Qwen1_5_7B_Dutch_Chat_DPO.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_DPO.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 DPO Allignment of the QLoRA adapter model [](https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat-Sft) which in turn is based on 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 and allign the Qwen1.5-7B-Chat model and optimize it for Dutch.\n",
        "\n",
        "The dataset used is the Dutch DPO Allignment Chat Dataset [BramVanroy/ultra_feedback_dutch_cleaned](https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch_cleaned) as created by Bram van Roy. Kudos to Bram for this dataset!\n"
      ],
      "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": "7eb1081d-f70d-4f28-de2d-ce5962c1b48d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7R3VLs6PHZQX"
      },
      "outputs": [],
      "source": [
        "# Import Modules\n",
        "from datasets import load_dataset\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import PeftModel, AutoPeftModelForCausalLM\n",
        "from transformers import (AutoTokenizer,\n",
        "                          AutoModelForCausalLM,\n",
        "                          BitsAndBytesConfig,\n",
        "                          TrainingArguments)\n",
        "import torch\n",
        "from trl import DPOTrainer\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",
        "base_model_name = 'Qwen/Qwen1.5-7B-Chat'\n",
        "sft_model_name = 'robinsmits/Qwen1.5-7B-Dutch-Chat-Sft'\n",
        "dpo_model_name = 'Qwen1.5-7B-Dutch-Chat-Dpo'\n",
        "dpo_merged_model_name = 'Qwen1.5-7B-Dutch-Chat'"
      ]
    },
    {
      "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": {
        "id": "adS3GcNGolT0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bb1d53a-125a-4f0d-e2da-2405364a0fd9"
      },
      "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": 145,
          "referenced_widgets": [
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            "f8028012a7c94d2b9b25118dc88623af"
          ]
        },
        "id": "HHeHs7goh3yP",
        "outputId": "6c474cf8-b845-465d-8288-db13a2ba363d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b20171ca8da4a62b764a9ff2ac47242"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# HuggingFace Hub Login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xED9rD-2h3yQ"
      },
      "source": [
        "## Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewyYBs9-h3yR",
        "outputId": "4132b8d8-f19b-4bb7-8b8a-1321a75df9f7"
      },
      "outputs": [
        {
          "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='robinsmits/Qwen1.5-7B-Dutch-Chat-Sft', vocab_size=151643, model_max_length=32768, is_fast=True, padding_side='right', truncation_side='left', special_tokens={'bos_token': '<|im_end|>', 'eos_token': '<|im_end|>', 'pad_token': '<|im_end|>', '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(sft_model_name)\n",
        "\n",
        "# Set Tokenizer Settings\n",
        "tokenizer.truncation_side = \"left\"\n",
        "tokenizer.add_special_tokens({\"bos_token\": tokenizer.eos_token})\n",
        "tokenizer.bos_token_id = tokenizer.eos_token_id\n",
        "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Tokenizer Summary\n",
        "print(tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fo0E4IRFh3yR"
      },
      "source": [
        "## Create QLoRa Model based on Qwen1.5_7B_Dutch_Chat_Sft Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 1000,
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        "id": "BtQrABkuh3yR",
        "outputId": "70af5124-1267-4ac2-cc89-57568580b205"
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      "outputs": [
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          "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 BitsAndBytesConfig\n",
        "bnb_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",
        "# Create Base Model\n",
        "model = AutoModelForCausalLM.from_pretrained(base_model_name,\n",
        "                                             quantization_config = bnb_config,\n",
        "                                             torch_dtype = torch.bfloat16,\n",
        "                                             device_map = 'auto')\n",
        "\n",
        "# Set cache to False\n",
        "model.config.use_cache = False\n",
        "\n",
        "# Load Adapter\n",
        "model = PeftModel.from_pretrained(model,\n",
        "                                  sft_model_name,\n",
        "                                  is_trainable = True)\n",
        "\n",
        "# Show Model Parameter Count\n",
        "model.print_trainable_parameters()\n",
        "\n",
