notebooks-finetuning-models/02_finetune_v3_malaysian_mistral_7b_32k_instructions_v4.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wanadzhar913/aitinkerers-hackathon-supa-team-werecooked/blob/master/notebooks-finetuning-models/02_finetune_v3_malaysian_mistral_7b_32k_instructions_v4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "In this notebook, we finetune [mesolitica/malaysian-mistral-7b-32k-instructions-v4](https://huggingface.co/mesolitica/malaysian-mistral-7b-32k-instructions-v4). We finetune primarily for a **Natural language inference (NLI)** and **reasoning** task. In our case, NLI is the task of determining whether a \"hypothesis\" is true (*entailment*) or false (*contradiction*) given a question-statement pair, as well as providing step-by-step reasoning for their choice. We select this model primarily due to it's:\n",
        "- **Context length of 32,000.** This refers to the maximum number of tokens (including words, punctuation, and spaces) that the model can consider at once during input processing. A high context length is important since we'll be doing NLI for text pairs of various length.\n",
        "- **No. of monthly downloads on HuggingFace.** The consistently high num. of downloads on a monthly basis is a good proxy for model quality.\n",
        "- **Good ability to comprehend Malay and English texts**, and reply in Malay due to being Instruction-finetuned beforehand.\n",
        "\n",
        "Overall, solely training on the [Boolq-Malay-With-Chain-of-Thought](https://huggingface.co/datasets/wanadzhar913/boolq-malay-with-chain-of-thought) dataset. It is comprised of both Malay and English versions of the original [Boolq](https://huggingface.co/datasets/google/boolq) dataset, as well a OpenAI 4o-mini generated Chain-of-Thought reasoning column.\n",
        "\n",
        "we use the following training parameters and obtain the following training results:\n",
        "\n",
        "- **No. of Epochs:** 1\n",
        "- **Per Device Train Batch Size:** 8\n",
        "- **Gradient Accumulation Steps:** 1\n",
        "- **LoRA Rank:** 64\n",
        "- **Learning Rate:** 2e-4\n",
        "- **Learning Rate Scheduler Type:** constant\n",
        "- **Maximum Sequence Lenght:** 32768\n",
        "- **Load model in 4-bit Precision:** True\n",
        "- **bf16 (Brain Floating Point 16-bit):** False\n",
        "- **Train Loss:** 0.3057\n",
        "\n",
        "The **model** can be found here: https://huggingface.co/wanadzhar913/malaysian-mistral-llmasajudge-v3\n",
        "\n",
        "The **Weights and Biases training run** can be found here: https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3\n",
        "\n",
        "For NLI benchmarks specifically, the **benchmarking notebook** can be found here: https://github.com/wanadzhar913/aitinkerers-hackathon-supa-team-werecooked/blob/master/notebooks-benchmarking-exercises/03_benchmark_malaysian_mistral_llmasajudge_v3.ipynb\n",
        "\n",
        "In the future, we can do the following to garner better results:\n",
        "- Set `bf16` parameter to `True` to optimize compute efficiency without significantly sacrificing model accuracy.\n",
        "- Increase the `gradient_accumulation_steps` to deal with the small GPU constraints or increase the `batch_size` if we've access to a larger GPU. The reasoning is mainly to avoid [Out of Memory Errors (OOM)](https://discuss.huggingface.co/t/batch-size-vs-gradient-accumulation/5260).\n",
        "- Given more compute resources, we can also increase our `patience` variable and train for more than 10 epochs.\n",
        "- **Limiting the reasoning portion (in the training dataset) to only be in Malay**. Since the model has been instruction finetuned to mainly reply in Malay, it'd be confusing to have it reason back in English.\n"
      ],
      "metadata": {
        "id": "7uuLyk3pDmux"
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      "cell_type": "code",
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        "!pip install datasets bitsandbytes peft trl wandb -q"
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m293.4/293.4 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import wandb\n",
        "# import huggingface_hub\n",
        "# import pandas as pd\n",
        "# import datasets\n",
        "# import torch\n",
        "# import bitsandbytes\n",
        "# import peft\n",
        "# import trl\n",
        "# import transformers\n",
        "\n",
        "# print(f\"WandB version: {wandb.__version__}\")\n",
        "# print(f\"Huggingface Hub version: {huggingface_hub.__version__}\")\n",
        "# print(f\"Pandas version: {pd.__version__}\")\n",
        "# print(f\"Datasets version: {datasets.__version__}\")\n",
        "# print(f\"Torch version: {torch.__version__}\")\n",
        "# print(f\"Bitsandbytes version: {bitsandbytes.__version__}\")\n",
        "# print(f\"Peft version: {peft.__version__}\")\n",
        "# print(f\"TRL version: {trl.__version__}\")\n",
        "# print(f\"Transformers version: {transformers.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D528IcxQChNk",
        "outputId": "b4134957-f93b-47c1-c5cd-6e0ba4c2c654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WandB version: 0.19.1\n",
            "Huggingface Hub version: 0.27.0\n",
            "Pandas version: 2.2.2\n",
            "Datasets version: 3.2.0\n",
            "Torch version: 2.5.1+cu121\n",
            "Bitsandbytes version: 0.45.0\n",
            "Peft version: 0.14.0\n",
            "TRL version: 0.13.0\n",
            "Transformers version: 4.47.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FQFv1CH8gVXC"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "import argparse\n",
        "from random import randint\n",
        "\n",
        "import wandb\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "import pandas as pd\n",
        "from datasets import Dataset, load_dataset\n",
        "\n",
        "import torch\n",
        "import bitsandbytes as bnb\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
        "from transformers.trainer_utils import get_last_checkpoint\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, \\\n",
        "                         BitsAndBytesConfig, TrainingArguments, \\\n",
        "                         logging, pipeline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"WANDB_PROJECT\"]=\"finetune-malaysian-mistral-llmasajudge-v3\""
      ],
      "metadata": {
        "id": "zi8uoSDGk2oy"
      },
      "execution_count": null,
      "outputs": []
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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        "!nvidia-smi"
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        "colab": {
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        "outputId": "a69a94a1-1e3d-41c3-bc4c-6483bc64af5c"
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      "execution_count": null,
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          "text": [
            "Tue Oct 22 17:12:36 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
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            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0              48W / 400W |      5MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.0 Load dataset and prepare the prompt input according to the Mistral format\n",
        "\n",
        "The [mesolitica/malaysian-mistral-7b-32k-instructions-v4](https://huggingface.co/mesolitica/malaysian-mistral-7b-32k-instructions-v4) is a conversational chat model meaning we can chat with it using the following prompt:\n",
        "\n",
        "> \\<s> [INST] User Instruction 1 [/INST] Model answer 1\\</s> [INST] User instruction 2 [/INST]\n",
        "\n",
        "For instruction fine-tuning, it is quite common to have two columns inside the dataset: one for the prompt & the other for the response."
