Qwen1_5_7B_Dutch_Chat_SFT.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/Qwen1_5_7B_Dutch_Chat_SFT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction\n",
        "\n",
        "This notebook performs a finetuning of a QLoRA adapter model based on [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat).\n",
        "\n",
        "Officially the Qwen1.5 model types don't support the Dutch language. However when doing some experiments I noticed that the chat quality for the Dutch language (for the 7B and larger sizes..) was comparable or may'be even better then with the Mistral models. Mistral officially also doesn't support Dutch however it already provided some interresting Dutch Chat Models as created by Bram van Roy and Edwin Rijgersberg.\n",
        "\n",
        "This is basically my attempt to further fine-tune the Qwen1.5-7B-Chat model and optimize it for Dutch.\n",
        "\n",
        "The dataset used is the Dutch Chat Dataset [BramVanroy/ultrachat_200k_dutch](https://huggingface.co/datasets/BramVanroy/ultrachat_200k_dutch) as created by Bram van Roy. Kudos to Bram for this dataset!"
      ],
      "metadata": {
        "id": "h8CtGi3NphCj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install and Import Modules"
      ],
      "metadata": {
        "id": "Q3lPgvN_pk-m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install Modules\n",
        "!pip install -q accelerate==0.27.2\n",
        "!pip install -q bitsandbytes==0.43.0\n",
        "!pip install -q datasets==2.17.1\n",
        "!pip install -q peft==0.9.0\n",
        "!pip install -q transformers==4.38.2\n",
        "!pip install -q trl==0.8.1"
      ],
      "metadata": {
        "id": "kLCEjnOZiVP7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "155e1356-624f-4ce8-b863-7a354e72c2d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7R3VLs6PHZQX"
      },
      "outputs": [],
      "source": [
        "# Import Modules\n",
        "from datasets import load_dataset, load_from_disk\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import AutoPeftModelForCausalLM, prepare_model_for_kbit_training, LoraConfig, get_peft_model, TaskType\n",
        "from transformers import (AutoTokenizer,\n",
        "                          AutoModelForCausalLM,\n",
        "                          BitsAndBytesConfig,\n",
        "                          DataCollatorForLanguageModeling,\n",
        "                          TrainingArguments)\n",
        "import torch\n",
        "from trl import SFTTrainer\n",
        "\n",
        "# Set TF32 for A100\n",
        "torch.backends.cuda.matmul.allow_tf32 = True\n",
        "torch.backends.cudnn.allow_tf32 = True"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Constants"
      ],
      "metadata": {
        "id": "K2ELwb27pc2p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uhk1Rkk_gNCK"
      },
      "outputs": [],
      "source": [
        "# Set Name Constants\n",
        "model_name = 'Qwen/Qwen1.5-7B-Chat'\n",
        "hf_model_name = 'Qwen1.5-7B-Dutch-Chat-Sft'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Connect Google Drive"
      ],
      "metadata": {
        "id": "H0mETpALpaGJ"
      }
    },
    {
      "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/QwenDutch/'\n",
        "os.makedirs(WORK_DIR, exist_ok = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adS3GcNGolT0",
        "outputId": "c47e2d07-5782-4939-caa0-37cacafb07ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HuggingFace Login"
      ],
      "metadata": {
        "id": "NquXCba0pW8w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166,
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        },
        "id": "HHeHs7goh3yP",
        "outputId": "59c0e12b-e3df-43bf-a87d-4f0824631f98"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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              "model_id": "c157421ce71443f79d4ce6125ff2d7be"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# HuggingFace Hub Login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xED9rD-2h3yQ"
      },
      "source": [
        "## Tokenizer"
      ]
    },
    {
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        },
        "id": "ewyYBs9-h3yR",
        "outputId": "3a1867cb-572f-4c97-ef44-f470faf21413"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "867a9b68f76845eabb24e491fda3aecb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc9bca9142d3497691f9e3c27a47e5b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5395eaaaa707455ab82e73b79bb0d82f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a2ba29e562246b595e58fab00d7c2b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Qwen2TokenizerFast(name_or_path='Qwen/Qwen1.5-7B-Chat', vocab_size=151643, model_max_length=32768, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>']}, clean_up_tokenization_spaces=False),  added_tokens_decoder={\n",
            "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Create Tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Max Length\n",
        "MAX_LEN = 2048\n",
        "\n",
        "# Tokenizer Summary\n",
        "print(tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fo0E4IRFh3yR"
      },
      "source": [
        "## Create QLoRa Model based on Qwen1.5_7B_Chat Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "BtQrABkuh3yR",
        "outputId": "0b99f895-febf-42db-83c9-b215b25434eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/663 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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        {
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28b14cef2a0e4d958f03ec5451303783"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 159,907,840 || all params: 7,881,232,384 || trainable%: 2.0289699910972705\n",