        "# Show Model Summary\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Add Adapter for DPO Reference"
      ],
      "metadata": {
        "id": "OveuUOOLrBVi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add Adapter for DPO Reference\n",
        "model.load_adapter(sft_model_name,\n",
        "                   adapter_name = 'reference')\n",
        "\n",
        "# Show Model Parameter Count\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEhcBNIpqxDf",
        "outputId": "39050583-2225-4f98-cb97-eafcdbacfd3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 159,907,840 || all params: 8,041,140,224 || trainable%: 1.9886214584684252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gMIhn1Nh3yS"
      },
      "source": [
        "## Show Dutch Chat template"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "kOn0_PLCh3yT",
        "outputId": "e735d8f5-4763-48f8-b16a-10039f37f92f"
      },
      "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": 10
        }
      ],
      "source": [
        "# Summary Chat Template\n",
        "tokenizer.chat_template"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load DPO Dataset\n",
        "\n",
        "For DPO allignment I used the 'dpo_hq' subset as it is further cleaned. I also tried out the 'dpo_all' subset. I didn't really notice much difference when using the models that were trained on them.\n",
        "\n",
        "However for safety and probably best allignment quality I will only use and publish the model based on the 'dpo_hq' subset."
      ],
      "metadata": {
        "id": "Z0QxjiyLb32-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
            "07f27b7f6dd84270b2b9c8f70ac2e667",
            "41dcf56dcdc8416e87862b01cc648b74",
            "c2fb6f2b81804e71a548c54c66131cfc",
            "5a256bf3d85448ce90a51d52e66a07ea",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/9.20k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train_prefs split:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_prefs split:   0%|          | 0/517 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "4838be8b0ffe4ddaa5a576dac5f3d9d7"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['prompt', 'chosen', 'rejected'],\n",
            "    num_rows: 9815\n",
            "})\n",
            "Dataset({\n",
            "    features: ['prompt', 'chosen', 'rejected'],\n",
            "    num_rows: 517\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Load Dataset\n",
        "dataset = load_dataset('BramVanroy/ultra_feedback_dutch_cleaned', 'dpo_hq')\n",
        "\n",
        "# Split Datasets\n",
        "train_dataset = dataset['train_prefs']\n",
        "val_dataset = dataset['test_prefs']\n",
        "\n",
        "# Summary\n",
        "print(train_dataset)\n",
        "print(val_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Used and modified from HuggingFace Alignment Handbook\n",
        "def apply_chat_template(example, tokenizer):\n",
        "    if all(k in example.keys() for k in (\"chosen\", \"rejected\")):\n",
        "        # For DPO, the inputs are triples of (prompt, chosen, rejected), where `chosen` and `rejected` are the final turn of a dialogue\n",
        "        # We therefore need to extract the N-1 turns to form the prompt\n",
        "        prompt_messages = example[\"chosen\"][:-1]\n",
        "\n",
        "        # Now we extract the final turn to define chosen/rejected responses\n",
        "        chosen_messages = example[\"chosen\"][-1:]\n",
        "        rejected_messages = example[\"rejected\"][-1:]\n",
        "        example[\"text_chosen\"] = tokenizer.apply_chat_template(chosen_messages, tokenize=False)\n",
        "        example[\"text_rejected\"] = tokenizer.apply_chat_template(rejected_messages, tokenize=False)\n",
        "        example[\"text_prompt\"] = tokenizer.apply_chat_template(prompt_messages, tokenize=False)\n",
        "\n",
        "    return example"
      ],
      "metadata": {
        "id": "5MlD31mhrkUP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get Original columns\n",
        "original_columns = train_dataset.column_names\n",
        "\n",
        "# Proces Train Dataset\n",
        "train_dataset = train_dataset.map(apply_chat_template,\n",
        "                                  fn_kwargs = {\"tokenizer\": tokenizer},\n",
        "                                  remove_columns = original_columns)\n",
        "\n",
        "# Proces Validation Dataset\n",
        "val_dataset = val_dataset.map(apply_chat_template,\n",
        "                              fn_kwargs = {\"tokenizer\": tokenizer},\n",
        "                              remove_columns = original_columns)\n",
        "\n",
        "# Rename columns\n",