      ],
      "metadata": {
        "id": "QxuPmtXddTXi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train = load_dataset(\"wanadzhar913/boolq-malay-with-chain-of-thought\", split='train')\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset_train)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286,
          "referenced_widgets": [
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            "a009383c3ac24347a984921448321073",
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            "3d50ce0b47de41a0a27d9924544e19aa",
            "18fbfe6099c440eca5b98c6d60eda970",
            "785f9c09f2ec4d99a1b61c8cc7677e56",
            "ace69edf8bf64c7a89659aa4d407c0e8"
          ]
        },
        "id": "KucwZrJowj2G",
        "outputId": "e2138d37-97a3-4960-a186-190a0818dfbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": [
              "boolq-with-reasoning-train.jsonl:   0%|          | 0.00/40.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae2057019815453e968cb53f061f1380"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "boolq-with-reasoning-val.jsonl:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d0caf1c2008486999f3933b343e1f35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/18851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c05f6d545d84f24981360dd17e3b045"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/6540 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b7bbfc0ce4f451db398b5a80a4a2a82"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 18851\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train[0].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSSRBGm7o_xe",
        "outputId": "ba15df83-7609-469a-c3a2-9f978b2afa10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['passage', 'question', 'answer', 'language', 'split', 'reasoning'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BWE5SkQUcNw7",
        "outputId": "0b6c004a-e8b1-4478-875f-1e444cfe6d0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'passage': 'The Bucks have won one league title (1971), two conference titles (1971 and 1974), and 13 division titles (1971--1974, 1976, 1980--1986, 2001). They have featured such notable players as Kareem Abdul-Jabbar, Sidney Moncrief, Oscar Robertson, Bob Dandridge, Bob Lanier, Glenn Robinson, Ray Allen, Sam Cassell, Junior Bridgeman, Michael Redd, Terry Cummings, Vin Baker, Jon McGlocklin, Marques Johnson, and Brian Winters.',\n",
              " 'question': 'have the milwaukee bucks ever won a championship',\n",
              " 'answer': 1,\n",
              " 'language': 'English',\n",
              " 'split': 'train',\n",
              " 'reasoning': 'To determine whether the statement \"have the Milwaukee Bucks ever won a championship\" is factually consistent with the provided passage, we can follow these steps:\\n\\n1. **Identify Key Information in the Passage**: The passage states that the Milwaukee Bucks have won \"one league title (1971).\" In the context of professional sports, a \"league title\" typically refers to a championship title. Therefore, this indicates that the Bucks have indeed won a championship.\\n\\n2. **Clarify Terminology**: The term \"championship\" in this context is synonymous with \"league title.\" The passage explicitly mentions that the Bucks won a league title in 1971, which is the same as saying they won a championship.\\n\\n3. **Assess the Statement**: The statement asks if the Milwaukee Bucks have ever won a championship. Given the information from the passage, we can confirm that they have won a championship (the league title in 1971).\\n\\n4. **Conclusion**: Since the passage provides clear evidence that the Milwaukee Bucks have won a championship, the statement is factually consistent with the information presented in the passage.\\n\\nThus, the output is:\\n```\\n1\\n```'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the create_prompt function\n",
        "def create_prompt(sample):\n",
        "    bos_token = \"<s>\"\n",
        "    eos_token = \"</s>\"\n",
        "\n",
        "    passage = sample['passage']\n",
        "    summary = sample['question']\n",
        "    answer = sample['answer']\n",
        "    reasoning = sample['reasoning']\n",
        "\n",
        "    text_row = f\"\"\"[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\n",
        "              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\n",
        "              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\n",
        "\n",
        "              Anda perlu memilih antara dua pilihan berikut:\n",
        "              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\n",
        "              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\n",
        "\n",
        "              Dokumen: {passage}\n",
        "              Soalan/Kenyataan: {summary}\n",
        "\n",
        "              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\n",
        "              Kembalikan jawapan (penjelasan dan konsisten/tak konsisten) dalam format JSON. Sebagai contoh: {{'reasoning': '...', 'consistency': 1}} atau {{'reasoning': '...', 'consistency': 0}}[/INST]\"\"\"\n",
        "\n",
        "    answer_row = f\"\"\"{{\"reasoning\": {reasoning}, \"consistency\": {answer}}}\"\"\"\n",
        "\n",
        "    sample[\"prompt\"] = bos_token + text_row\n",
        "    sample[\"completion\"] = answer_row + eos_token\n",
        "\n",
        "    return sample"
      ],
      "metadata": {
        "id": "xhDVquuxYg7M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_instruct_format_train = dataset_train.shuffle(seed=42).map(create_prompt, remove_columns=['passage','question','answer','language', 'split'])\n",
        "\n",
        "# print random sample\n",
        "dataset_instruct_format_train[randint(0, len(dataset_instruct_format_train))]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622,
          "referenced_widgets": [
            "2ac98f165ae24ca38b8ee845d8a899cf",
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            "4faf5b9e8f6c493ca92e85ccf430a012",
            "2206c94eeb154131b9511a3ba8382ecd",
            "25fe9ed22944404c907cb82353b125ba",
            "3ec4142e493744a5b1d7936d85de33e5",
            "e577072603984db8aaace903e6dab3c5",
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            "36b19f40aefc41d1b6ac218dcdd3028a"
          ]
        },
        "id": "gnJqmcC0b22U",
        "outputId": "d6a16bf2-0ca9-42b7-93dc-b09a1faa1bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/18851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ac98f165ae24ca38b8ee845d8a899cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'reasoning': 'Langkah-langkah untuk menentukan konsistensi fakta antara pernyataan dan petikan adalah seperti berikut:\\n\\n1. **Menganalisis Petikan**: Petikan menyatakan bahawa Asia Tenggara adalah subregion Asia yang terletak di selatan China, timur India, barat New Guinea, dan utara Australia. Ia juga menyebut sempadan Asia Tenggara dengan kawasan lain, termasuk Asia Selatan yang merangkumi India.\\n\\n2. **Memahami Sempadan Geografi**: Dalam petikan, dinyatakan bahawa Asia Tenggara bersempadan dengan Asia Selatan. Ini menunjukkan bahawa Asia Tenggara dan Asia Selatan adalah dua kawasan yang berbeza.\\n\\n3. **Menilai Pernyataan**: Pernyataan yang diberikan adalah \"adakah india sebahagian daripada asia tenggara\". Untuk menjawab soalan ini, kita perlu melihat sama ada India termasuk dalam kawasan Asia Tenggara.\\n\\n4. **Fakta Mengenai India**: India terletak di Asia Selatan, bukan di Asia Tenggara. Oleh itu, India tidak boleh dianggap sebagai sebahagian daripada Asia Tenggara.\\n\\n5. **Kesimpulan**: Berdasarkan analisis di atas, pernyataan bahawa India adalah sebahagian daripada Asia Tenggara adalah tidak konsisten dengan maklumat yang diberikan dalam petikan. Oleh itu, jawapan yang tepat adalah 0, yang menunjukkan bahawa pernyataan tersebut tidak konsisten dengan fakta yang ada.\\n\\nOleh itu, output yang diberikan adalah:\\n```\\n0\\n```',\n",