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): Qwen2ForCausalLM(\n",
            "      (model): Qwen2Model(\n",
            "        (embed_tokens): Embedding(151936, 4096)\n",
            "        (layers): ModuleList(\n",
            "          (0-31): 32 x Qwen2DecoderLayer(\n",
            "            (self_attn): Qwen2SdpaAttention(\n",
            "              (q_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=True)\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=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (k_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=True)\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=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (v_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=True)\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=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (o_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(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=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (rotary_emb): Qwen2RotaryEmbedding()\n",
            "            )\n",
            "            (mlp): Qwen2MLP(\n",
            "              (gate_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(in_features=4096, out_features=11008, 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=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=11008, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (up_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(in_features=4096, out_features=11008, 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=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=11008, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (down_proj): lora.Linear4bit(\n",
            "                (base_layer): Linear4bit(in_features=11008, 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=11008, out_features=64, bias=False)\n",
            "                )\n",
            "                (lora_B): ModuleDict(\n",
            "                  (default): Linear(in_features=64, out_features=4096, bias=False)\n",
            "                )\n",
            "                (lora_embedding_A): ParameterDict()\n",
            "                (lora_embedding_B): ParameterDict()\n",
            "              )\n",
            "              (act_fn): SiLU()\n",
            "            )\n",
            "            (input_layernorm): Qwen2RMSNorm()\n",
            "            (post_attention_layernorm): Qwen2RMSNorm()\n",
            "          )\n",
            "        )\n",
            "        (norm): Qwen2RMSNorm()\n",
            "      )\n",
            "      (lm_head): Linear(in_features=4096, out_features=151936, bias=False)\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map = \"auto\",\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",
        "# Set cache to False\n",
        "model.config.use_cache = False\n",
        "\n",
        "# Create LoRA config\n",
        "lora_config = LoraConfig(r = 64,\n",
        "                         lora_alpha = 16,\n",
        "                         target_modules = ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'gate_proj', 'down_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, lora_config)\n",
        "model.print_trainable_parameters()\n",
        "\n",
        "# Show Model Summary\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gMIhn1Nh3yS"
      },
      "source": [
        "## Modify Default Chat template to Dutch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "kOn0_PLCh3yT",
        "outputId": "da04e346-950b-4fec-8f61-f4230b573f30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\\nJe bent een behulpzame AI assistent<|im_end|>\\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Modify Default Chattemplate with Dutch System Message\n",
        "tokenizer.chat_template = \"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\\nJe bent een behulpzame AI assistent<|im_end|>\\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}\"\n",
        "\n",
        "# Summary Chat Template\n",
        "tokenizer.chat_template"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test Dataset\n",
        "\n",
        "We save the data to storage so that we can use the same data in the second run."
      ],
      "metadata": {
        "id": "Z0QxjiyLb32-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXliS4gCEi1V",
        "outputId": "6edb82a2-c648-45dd-86b6-ba8fda72a89c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['messages'],\n",
            "    num_rows: 2048\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Load Test Dataset\n",
        "#test_data = load_dataset('BramVanroy/ultrachat_200k_dutch', split = 'test_sft')\n",
        "#test_data = test_data.select(range(2048)).remove_columns([\"prompt\", \"prompt_id\"]) # Only part of the test data to limit the time spent on validation.\n",
        "\n",
        "# Save for later use\n",
        "#test_data.save_to_disk(f'{WORK_DIR}test_data')\n",
        "\n",
        "# Load from disk\n",
        "test_data = load_from_disk(f'{WORK_DIR}test_data')\n",
        "\n",
        "# Summary\n",
        "print(test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Dataset\n",
        "\n",
        "The training dataset is split into 2 equal parts to make sure 1 part fits into the maximum of 24 hours runtime with Google Colab Pro.\n",
        "We save the data to storage so that we can use the same data in the second run.\n",
        "\n",
        "We use the HuggingFace Trainer 'resume_from_checkpoint' option to continue training on the second part of the training data."