        "train_dataset = train_dataset.rename_columns({\"text_prompt\": \"prompt\", \"text_chosen\": \"chosen\", \"text_rejected\": \"rejected\"})\n",
        "val_dataset = val_dataset.rename_columns({\"text_prompt\": \"prompt\", \"text_chosen\": \"chosen\", \"text_rejected\": \"rejected\"})\n",
        "\n",
        "# Summary\n",
        "print(train_dataset)\n",
        "print(val_dataset)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 220,
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        "outputId": "3f33a496-6a07-4b11-a4b0-83e3d4abd275"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['chosen', 'rejected', 'prompt'],\n",
            "    num_rows: 9815\n",
            "})\n",
            "Dataset({\n",
            "    features: ['chosen', 'rejected', 'prompt'],\n",
            "    num_rows: 517\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poEgM4M-h3yU"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Steps\n",
        "eval_steps = 30\n",
        "save_steps = 30\n",
        "logging_steps = 15\n",
        "\n",
        "# DPO Training Arguments\n",
        "training_args = TrainingArguments(num_train_epochs = 1,\n",
        "                                  learning_rate = 1.0e-5,\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 = 1,\n",
        "                                  per_device_eval_batch_size = 2,\n",
        "                                  gradient_accumulation_steps = 32,\n",
        "                                  gradient_checkpointing = True,\n",
        "                                  gradient_checkpointing_kwargs = {'use_reentrant': False},\n",
        "                                  warmup_ratio = 0.05,\n",
        "                                  bf16 = True,\n",
        "                                  tf32 = True,\n",
        "                                  output_dir = dpo_model_name,\n",
        "                                  hub_model_id = dpo_model_name,\n",
        "                                  remove_unused_columns = False,\n",
        "                                  push_to_hub = True,\n",
        "                                  hub_private_repo = True,\n",
        "                                  optim = 'paged_adamw_8bit',\n",
        "                                  report_to = 'tensorboard')\n",
        "\n",
        "# Config DPOTrainer\n",
        "dpo_trainer = DPOTrainer(model,\n",
        "                         args = training_args,\n",
        "                         beta = 0.05,\n",
        "                         max_length = 1536,\n",
        "                         max_prompt_length = 1024,\n",
        "                         train_dataset = train_dataset,\n",
        "                         eval_dataset = val_dataset,\n",
        "                         tokenizer = tokenizer,\n",
        "                         model_adapter_name = 'default',\n",
        "                         ref_adapter_name = 'reference')\n",
        "# Train DPO Model\n",
        "dpo_trainer.train()"
      ],
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        "id": "VY9HJotbvVso",
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        {
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              "Map:   0%|          | 0/9815 [00:00<?, ? examples/s]"
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        {
          "metadata": {
            "tags": null
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
          ]
        },
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              "      [219/306 2:34:26 < 1:01:55, 0.02 it/s, Epoch 0.71/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rewards/chosen</th>\n",
              "      <th>Rewards/rejected</th>\n",
              "      <th>Rewards/accuracies</th>\n",
              "      <th>Rewards/margins</th>\n",
              "      <th>Logps/rejected</th>\n",
              "      <th>Logps/chosen</th>\n",
              "      <th>Logits/rejected</th>\n",
              "      <th>Logits/chosen</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.550300</td>\n",
              "      <td>0.468360</td>\n",
              "      <td>-0.043876</td>\n",
              "      <td>-0.629464</td>\n",
              "      <td>0.891892</td>\n",
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              "      <td>-0.933545</td>\n",
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              "    <tr>\n",
              "      <td>60</td>\n",
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              "      <td>-1.476905</td>\n",
              "      <td>0.901544</td>\n",
              "      <td>1.105578</td>\n",
              "      <td>-854.900024</td>\n",
              "      <td>-776.359375</td>\n",
              "      <td>-0.876791</td>\n",
