!pip install bitsandbytes\n",
        "!pip install transformers\n",
        "!pip install -U git+https://github.com/huggingface/peft.git\n",
        "!pip install datasets\n",
        "!pip install wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7R3VLs6PHZQX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "222892bd-3499-4613-84e3-4303235d2612"
      },
      "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.11.0\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('//172.28.0.1'), PosixPath('http'), PosixPath('8013')}\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-ahg8gtaa0ju3 --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('//ipykernel.pylab.backend_inline'), PosixPath('module')}\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.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. 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 Modules\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\n",
        "import wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J_CWF0Jxpv01"
      },
      "outputs": [],
      "source": [
        "# WandB logging\n",
        "reporting = \"wandb\"\n",
        "\n",
        "# WandB Login\n",
        "wandb.login()\n",
        "\n",
        "# WandB Settings\n",
        "wandb.init(project = 'OpenLlama13BAlpacaCleanDutch',\n",
        "           config = {\"model\": 'open_llama_13B', \"dataset\": 'alpaca-dutch-cleaned'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Uhk1Rkk_gNCK"
      },
      "outputs": [],
      "source": [
        "# Set Name Constants\n",
        "model_name = \"openlm-research/open_llama_13b\"\n",
        "hf_model_name = 'open_llama_13b_alpaca_clean_dutch_qlora'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "6Zu2NDXPERde",
        "outputId": "be7a667b-0dca-4c37-a453-a7697ef0da6b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/593 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5b0a72b18614a6d90b9beef5b1a9125"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/534k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1669ac8194c14f4e9fd2d0f57bf49b2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/330 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dfcf309fc2cb48beb09585854a0793f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaTokenizer(name_or_path='openlm-research/open_llama_13b', 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": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496,
          "referenced_widgets": [
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        "id": "FZVrimoUDoEd",
        "outputId": "eb5a87f1-a26d-4d80-c641-bbca17ccb839"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/507 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6971e96dc10d49c894c658492201bf16"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaConfig {\n",
              "  \"_name_or_path\": \"openlm-research/open_llama_13b\",\n",
              "  \"architectures\": [\n",
              "    \"LlamaForCausalLM\"\n",
              "  ],\n",
              "  \"bos_token_id\": 1,\n",
              "  \"eos_token_id\": 2,\n",
              "  \"hidden_act\": \"silu\",\n",
              "  \"hidden_size\": 5120,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 13824,\n",
              "  \"max_position_embeddings\": 2048,\n",
              "  \"model_type\": \"llama\",\n",
              "  \"num_attention_heads\": 40,\n",
              "  \"num_hidden_layers\": 40,\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": 7
        }
      ],
      "source": [
        "# Create Config\n",
        "config = AutoConfig.from_pretrained(model_name,\n",
        "                                    use_cache = False)\n",
        "\n",
        "# Summary\n",
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 5120, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-39): 40 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear4bit(in_features=5120, out_features=13824, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=13824, out_features=5120, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=5120, out_features=13824, 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=5120, 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": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LojhXAqEjeJ",
        "outputId": "06b0a0f0-34e6-41c7-dbfd-004b8b81afd6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 6,553,600 || all params: 6,678,533,120 || trainable%: 0.09812933292752765\n",
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): LlamaForCausalLM(\n",
            "      (model): LlamaModel(\n",
            "        (embed_tokens): Embedding(32000, 5120, padding_idx=0)\n",
            "        (layers): ModuleList(\n",
            "          (0-39): 40 x LlamaDecoderLayer(\n",
            "            (self_attn): LlamaAttention(\n",
            "              (q_proj): Linear4bit(\n",
            "                in_features=5120, out_features=5120, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, 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=5120, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (k_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
            "              (v_proj): Linear4bit(\n",
            "                in_features=5120, out_features=5120, bias=False\n",
            "                (lora_dropout): ModuleDict(\n",
            "                  (default): Dropout(p=0.05, 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=5120, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (o_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