      ],
      "metadata": {
        "id": "nxcv_pBVbWrp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Train Dataset\n",
        "#train_data1 = load_dataset('BramVanroy/ultrachat_200k_dutch', split = 'train_sft[:50%]').remove_columns([\"prompt\", \"prompt_id\"])\n",
        "#train_data2 = load_dataset('BramVanroy/ultrachat_200k_dutch', split = 'train_sft[-50%:]').remove_columns([\"prompt\", \"prompt_id\"])\n",
        "\n",
        "# Save for later use\n",
        "#train_data1.save_to_disk(f'{WORK_DIR}train_data1') # 733\n",
        "#train_data2.save_to_disk(f'{WORK_DIR}train_data2') # 733\n",
        "\n",
        "# Load from disk\n",
        "train_data1 = load_from_disk(f'{WORK_DIR}train_data1')\n",
        "train_data2 = load_from_disk(f'{WORK_DIR}train_data2')\n",
        "\n",
        "# Summary\n",
        "print(train_data1)\n",
        "print(train_data2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-tGAWKKZSc_",
        "outputId": "75eb1413-ca4e-4a41-db6a-d83315f2f7aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['messages'],\n",
            "    num_rows: 96299\n",
            "})\n",
            "Dataset({\n",
            "    features: ['messages'],\n",
            "    num_rows: 96299\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Chat Template Example"
      ],
      "metadata": {
        "id": "39p5KtlibNRy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAS45lwFP_KI",
        "outputId": "52ba3fe4-7cf7-4d5e-8a9b-69b11da41a66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|im_start|>system\n",
            "Je bent een behulpzame AI assistent<|im_end|>\n",
            "<|im_start|>user\n",
            "Kan je mij vertellen welke versie van mijn website thema ik gebruik? Er staat iets over sectie-gebaseerde thema's zoals Responsive 6.0+, Retina 4.0+, Parallax 3.0+ Turbo 2.0+, Mobilia 5.0+. En ook, hoe zit het met die functie die toelaat het tweede productafbeelding te tonen wanneer ik erover hover? Is dat voor alle secties of enkel de genoemden?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Om te bepalen welke themaversie u gebruikt, moet u meestal in de instellingen van uw websiteachtergrond kijken. Deze kunnen vaak gevonden worden in de thema-editor of het dashboard waar informatie staat over het huidige thema. Wat betreft de functie om de secundaire afbeelding van een product te tonen bij het erover hoveren; dit is een instellingsmogelijkheid die vaak in de secties 'Collecties pagina's' en 'Uitgelichte Collecties' zich bevindt. Deze functie is doorgaans beperkt tot de secties die in het materiaal genoemd worden, tenzij het thema die mogelijkheid ook expliciet in andere secties aanbiedt.<|im_end|>\n",
            "<|im_start|>user\n",
            "Aha, begrijp ik het goed dat ik zelf even moet spitten in de thema-instellingen voor de exacte versie? Zou je me kunnen wijzen waar ik dat normaal vind?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Ja, dat klopt. U kunt meestal de themaversie vinden door in te loggen op uw beheerdersdashboard en naar het gedeelte 'Thema's' te navigeren. Daarna selecteert u 'Aanpassen' bij het thema dat u momenteel gebruikt. Veel thema's hebben informatie over de versie onderaan in de voettekst van de thema-editor of soms onder 'Instellingen' of 'Informatie' binnen de thema-editor.<|im_end|>\n",
            "<|im_start|>user\n",
            "Oké, tof. En nu ben ik ook benieuwd naar iets anders. Stel dat ik die hover-functie heb aanstaan, heeft dit dan invloed op hoe snel mijn pagina laadt?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Het toevoegen van de functie om bij hover een secundaire afbeelding te tonen kan enige invloed hebben op de laadtijd van een pagina, voornamelijk als de afbeeldingsbestanden groot zijn. Echter, dit soort functies zijn normaal gesproken geoptimaliseerd om de impact op de laadtijd te minimaliseren. Het is wel belangrijk om ervoor te zorgen dat de gebruikte afbeeldingen geoptimaliseerd zijn voor het web, om de beste laadtijden te garanderen.<|im_end|>\n",
            "<|im_start|>user\n",
            "Interessant, ik zal ervoor zorgen dat mijn afbeeldingen geoptimaliseerd zijn dan. Kan ik ook nog iets toevoegen zodat de klanten de afbeeldingen in een lichtbox kunnen zien als ze erop klikken?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Veel thema's bieden de mogelijkheid om een lichtbox-functie te activeren. Dit laat klanten toe om op de afbeelding te klikken en deze in een groter formaat te bekijken zonder de pagina te verlaten. Of uw thema deze functie heeft, kunt u controleren in de thema-instellingen onder de productpagina opties. Zoek naar 'lichtbox' of iets vergelijkbaars in de omschrijving van de instelmogelijkheden.<|im_end|>\n",
            "<|im_start|>user\n",