              "      <td>-0.827572</td>\n",
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              "      <td>0.326400</td>\n",
              "      <td>0.314266</td>\n",
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              "      <td>-1.873036</td>\n",
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              "      <td>-0.842782</td>\n",
              "      <td>-0.792899</td>\n",
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              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.299900</td>\n",
              "      <td>0.288490</td>\n",
              "      <td>-0.683218</td>\n",
              "      <td>-2.311808</td>\n",
              "      <td>0.915058</td>\n",
              "      <td>1.628590</td>\n",
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              "      <td>-0.825997</td>\n",
              "      <td>-0.772953</td>\n",
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              "      <td>-2.490368</td>\n",
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              "      <td>1.766443</td>\n",
              "      <td>-875.169250</td>\n",
              "      <td>-783.411255</td>\n",
              "      <td>-0.823463</td>\n",
              "      <td>-0.767826</td>\n",
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              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.335400</td>\n",
              "      <td>0.268453</td>\n",
              "      <td>-0.677479</td>\n",
              "      <td>-2.485877</td>\n",
              "      <td>0.916988</td>\n",
              "      <td>1.808398</td>\n",
              "      <td>-875.079529</td>\n",
              "      <td>-782.482422</td>\n",
              "      <td>-0.813010</td>\n",
              "      <td>-0.757381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>0.284800</td>\n",
              "      <td>0.265212</td>\n",
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              "      <td>-2.569226</td>\n",
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              "    <div>\n",
              "      \n",
              "      <progress value='306' max='306' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [306/306 3:37:30, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rewards/chosen</th>\n",
              "      <th>Rewards/rejected</th>\n",
              "      <th>Rewards/accuracies</th>\n",
              "      <th>Rewards/margins</th>\n",
              "      <th>Logps/rejected</th>\n",
              "      <th>Logps/chosen</th>\n",
              "      <th>Logits/rejected</th>\n",
              "      <th>Logits/chosen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.550300</td>\n",
              "      <td>0.468360</td>\n",
              "      <td>-0.043876</td>\n",
              "      <td>-0.629464</td>\n",
              "      <td>0.891892</td>\n",
              "      <td>0.585588</td>\n",
              "      <td>-837.951294</td>\n",
              "      <td>-769.810303</td>\n",
              "      <td>-0.933545</td>\n",
              "      <td>-0.889388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.417800</td>\n",
              "      <td>0.356786</td>\n",
              "      <td>-0.371327</td>\n",
              "      <td>-1.476905</td>\n",
              "      <td>0.901544</td>\n",
              "      <td>1.105578</td>\n",
              "      <td>-854.900024</td>\n",
              "      <td>-776.359375</td>\n",
              "      <td>-0.876791</td>\n",
              "      <td>-0.827572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.326400</td>\n",
              "      <td>0.314266</td>\n",
              "      <td>-0.489312</td>\n",
              "      <td>-1.873036</td>\n",
              "      <td>0.915058</td>\n",
              "      <td>1.383724</td>\n",
              "      <td>-862.822754</td>\n",
              "      <td>-778.719055</td>\n",
              "      <td>-0.842782</td>\n",
              "      <td>-0.792899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.299900</td>\n",
              "      <td>0.288490</td>\n",
              "      <td>-0.683218</td>\n",
              "      <td>-2.311808</td>\n",
              "      <td>0.915058</td>\n",
              "      <td>1.628590</td>\n",
              "      <td>-871.598145</td>\n",
              "      <td>-782.597107</td>\n",
              "      <td>-0.825997</td>\n",
              "      <td>-0.772953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.274907</td>\n",
              "      <td>-0.723924</td>\n",
              "      <td>-2.490368</td>\n",
              "      <td>0.918919</td>\n",
              "      <td>1.766443</td>\n",
              "      <td>-875.169250</td>\n",