              " 'prompt': \"<s>[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\\n              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\\n              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\\n\\n              Anda perlu memilih antara dua pilihan berikut:\\n              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\\n              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\\n\\n              Dokumen: Asia Tenggara atau Asia Tenggara adalah subregion Asia, terdiri daripada negara-negara yang secara geografi terletak di selatan China, timur India, barat New Guinea dan utara Australia. Asia Tenggara bersempadan di utara dengan Asia Timur, di barat dengan Asia Selatan dan Teluk Bengal, di timur dengan Oceania dan Lautan Pasifik, dan di selatan dengan Australia dan Lautan Hindi. Wilayah ini adalah satu-satunya bahagian Asia yang terletak sebahagiannya dalam Hemisfera Selatan, walaupun majoritinya berada di Hemisfera Utara. Dalam definisi kontemporari, Asia Tenggara terdiri daripada dua kawasan geografi:\\n              Soalan/Kenyataan: adakah india sebahagian daripada asia tenggara\\n\\n              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\\n              Kembalikan jawapan (penjelasan dan konsisten/tak konsisten) dalam format JSON. Sebagai contoh: {'reasoning': '...', 'consistency': 1} atau {'reasoning': '...', 'consistency': 0}[/INST]\",\n",
              " 'completion': '{\"reasoning\": Langkah-langkah untuk menentukan konsistensi fakta antara pernyataan dan petikan adalah seperti berikut:\\n\\n1. **Menganalisis Petikan**: Petikan menyatakan bahawa Asia Tenggara adalah subregion Asia yang terletak di selatan China, timur India, barat New Guinea, dan utara Australia. Ia juga menyebut sempadan Asia Tenggara dengan kawasan lain, termasuk Asia Selatan yang merangkumi India.\\n\\n2. **Memahami Sempadan Geografi**: Dalam petikan, dinyatakan bahawa Asia Tenggara bersempadan dengan Asia Selatan. Ini menunjukkan bahawa Asia Tenggara dan Asia Selatan adalah dua kawasan yang berbeza.\\n\\n3. **Menilai Pernyataan**: Pernyataan yang diberikan adalah \"adakah india sebahagian daripada asia tenggara\". Untuk menjawab soalan ini, kita perlu melihat sama ada India termasuk dalam kawasan Asia Tenggara.\\n\\n4. **Fakta Mengenai India**: India terletak di Asia Selatan, bukan di Asia Tenggara. Oleh itu, India tidak boleh dianggap sebagai sebahagian daripada Asia Tenggara.\\n\\n5. **Kesimpulan**: Berdasarkan analisis di atas, pernyataan bahawa India adalah sebahagian daripada Asia Tenggara adalah tidak konsisten dengan maklumat yang diberikan dalam petikan. Oleh itu, jawapan yang tepat adalah 0, yang menunjukkan bahawa pernyataan tersebut tidak konsisten dengan fakta yang ada.\\n\\nOleh itu, output yang diberikan adalah:\\n```\\n0\\n```, \"consistency\": 0}</s>'}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.0 Prepare the configuration for training the LLM\n",
        "Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA https://huggingface.co/blog/4bit-transformers-bitsandbytes"
      ],
      "metadata": {
        "id": "uRzQJ7q6dH0P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = \"mesolitica/malaysian-mistral-7b-32k-instructions-v4\"\n",
        "new_model = \"malaysian-mistral-qlora-7b-32k-instructions-llmasajudge\" #set the name of the new model"
      ],
      "metadata": {
        "id": "xm1RSChgeEYZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "################################################################################\n",
        "# QLoRA parameters\n",
        "################################################################################\n",
        "\n",
        "# LoRA attention dimension\n",
        "lora_r = 64\n",
        "\n",
        "# Alpha parameter for LoRA scaling\n",
        "lora_alpha = 16\n",
        "\n",
        "# Dropout probability for LoRA layers\n",
        "lora_dropout = 0.1\n",
        "\n",
        "################################################################################\n",
        "# bitsandbytes parameters\n",
        "################################################################################\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"bfloat16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Activate nested quantization for 4-bit base models (double quantization)\n",
        "use_nested_quant = True\n",
        "\n",
        "\n",
        "################################################################################\n",
        "# TrainingArguments parameters\n",
        "################################################################################\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 1\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 8\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule (constant a bit better than cosine)\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 300\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 20\n",
        "\n",
        "################################################################################\n",
        "# SFT parameters\n",
        "################################################################################\n",
        "\n",
        "# Maximum sequence length to use\n",
        "max_seq_length = 32768\n",
        "\n",
        "# Maximum batch size\n",
        "# dataset_batch_size = 96\n",
        "\n",
        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
        "packing = False\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}\n",
        "#device_map = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "Pi51f722gXr_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the base model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        ")\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")\n",
        "\n",
        "base_model.config.use_cache = False\n",
        "base_model.config.pretraining_tp = 1\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = 'right'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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          "metadata": {}
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        {
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          "metadata": {}
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          "metadata": {}
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        {
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          "data": {
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            ],
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            }
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(base_model)"
      ],
      "metadata": {
        "id": "-CE-uVyNg-bh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df751109-348e-45a3-88e9-f9a4b4d0ecc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MistralForCausalLM(\n",
            "  (model): MistralModel(\n",
            "    (embed_tokens): Embedding(32000, 4096)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x MistralDecoderLayer(\n",
            "        (self_attn): MistralSdpaAttention(\n",
            "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): MistralRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): MistralMLP(\n",
            "          (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
            "          (act_fn): SiLU()\n",
            "        )\n",
            "        (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
            "        (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
            "      )\n",
            "    )\n",
            "    (norm): MistralRMSNorm((4096,), eps=1e-05)\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_all_linear_names(model):\n",
        "    lora_module_names = set()\n",
        "    for name, module in model.named_modules():\n",
        "        if isinstance(module, bnb.nn.Linear4bit):\n",
        "            names = name.split(\".\")\n",
        "            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
        "\n",
        "    if \"lm_head\" in lora_module_names:  # needed for 16-bit\n",
        "        lora_module_names.remove(\"lm_head\")\n",
        "    return list(lora_module_names)"
      ],
      "metadata": {
        "id": "if58KRPzg_sM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get lora target modules\n",
        "modules = find_all_linear_names(base_model)"
      ],
      "metadata": {
        "id": "4H6DUKwzhChI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(modules)"
      ],
      "metadata": {
        "id": "pQ0nB6_AhE3L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7b5e278-d7b1-42f6-e4c6-c0b937efe943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['v_proj', 'up_proj', 'gate_proj', 'k_proj', 'o_proj', 'down_proj', 'q_proj']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inference using base model only before fine tuning."