            "Perfect, dan ga ik dat ook nog instellen. Nu iets heel anders, hoe zou ik het beste social media kunnen inzetten voor meer verkeer naar mijn website?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Om social media effectief te gebruiken voor meer websiteverkeer, kunt u overwegen om regelmatig content te delen die waarde biedt aan uw volgers, zoals promoties, informatieve blogposts, of interactieve content zoals wedstrijden. Het vormen van relaties met influencers of andere merken voor samenwerkingen kan ook helpen. Vergeet niet om directe links naar uw website of specifieke productpagina's te plaatsen om het bezoekers gemakkelijk te maken uw website te vinden.\n"
          ]
        }
      ],
      "source": [
        "# Show sample\n",
        "item_data = train_data1[0]\n",
        "chat_template_string = tokenizer.apply_chat_template(item_data[\"messages\"], tokenize = False)\n",
        "print(chat_template_string)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poEgM4M-h3yU"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Steps\n",
        "eval_steps = 146\n",
        "save_steps = 146\n",
        "logging_steps = 73\n",
        "\n",
        "# Set TrainingArguments\n",
        "training_args = TrainingArguments(num_train_epochs = 1,\n",
        "                                  max_steps = 1466,\n",
        "                                  learning_rate = 3.0e-4,\n",
        "                                  lr_scheduler_type = 'cosine',\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 = 1,\n",
        "                                  per_device_train_batch_size = 2,\n",
        "                                  per_device_eval_batch_size = 4,\n",
        "                                  gradient_accumulation_steps = 32,\n",
        "                                  gradient_checkpointing = True,\n",
        "                                  gradient_checkpointing_kwargs = {'use_reentrant': False},\n",
        "                                  warmup_ratio = 0.05,\n",
        "                                  weight_decay = 0.01,\n",
        "                                  bf16 = True,\n",
        "                                  tf32 = True,\n",
        "                                  output_dir = f'{WORK_DIR}{hf_model_name}',\n",
        "                                  hub_model_id = hf_model_name,\n",
        "                                  push_to_hub = True,\n",
        "                                  hub_private_repo = True,\n",
        "                                  optim = 'paged_adamw_8bit',\n",
        "                                  report_to = 'tensorboard')\n",
        "\n",
        "# Config SFTTrainer\n",
        "trainer = SFTTrainer(model,\n",
        "                     train_dataset = train_data2,\n",
        "                     eval_dataset = test_data,\n",
        "                     tokenizer = tokenizer,\n",
        "                     packing = True,\n",
        "                     eval_packing = False,\n",
        "                     max_seq_length = MAX_LEN,\n",
        "                     data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False),\n",
        "                     args = training_args)\n",
        "\n",
        "# Perform Training\n",
        "trainer.train(resume_from_checkpoint = True)\n"
      ],
      "metadata": {
        "id": "VY9HJotbvVso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523,
          "referenced_widgets": [
            "871b187fac594d42ac2ca132d646affc",
            "67a20209c0424acc97303e01e5c04658",
            "813caa6abf13415ba8cbee96fd6ef16b",
            "2ec7c5bb5ac840f0b802ba7d88512572",
            "a54034786c7a4727b5d488ea4b5a431f",
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            "ca4cccc02a3c47ec98568f7e2fbff574",
            "951664b5537849ff88af3763946435e6",
            "0cff2b3d39c643f59cd509016d974647"
          ]
        },
        "outputId": "b3aeabb3-db9f-4b5a-95b9-fded8ee17b0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "871b187fac594d42ac2ca132d646affc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:341: UserWarning: You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1247' max='1466' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1247/1466 9:54:44 < 4:12:54, 0.01 it/s, Epoch 1.70/2]\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>876</td>\n",
              "      <td>1.238800</td>\n",
              "      <td>1.199769</td>\n",
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              "      <td>1022</td>\n",
              "      <td>1.224600</td>\n",
              "      <td>1.188210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1168</td>\n",
              "      <td>1.211000</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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              "<IPython.core.display.HTML object>"
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        {
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1466' max='1466' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1466/1466 14:14:09, Epoch 1/2]\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>876</td>\n",