              "      <td>-783.411255</td>\n",
              "      <td>-0.823463</td>\n",
              "      <td>-0.767826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.335400</td>\n",
              "      <td>0.268453</td>\n",
              "      <td>-0.677479</td>\n",
              "      <td>-2.485877</td>\n",
              "      <td>0.916988</td>\n",
              "      <td>1.808398</td>\n",
              "      <td>-875.079529</td>\n",
              "      <td>-782.482422</td>\n",
              "      <td>-0.813010</td>\n",
              "      <td>-0.757381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>0.284800</td>\n",
              "      <td>0.265212</td>\n",
              "      <td>-0.715690</td>\n",
              "      <td>-2.569226</td>\n",
              "      <td>0.913127</td>\n",
              "      <td>1.853536</td>\n",
              "      <td>-876.746521</td>\n",
              "      <td>-783.246643</td>\n",
              "      <td>-0.815739</td>\n",
              "      <td>-0.758648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>0.343700</td>\n",
              "      <td>0.262121</td>\n",
              "      <td>-0.723330</td>\n",
              "      <td>-2.609051</td>\n",
              "      <td>0.915058</td>\n",
              "      <td>1.885720</td>\n",
              "      <td>-877.543030</td>\n",
              "      <td>-783.399353</td>\n",
              "      <td>-0.813835</td>\n",
              "      <td>-0.756063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>0.265500</td>\n",
              "      <td>0.261088</td>\n",
              "      <td>-0.718335</td>\n",
              "      <td>-2.615443</td>\n",
              "      <td>0.915058</td>\n",
              "      <td>1.897108</td>\n",
              "      <td>-877.670837</td>\n",
              "      <td>-783.299500</td>\n",
              "      <td>-0.810576</td>\n",
              "      <td>-0.752410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.344200</td>\n",
              "      <td>0.260963</td>\n",
              "      <td>-0.724767</td>\n",
              "      <td>-2.622410</td>\n",
              "      <td>0.916988</td>\n",
              "      <td>1.897643</td>\n",
              "      <td>-877.810181</td>\n",
              "      <td>-783.428162</td>\n",
              "      <td>-0.810977</td>\n",
              "      <td>-0.752830</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=306, training_loss=0.3584630991898331, metrics={'train_runtime': 13085.7986, 'train_samples_per_second': 0.75, 'train_steps_per_second': 0.023, 'total_flos': 0.0, 'train_loss': 0.3584630991898331, 'epoch': 1.0})"
            ]
          },
          "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": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
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        },
        "outputId": "6c934e65-bc59-401a-affb-83715463bf89"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/640M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/640M [00:00<?, ?B/s]"
            ],
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        {
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              "events.out.tfevents.1711704394.2ffe06bbd684.4391.0:   0%|          | 0.00/26.5k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 3 LFS files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/robinsmits/Qwen1.5-7B-Dutch-Chat-Dpo/commit/b7e48de00d682165656696a8c5b7a55456345232', commit_message='End of training', commit_description='', oid='b7e48de00d682165656696a8c5b7a55456345232', 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(dpo_model_name, private = True)\n",
        "\n",
        "# Push model to hub\n",
        "dpo_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/{dpo_model_name}'\n",
        "merged_model_name = f'robinsmits/{dpo_merged_model_name}'\n",
        "\n",
        "# Summary\n",
        "print(model_name)\n",
        "print(merged_model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOXICUWAmoz9",
        "outputId": "f29f80eb-35c6-42ca-88cc-1b740ebf5f9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "robinsmits/Qwen1.5-7B-Dutch-Chat-Dpo\n",
            "robinsmits/Qwen1.5-7B-Dutch-Chat\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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791,
          "referenced_widgets": [
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        "id": "lSp1b1q7mLRR",
        "outputId": "17563840-b4d0-478d-a484-21da2435a249"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
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            "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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