      ],
      "metadata": {
        "id": "6XpABwXAhTQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt = create_prompt(dataset_train[0])[\"prompt\"]\n",
        "print(eval_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmdKf9Bwq6gi",
        "outputId": "7e5c0e29-c417-40e0-c3f9-7fb21e6e537d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\n",
            "              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\n",
            "              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\n",
            "\n",
            "              Anda perlu memilih antara dua pilihan berikut:\n",
            "              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\n",
            "              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\n",
            "\n",
            "              Dokumen: The Bucks have won one league title (1971), two conference titles (1971 and 1974), and 13 division titles (1971--1974, 1976, 1980--1986, 2001). They have featured such notable players as Kareem Abdul-Jabbar, Sidney Moncrief, Oscar Robertson, Bob Dandridge, Bob Lanier, Glenn Robinson, Ray Allen, Sam Cassell, Junior Bridgeman, Michael Redd, Terry Cummings, Vin Baker, Jon McGlocklin, Marques Johnson, and Brian Winters.\n",
            "              Soalan/Kenyataan: have the milwaukee bucks ever won a championship\n",
            "\n",
            "              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\n",
            "              Kembalikan jawapan (penjelasan dan konsisten/tak konsisten) dalam format JSON. Sebagai contoh: {'reasoning': '...', 'consistency': 1} atau {'reasoning': '...', 'consistency': 0}[/INST]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "base_model.eval()\n",
        "with torch.no_grad():\n",
        "    print(tokenizer.decode(base_model.generate(**model_input, max_new_tokens=1024, pad_token_id=2)[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f0N25AthRYk",
        "outputId": "24c40074-542a-4bf6-9991-4bdaa394465c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\n",
            "              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\n",
            "              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\n",
            "\n",
            "              Anda perlu memilih antara dua pilihan berikut:\n",
            "              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\n",
            "              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\n",
            "\n",
            "              Dokumen: The Bucks have won one league title (1971), two conference titles (1971 and 1974), and 13 division titles (1971--1974, 1976, 1980--1986, 2001). They have featured such notable players as Kareem Abdul-Jabbar, Sidney Moncrief, Oscar Robertson, Bob Dandridge, Bob Lanier, Glenn Robinson, Ray Allen, Sam Cassell, Junior Bridgeman, Michael Redd, Terry Cummings, Vin Baker, Jon McGlocklin, Marques Johnson, and Brian Winters.\n",
            "              Soalan/Kenyataan: have the milwaukee bucks ever won a championship\n",
            "\n",
            "              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\n",
            "              Kembalikan jawapan (penjelasan dan konsisten/tak konsisten) dalam format JSON. Sebagai contoh: {'reasoning': '...', 'consistency': 1} atau {'reasoning': '...', 'consistency': 0}[/INST]Dokumen: The Bucks have won one league title (1971), two conference titles (1971 and 1974), and 13 division titles (1971--1974, 1976, 1980--1986, 2001). They have featured such notable players as Kareem Abdul-Jabbar, Sidney Moncrief, Oscar Robertson, Bob Dandridge, Bob Lanier, Glenn Robinson, Ray Allen, Sam Cassell, Junior Bridgeman, Michael Redd, Terry Cummings, Vin Baker, Jon McGlocklin, Marques Johnson, and Brian Winters.\n",
            "\n",
            "Soalan/Kenyataan: Adakah Milwaukee Bucks pernah memenangi kejuaraan?\n",
            "\n",
            "Langkah 1: Baca dokumen dengan teliti.\n",
            "\n",
            "Langkah 2: Analisis maklumat dalam dokumen.\n",
            "\n",
            "Langkah 3: Bandingkan maklumat dalam dokumen dengan soalan/kenyataan.\n",
            "\n",
            "Langkah 4: Kenal pasti ketidakkonsistenan fakta.\n",
            "\n",
            "Langkah 5: Pilih antara dua pilihan: Konsisten dengan Fakta atau Tidak Konsisten dengan Fakta.\n",
            "\n",
            "Langkah 6: Labelkan jawapan berdasarkan pilihan yang dipilih.\n",
            "\n",
            "Langkah 7: Kembalikan jawapan dalam format JSON.\n",
            "\n",
            "Jawapan:\n",
            "\n",
            "```json\n",
            "{\n",
            "  \"reasoning\": \"The Bucks have won one league title (1971), two conference titles (1971 and 1974), and 13 division titles (1971--1974, 1976, 1980--1986, 2001). They have featured such notable players as Kareem Abdul-Jabbar, Sidney Moncrief, Oscar Robertson, Bob Dandridge, Bob Lanier, Glenn Robinson, Ray Allen, Sam Cassell, Junior Bridgeman, Michael Redd, Terry Cummings, Vin Baker, Jon McGlocklin, Marques Johnson, and Brian Winters. However, the question/statement 'have the Milwaukee Bucks ever won a championship' is not supported by the information in the document.\",\n",
            "  \"consistency\": 0\n",
            "}\n",
            "```\n",
            "\n",
            "Jadi, jawapan untuk soalan/kenyataan \"have the Milwaukee Bucks ever won a championship\" adalah tidak konsisten dengan fakta berdasarkan maklumat dalam dokumen.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.0 Train the LLM\n",
        "\n",
        "Train on completions only https://huggingface.co/docs/trl/en/sft_trainer"
      ],
      "metadata": {
        "id": "lrY7qLw2iMbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set LoRA configuration\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    target_modules=[\n",
        "        \"q_proj\",\n",
        "        \"k_proj\",\n",
        "        \"v_proj\",\n",
        "        \"o_proj\",\n",
        "        \"gate_proj\",\n",
        "        \"up_proj\",\n",
        "        \"down_proj\",\n",
        "    ],\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    report_to=\"wandb\",  # enable logging to W&B\n",
        "    # run_name=f'{new_model} + v5',  # name of the W&B run (optional)\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    gradient_checkpointing=gradient_checkpointing,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    # max_steps=1000, # the total number of training steps to perform\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        ")"
      ],
      "metadata": {
        "id": "rKCfOONds8Zn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def formatting_prompts_func(example):\n",
        "    output_texts = []\n",
        "    for i in range(len(example['prompt'])):\n",
        "        text = f\"{example['prompt'][i]}\\n\\n ### Jawapan: {example['completion'][i]}\"\n",
        "        output_texts.append(text)\n",
        "    return output_texts\n",
        "\n",
        "response_template = \"### Jawapan:\"\n",
        "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "pGCosthliKSU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the SFTTrainer for fine-tuning\n",
        "trainer = SFTTrainer(\n",
        "    model=base_model,\n",
        "    train_dataset=dataset_instruct_format_train,\n",
        "    formatting_func=formatting_prompts_func,\n",
        "    data_collator=collator,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "33babf87947e44ccb7a8df89a8926905",
            "c006e38f536846a2b7cb3e818b7ac98a",
            "27f290593f4f45ad862dafd0fddafb0f",
            "d60218fec1d742cfb205dbd6407c89c8",
            "f99f49ac582845a39bdbe4c2e616c3af",
            "9bfde07c24d5411185d37b1cea01afe3",
            "e178918b79ce41ac8155c23b48a5a3fa",
            "4f518123137e46a490517cdd8639c980",
            "0560b6d106b249e79d8a6397692379bc",
            "1159df643c404ab1b063cfe8f58aa2e2",
            "a3b11ab7778042b1bacda441a0979924"
          ]
        },
        "id": "63fTNph4i1m7",
        "outputId": "c899629a-71c7-46d8-8380-061cb8842686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': max_seq_length. Will not be supported from version '1.0.0'.\n",
            "\n",
            "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:283: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/18851 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33babf87947e44ccb7a8df89a8926905"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Start the training process\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qcNtMq7Ki62w",