            "              (rotary_emb): LlamaRotaryEmbedding()\n",
            "            )\n",
            "            (mlp): LlamaMLP(\n",
            "              (gate_proj): Linear4bit(in_features=5120, out_features=13824, bias=False)\n",
            "              (down_proj): Linear4bit(in_features=13824, out_features=5120, bias=False)\n",
            "              (up_proj): Linear4bit(in_features=5120, out_features=13824, 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=5120, 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": 10,
      "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": 11,
      "metadata": {
        "id": "jhAmxWFPmh3f"
      },
      "outputs": [],
      "source": [
        "# Model Training Prompt\n",
        "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\"]}\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAS45lwFP_KI",
        "outputId": "af171f68-124e-4262-a8ce-13716e87dd97"
      },
      "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"
          ]
        }
      ],
      "source": [
        "# Plot some samples\n",
        "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
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            "a9a0b0a37df54fb8b2bb8d08c3efbb5e"
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        },
        "id": "KAgTy7mbjGCV",
        "outputId": "4c60662d-e5f2-4efc-fea4-5c5706dbc64e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/49664 [00:00<?, ? examples/s]"
            ],
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        }
      ],
      "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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "TN9RYHOIZjpa",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2048 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8a445dcdc5f41da9c159adaadf43f37"
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          },
          "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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "NQmjWednBkgM",
        "outputId": "b2660061-e045-4276-95a6-59ec8f419340"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1552' max='1552' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1552/1552 10:40:32, 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>1.141800</td>\n",
              "      <td>1.142239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>256</td>\n",
              "      <td>1.111100</td>\n",
              "      <td>1.106115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>384</td>\n",
              "      <td>1.095600</td>\n",
              "      <td>1.099293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>512</td>\n",
              "      <td>1.057900</td>\n",
              "      <td>1.079856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>1.062900</td>\n",
              "      <td>1.060519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>768</td>\n",
              "      <td>1.043900</td>\n",
              "      <td>1.049627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>896</td>\n",
              "      <td>0.982900</td>\n",
              "      <td>1.037903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1024</td>\n",
              "      <td>1.005900</td>\n",
              "      <td>1.029499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1152</td>\n",
              "      <td>0.978800</td>\n",
              "      <td>1.015532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1280</td>\n",
              "      <td>0.958100</td>\n",
              "      <td>1.004862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1408</td>\n",
              "      <td>0.963600</td>\n",
              "      <td>0.995185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1536</td>\n",
              "      <td>0.943100</td>\n",
              "      <td>0.987897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1552, training_loss=1.0727190885347189, metrics={'train_runtime': 38459.1874, 'train_samples_per_second': 2.583, 'train_steps_per_second': 0.04, 'total_flos': 1.987864898222162e+18, 'train_loss': 1.0727190885347189, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "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": 16,
      "metadata": {
        "id": "cFul80rnlFel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9653dead-510a-4e21-becd-58b98996f991"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/My Drive/LLM/OpenLlama13BAlpacaCleanDutchQlora/tokenizer_config.json',\n",
              " '/content/drive/My Drive/LLM/OpenLlama13BAlpacaCleanDutchQlora/special_tokens_map.json',\n",
              " '/content/drive/My Drive/LLM/OpenLlama13BAlpacaCleanDutchQlora/tokenizer.model',\n",
              " '/content/drive/My Drive/LLM/OpenLlama13BAlpacaCleanDutchQlora/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Save Finetuned Model\n",
        "trainer.model.save_pretrained(f'{WORK_DIR}')\n",
        "tokenizer.save_pretrained(f'{WORK_DIR}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
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        "id": "LYtkOOOEYEQ2",
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      "source": [
        "# Huggingface Hub Login\n",
        "notebook_login()"
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      "source": [
        "# Push Model to HF Hub\n",
        "model.push_to_hub(hf_model_name, private = True)"
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