              "      <td>1.238800</td>\n",
              "      <td>1.199769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1022</td>\n",
              "      <td>1.224600</td>\n",
              "      <td>1.188210</td>\n",
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              "    <tr>\n",
              "      <td>1168</td>\n",
              "      <td>1.211000</td>\n",
              "      <td>1.180156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1314</td>\n",
              "      <td>1.204000</td>\n",
              "      <td>1.176272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1460</td>\n",
              "      <td>1.204100</td>\n",
              "      <td>1.175572</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          },
          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1466, training_loss=0.6111853034889877, metrics={'train_runtime': 51317.7302, 'train_samples_per_second': 1.828, 'train_steps_per_second': 0.029, 'total_flos': 8.368856319431541e+18, 'train_loss': 0.6111853034889877, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjTQt-aIh3yU"
      },
      "source": [
        "## Push to Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x1pnQQJih3yU",
        "colab": {
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              "adapter_model.safetensors:   0%|          | 0.00/640M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "634968f4db83474581e2960cbc6c13a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat-Sft/commit/116810213ea3eb70828cc62d607742f3b36bc902', commit_message='End of training', commit_description='', oid='116810213ea3eb70828cc62d607742f3b36bc902', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Push tokenizer to hub\n",
        "tokenizer.push_to_hub(hf_model_name, private = True)\n",
        "\n",
        "# Push model to hub\n",
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merge Model and Push to Hub"
      ],
      "metadata": {
        "id": "ZCvkZZsZln0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Name Constants\n",
        "model_name = f'robinsmits/{hf_model_name}'\n",
        "merged_model_name = f'{model_name}-Bf16'\n",
        "\n",
        "# Summary\n",
        "print(model_name)\n",
        "print(merged_model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOXICUWAmoz9",
        "outputId": "f2108be8-7521-4a99-fa5d-45224685efff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "robinsmits/Qwen1.5-7B-Dutch-Chat-Sft\n",
            "robinsmits/Qwen1.5-7B-Dutch-Chat-Sft-Bf16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleanup\n",
        "del model, tokenizer\n",
        "\n",
        "# Load from Hub\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Create Model - Dtype bfloat16\n",
        "model = AutoPeftModelForCausalLM.from_pretrained(model_name,\n",
        "                                                 torch_dtype = torch.bfloat16)\n",
        "\n",
        "# Merge and Unload\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Summary\n",
        "print(model)"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
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              "special_tokens_map.json:   0%|          | 0.00/367 [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "adapter_config.json:   0%|          | 0.00/694 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/640M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Qwen2ForCausalLM(\n",
            "  (model): Qwen2Model(\n",
            "    (embed_tokens): Embedding(151646, 4096)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x Qwen2DecoderLayer(\n",
            "        (self_attn): Qwen2SdpaAttention(\n",
            "          (q_proj): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "          (k_proj): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "          (v_proj): Linear(in_features=4096, out_features=4096, bias=True)\n",
            "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): Qwen2RotaryEmbedding()\n",
            "        )\n",
            "        (mlp): Qwen2MLP(\n",
            "          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
            "          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
            "          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
            "          (act_fn): SiLU()\n",
            "        )\n",
            "        (input_layernorm): Qwen2RMSNorm()\n",
            "        (post_attention_layernorm): Qwen2RMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): Qwen2RMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=151646, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
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        "# Push To Hub\n",
        "tokenizer.push_to_hub(merged_model_name, private = True)\n",
        "model.push_to_hub(merged_model_name, private = True)"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
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