        "outputId": "67aaa59d-c694-4716-c9f7-402b989bc8de"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33madzhar-faiq\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.18.5"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20241022_171705-8ocdvq5y</code>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3/runs/8ocdvq5y' target=\"_blank\">./results</a></strong> to <a href='https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3' target=\"_blank\">https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3</a>"
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              "<IPython.core.display.HTML object>"
            ]
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          "data": {
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              " View run at <a href='https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3/runs/8ocdvq5y' target=\"_blank\">https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3/runs/8ocdvq5y</a>"
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='601' max='2357' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 601/2357 59:02 < 2:53:06, 0.17 it/s, Epoch 0.25/1]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.323800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.453300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.334900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.310900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.419600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.221000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.383400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.331900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.251700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.393700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>0.225700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>0.347600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>0.285300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>0.267500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.373200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>0.208000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>0.350500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>0.286800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>0.251700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.369100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>0.205400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>0.341800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>0.274600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>0.260900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.366400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>520</td>\n",
              "      <td>0.213900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>540</td>\n",
              "      <td>0.332200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>560</td>\n",
              "      <td>0.270900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>580</td>\n",
              "      <td>0.232400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2357' max='2357' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2357/2357 3:54:08, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.323800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.453300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.334900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.310900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.419600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.221000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.383400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.331900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.251700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.393700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>0.225700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>0.347600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>0.285300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>0.267500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.373200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>0.208000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>0.350500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>0.286800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>0.251700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.369100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>0.205400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>0.341800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>0.274600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>0.260900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.366400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>520</td>\n",
              "      <td>0.213900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>540</td>\n",
              "      <td>0.332200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>560</td>\n",
              "      <td>0.270900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>580</td>\n",
              "      <td>0.232400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.360700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>620</td>\n",
              "      <td>0.199900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>0.332300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>660</td>\n",
              "      <td>0.263600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>680</td>\n",
              "      <td>0.229800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.339800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>720</td>\n",
              "      <td>0.199600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>740</td>\n",
              "      <td>0.320500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>760</td>\n",
              "      <td>0.251400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>780</td>\n",
              "      <td>0.257800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.340400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>820</td>\n",
              "      <td>0.192700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>840</td>\n",
              "      <td>0.304000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>860</td>\n",
              "      <td>0.258900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>880</td>\n",
              "      <td>0.224900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.351400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>920</td>\n",
              "      <td>0.196900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>940</td>\n",
              "      <td>0.329400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>960</td>\n",
              "      <td>0.264000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>980</td>\n",
              "      <td>0.236900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.350600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1020</td>\n",
              "      <td>0.179500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1040</td>\n",
              "      <td>0.315700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1060</td>\n",
              "      <td>0.242200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1080</td>\n",
              "      <td>0.255100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.344400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1120</td>\n",
              "      <td>0.195700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1140</td>\n",
              "      <td>0.306000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1160</td>\n",
              "      <td>0.255200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1180</td>\n",
              "      <td>0.229400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.343100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1220</td>\n",
              "      <td>0.189300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1240</td>\n",
              "      <td>0.296600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1260</td>\n",
              "      <td>0.245400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1280</td>\n",
              "      <td>0.214500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.332100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1320</td>\n",
              "      <td>0.191300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1340</td>\n",
              "      <td>0.303900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1360</td>\n",
              "      <td>0.255400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1380</td>\n",
              "      <td>0.228900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.337400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1420</td>\n",
              "      <td>0.185700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1440</td>\n",
              "      <td>0.295900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1460</td>\n",
              "      <td>0.236900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1480</td>\n",
              "      <td>0.201400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.344300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1520</td>\n",
              "      <td>0.182800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1540</td>\n",
              "      <td>0.305900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1560</td>\n",
              "      <td>0.237600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1580</td>\n",
              "      <td>0.231300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.325300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1620</td>\n",
              "      <td>0.208000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1640</td>\n",
              "      <td>0.292200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1660</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1680</td>\n",
              "      <td>0.237100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.318200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1720</td>\n",
              "      <td>0.184900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1740</td>\n",
              "      <td>0.314900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1760</td>\n",
              "      <td>0.245300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1780</td>\n",
              "      <td>0.212500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.322100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1820</td>\n",
              "      <td>0.193200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1840</td>\n",
              "      <td>0.296900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1860</td>\n",
              "      <td>0.239900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1880</td>\n",
              "      <td>0.208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.320200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1920</td>\n",
              "      <td>0.192200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1940</td>\n",
              "      <td>0.288800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1960</td>\n",
              "      <td>0.243000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1980</td>\n",
              "      <td>0.225800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.296900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2020</td>\n",
              "      <td>0.198300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2040</td>\n",
              "      <td>0.286100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2060</td>\n",
              "      <td>0.247200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2080</td>\n",
              "      <td>0.198400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.317500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2120</td>\n",
              "      <td>0.194900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2140</td>\n",
              "      <td>0.287100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2160</td>\n",
              "      <td>0.247500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2180</td>\n",
              "      <td>0.211300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.304400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2220</td>\n",
              "      <td>0.171400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2240</td>\n",
              "      <td>0.293000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2260</td>\n",
              "      <td>0.252900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2280</td>\n",
              "      <td>0.200700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.311900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2320</td>\n",
              "      <td>0.184500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2340</td>\n",
              "      <td>0.305700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2357, training_loss=0.27187459419276383, metrics={'train_runtime': 14072.4905, 'train_samples_per_second': 1.34, 'train_steps_per_second': 0.167, 'total_flos': 9.064403773172122e+17, 'train_loss': 0.27187459419276383, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.finish()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "Tm-JAgsVy8xd",
        "outputId": "5f0c7306-63e5-4cec-93d3-d7e273f81b8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇███</td></tr><tr><td>train/global_step</td><td>▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇█████</td></tr><tr><td>train/grad_norm</td><td>▂▂▄▆▂▄▇▃▆▁▁▅▂▆▂▂▅▆▂▅▂▆▅▆▃▆▃▅▂▂▃▇▇▂▆▂▆█▂█</td></tr><tr><td>train/learning_rate</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/loss</td><td>█▂▇▇▃▂▄▆▆▃▃▂▄▆▅▃▆▂▆▅▆▆▁▅▃▂▄▃▃▅▂▄▃▃▄▅▂▅▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>total_flos</td><td>9.064403773172122e+17</td></tr><tr><td>train/epoch</td><td>1</td></tr><tr><td>train/global_step</td><td>2357</td></tr><tr><td>train/grad_norm</td><td>0.22363</td></tr><tr><td>train/learning_rate</td><td>0.0002</td></tr><tr><td>train/loss</td><td>0.3057</td></tr><tr><td>train_loss</td><td>0.27187</td></tr><tr><td>train_runtime</td><td>14072.4905</td></tr><tr><td>train_samples_per_second</td><td>1.34</td></tr><tr><td>train_steps_per_second</td><td>0.167</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">./results</strong> at: <a href='https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3/runs/8ocdvq5y' target=\"_blank\">https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3/runs/8ocdvq5y</a><br/> View project at: <a href='https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3' target=\"_blank\">https://wandb.ai/adzhar-faiq/finetune-malaysian-mistral-llmasajudge-v3</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20241022_171705-8ocdvq5y/logs</code>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the fine-tuned model\n",
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "k6Mwj223i7aN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = get_last_checkpoint('./results')\n",
        "checkpoint"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "nxydGqgpmhZY",
        "outputId": "f95dd875-542b-4c5d-b8d7-97ae2900987f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'./results/checkpoint-2357'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(checkpoint)\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0d0bae1f98c24647a7f4533d5bf3d886",
            "7c5c401ba2e648958e248c24e2019b27",
            "756cf1eaf994476f8d39db9a692fb0dd",
            "09b95c947575410a95bebeda7e43f610",
            "14c2166157fd4b919089d4c24a2402a0",
            "fe6e6eb152554b5fa68d0f06bf2ff540",
            "18da8497431c426d8e7ed33f47b167a9",
            "2ac6824e5f7e486cbd5a9fdf6887ca73",
            "22bbf89fda194e728df02a6984f2466d",
            "796099af51184460aa6534b20f6f5721",
            "04ae35e7fcd44e84ac8535dd0b1035d3"
          ]
        },
        "id": "htbbPR9dnNNI",
        "outputId": "b48ccd28-a7db-4f90-8fe6-caeaeaf86b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d0bae1f98c24647a7f4533d5bf3d886"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"loraadapter-malaysian-mistral-llmasajudge-v3\", safe_serialization = True)\n",
        "tokenizer.push_to_hub(\"loraadapter-malaysian-mistral-llmasajudge-v3\", safe_serialization = True)"
      ],
      "metadata": {
        "id": "K_lnuwotnUYe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.0 Merge the trained qlora into the base model"
      ],
      "metadata": {
        "id": "QCa348n_jwRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    low_cpu_mem_usage=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=device_map\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5a9d096851fd4495b74056c81c4d9a1e",
            "3337aa37187d4bc696981139d4bd268b",
            "1534bb5fdc5f4603b97cb342ea141a18",
            "671e17a6938841a5ac80d5744996bcf9",
            "c0c11235db284fe3b6ee93ca4853b7f9",
            "a44db8d57c284d9e82885edf4ea7e649",
            "1200b52308f540a093a97d0aa27283aa",
            "9922a9025c754aad819607f4bd59057c",
            "ade1f02163aa43a681ece9ceb6f37edd",
            "61596ed2e3f54bc8a0582c2711666130",
            "92b46e460d2b4287b3da961c83b8040a"
          ]
        },
        "id": "46bvz6evjxwY",
        "outputId": "ae13a796-1f83-4a7b-e181-04f65b0c6782"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a9d096851fd4495b74056c81c4d9a1e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TG9pJiuj0yi",
        "outputId": "4589b6aa-d9c0-430c-fa5b-5e9230d09ebf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MistralForCausalLM(\n",
              "  (model): MistralModel(\n",
              "    (embed_tokens): Embedding(32000, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x MistralDecoderLayer(\n",
              "        (self_attn): MistralSdpaAttention(\n",
              "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "          (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): MistralRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): MistralMLP(\n",
              "          (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "          (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "          (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "        (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "      )\n",
              "    )\n",
              "    (norm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model= PeftModel.from_pretrained(base_model, new_model)"
      ],
      "metadata": {
        "id": "9fyaFrnXj2-3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8zVYC0gj6wx",
        "outputId": "6fa2a7b8-c661-4625-d376-abe4b78b6943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): MistralForCausalLM(\n",
              "      (model): MistralModel(\n",
              "        (embed_tokens): Embedding(32000, 4096)\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x MistralDecoderLayer(\n",
              "            (self_attn): MistralSdpaAttention(\n",
              "              (q_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, 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",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, 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=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, 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=1024, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, 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",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (rotary_emb): MistralRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): MistralMLP(\n",
              "              (gate_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, 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=14336, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (up_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, 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=14336, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (down_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=14336, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Dropout(p=0.1, inplace=False)\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=14336, 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",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "            (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "          )\n",
              "        )\n",
              "        (norm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "      )\n",
              "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model= merged_model.merge_and_unload()"
      ],
      "metadata": {
        "id": "Tg6JxCtkj7dn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0RY3kzZH7gy",
        "outputId": "28625327-4bd7-4bda-df76-e8b15cfef23f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MistralForCausalLM(\n",
              "  (model): MistralModel(\n",
              "    (embed_tokens): Embedding(32000, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x MistralDecoderLayer(\n",
              "        (self_attn): MistralSdpaAttention(\n",
              "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "          (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): MistralRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): MistralMLP(\n",
              "          (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "          (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "          (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "        (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "      )\n",
              "    )\n",
              "    (norm): MistralRMSNorm((4096,), eps=1e-05)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.0 Upload model to HuggingFace"
      ],
      "metadata": {
        "id": "O85HvbhNkMRu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model.push_to_hub(\"malaysian-mistral-llmasajudge-v3\", safe_serialization = True)"
      ],
      "metadata": {
        "id": "av5yBMx-kQZh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197,
          "referenced_widgets": [
            "89de27dba52d4133988876efdbb88dbe",
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            "01b55105addf457693189882f430c7bf",
            "82c031dec3164eb0b59fb75008c0b702",
            "d0305b70a30d4150b1144e8e430be6bc",
            "09841d1288304c96a183091e7415e3c0"
          ]
        },
        "outputId": "b33162f2-ab8a-4ee2-9ec9-1c2741a5298f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89de27dba52d4133988876efdbb88dbe"
            }
          },
          "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,
              "model_id": "7ca706f360e5416084b37f089e4e5d51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00003-of-00003.safetensors:   0%|          | 0.00/4.54G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "699931378c2e4eefb19e1774c29c5f87"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00003.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15e3a55ae92f4738baeb98a4afa72661"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/wanadzhar913/malaysian-mistral-llmasajudge-v3/commit/19b1ee91d6ee18bf6b6b25a0780acd7814b2595a', commit_message='Upload MistralForCausalLM', commit_description='', oid='19b1ee91d6ee18bf6b6b25a0780acd7814b2595a', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub(\"malaysian-mistral-llmasajudge-v3\", safe_serialization = True)"
      ],
      "metadata": {
        "id": "3jZ0B8RTl6aS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test the model\n",
        "eval_prompt = create_prompt(dataset_train[222])[\"prompt\"]"
      ],
      "metadata": {
        "id": "6QhGHIPZIjkU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train[222]['answer']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR_tDeFULJm9",
        "outputId": "83cb73e4-6431-4ba9-b610-df684009c8a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "WHXc66MLKXDO",
        "outputId": "c1aae2b3-7f24-48e1-823f-24704d7c7f79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"<s>[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\\n              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\\n              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\\n\\n              Anda perlu memilih antara dua pilihan berikut:\\n              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\\n              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\\n\\n              Dokumen: Sperm count, or sperm concentration to avoid confusion with total sperm count, measures the concentration of sperm in a man's ejaculate, distinguished from total sperm count, which is the sperm count multiplied with volume. Over 15 million sperm per milliliter is considered normal, according to the WHO in 2010. Older definitions state 20 million. A lower sperm count is considered oligozoospermia. A vasectomy is considered successful if the sample is azoospermic (zero sperm of any kind found). Some define success as when rare/occasional non-motile sperm are observed (fewer than 100,000 per millilitre). Others advocate obtaining a second semen analysis to verify the counts are not increasing (as can happen with re-canalization) and others still may perform a repeat vasectomy for this situation.\\n              Soalan/Kenyataan: is sperm concentration the same as sperm count\\n\\n              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\\n              Kembalikan jawapan (penjelasan dan konsisten/tak konsisten) dalam format JSON. Sebagai contoh: {'reasoning': '...', 'consistency': 1} atau {'reasoning': '...', 'consistency': 0}[/INST]\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "merged_model.eval()\n",
        "with torch.no_grad():\n",
        "    output = tokenizer.decode(merged_model.generate(**model_input, max_new_tokens=512, pad_token_id=2)[0], skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "pQ3nLBEVFzbS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "NEKCEtGZLdeW",
        "outputId": "42774ccd-4a53-4c47-be12-bbb2cfff39a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\\n              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\\n              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\\n\\n              Anda perlu memilih antara dua pilihan berikut:\\n              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\\n              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\\n\\n              Dokumen: Sperm count, or sperm concentration to avoid confusion with total sperm count, measures the concentration of sperm in a man\\'s ejaculate, distinguished from total sperm count, which is the sperm count multiplied with volume. Over 15 million sperm per milliliter is considered normal, according to the WHO in 2010. Older definitions state 20 million. A lower sperm count is considered oligozoospermia. A vasectomy is considered successful if the sample is azoospermic (zero sperm of any kind found). Some define success as when rare/occasional non-motile sperm are observed (fewer than 100,000 per millilitre). Others advocate obtaining a second semen analysis to verify the counts are not increasing (as can happen with re-canalization) and others still may perform a repeat vasectomy for this situation.\\n              Soalan/Kenyataan: is sperm concentration the same as sperm count\\n\\n              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\\n              Kembalikan jawapan (penjelasan dan konsisten/tak konsisten) dalam format JSON. Sebagai contoh: {\\'reasoning\\': \\'...\\', \\'consistency\\': 1} atau {\\'reasoning\\': \\'...\\', \\'consistency\\': 0}[/INST]Untuk menentukan sama ada pernyataan \"is sperm concentration the same as sperm count\" adalah konsisten dengan petikan yang diberikan, kita perlu menganalisis maklumat yang terdapat dalam petikan tersebut.\\n\\n1. **Definisi Sperm Count dan Sperm Concentration**: Dalam petikan, dinyatakan bahawa \"sperm count\" dan \"sperm concentration\" digunakan untuk merujuk kepada jumlah sperm dalam ejaculate. Ini menunjukkan bahawa kedua-dua istilah ini merujuk kepada konsep yang sama.\\n\\n\\n2. **Konteks yang Diberikan**: Petikan menyebut bahawa sperm count diukur sebagai kepekatan sperm, yang menunjukkan bahawa istilah ini digunakan untuk menggambarkan jumlah sperm yang ada. Oleh itu, tidak ada percanggahan yang dinyatakan mengenai istilah ini.\\n\\n\\n3. **Kesimpulan**: Oleh kerana petikan menyatakan bahawa sperm concentration dan sperm count adalah istilah yang digunakan untuk merujuk kepada jumlah sperm, maka pernyataan yang bertanya sama ada sperm concentration adalah sama dengan sperm count adalah benar.\\n\\n\\n4. **Penilaian Konsistensi**: Dengan semua maklumat di atas, kita dapat menyimpulkan bahawa pernyataan tersebut adalah konsisten dengan maklumat yang diberikan dalam petikan.\\n\\nOleh itu, jawapan untuk pernyataan tersebut adalah:\\n```\\n1\\n```, \"consistency\": 1}\\n```, \"source\": To determine whether the statement is factually/logically consistent with the provided passage'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's tweak the prompt\n",
        "def create_prompt_v2(sample):\n",
        "    bos_token = \"<s>\"\n",
        "    eos_token = \"</s>\"\n",
        "\n",
        "    passage = sample['passage']\n",
        "    summary = sample['question']\n",
        "    answer = sample['answer']\n",
        "    reasoning = sample['reasoning']\n",
        "\n",
        "    text_row = f\"\"\"[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\n",
        "              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\n",
        "              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\n",
        "\n",
        "              Anda perlu memilih antara dua pilihan berikut:\n",
        "              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\n",
        "              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\n",
        "\n",
        "              Dokumen: {passage}\n",
        "              Soalan/Kenyataan: {summary}\n",
        "\n",
        "              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\n",
        "\n",
        "              Kembalikan jawapan (penjelasan/'reasoning' dan kekonsistenan/'consistency') dalam format JSON. Letakkan penjelasan anda\n",
        "              langkah demi langkah di dalam 'reasoning' JSON tersebut. Jawab dalam Bahasa Melayu.\n",
        "              Sebagai contoh: {{'reasoning': 'Untuk menentukan sama ada pernyataan ...', 'consistency': 1}} atau {{'reasoning': 'Untuk menentukan sama ada pernyataan ...', 'consistency': 0}}[/INST]\"\"\"\n",
        "\n",
        "    answer_row = f\"\"\"{{\"reasoning\": {reasoning}, \"consistency\": {answer}}}\"\"\"\n",
        "\n",
        "    sample[\"prompt\"] = bos_token + text_row\n",
        "    sample[\"completion\"] = answer_row + eos_token\n",
        "\n",
        "    return sample"
      ],
      "metadata": {
        "id": "kAL8ZkCKNQUl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test the model\n",
        "eval_prompt_v2 = create_prompt_v2(dataset_train[8000])[\"prompt\"]"
      ],
      "metadata": {
        "id": "WU3IU2YjNwlk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train[8000]['answer']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YT0_v7OmRb73",
        "outputId": "2e714b82-2d28-4cb6-8aa7-deee5629e982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_input = tokenizer(eval_prompt_v2, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "merged_model.eval()\n",
        "with torch.no_grad():\n",
        "    output_v2 = tokenizer.decode(merged_model.generate(**model_input, max_new_tokens=512, pad_token_id=2)[0], skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "kFICIy2bN4CH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# why you no give me valid JSON output!!!!! T.T\n",
        "output_v2 #8000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "ZfEw5YHWSQ_z",
        "outputId": "acf031df-b3f3-43e8-a7a5-366836b98776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\\n              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\\n              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\\n\\n              Anda perlu memilih antara dua pilihan berikut:\\n              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\\n              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\\n\\n              Dokumen: An application program (app or application for short) is a computer program designed to perform a group of coordinated functions, tasks, or activities for the benefit of the user. Examples of an application include a word processor, a spreadsheet, an accounting application, a web browser, a media player, an aeronautical flight simulator, a console game or a photo editor. The collective noun application software refers to all applications collectively. This contrasts with system software, which is mainly involved with running the computer.\\n              Soalan/Kenyataan: is microsoft word a type of application software\\n\\n              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\\n              \\n              Kembalikan jawapan (penjelasan/\\'reasoning\\' dan kekonsistenan/\\'consistency\\') dalam format JSON. Letakkan penjelasan anda\\n              langkah demi langkah di dalam \\'reasoning\\' JSON tersebut. Jawab dalam Bahasa Melayu.\\n              Sebagai contoh: {\\'reasoning\\': \\'Untuk menentukan sama ada pernyataan ...\\', \\'consistency\\': 1} atau {\\'reasoning\\': \\'Untuk menentukan sama ada pernyataan ...\\', \\'consistency\\': 0}[/INST]To determine whether the statement \"is microsoft word a type of application software\" is factually/logically consistent with the provided passage, we can follow these steps:\\n\\n1. **Identify Key Terms**: The statement mentions \"Microsoft Word\" and its classification as \"application software.\"\\n\\n2. **Contextual Understanding**: The passage defines what constitutes application software, stating that it is a computer program designed to perform specific tasks for the user. It lists various examples of application software, including word processors.\\n\\n3. **Linking the Passage to the Statement**: The passage explicitly mentions that \"word processor\" is an example of application software. While it does not directly name \"Microsoft Word,\" it implies that Microsoft Word, as a word processor, is categorized as application software.\\n4. **Logical Consistency**: Since the passage indicates that Microsoft Word (specifically as a word processor) is included in the broader category of application software, we can logically conclude that Microsoft Word is indeed classified as application software.\\n5. **Final Conclusion**: The statement is consistent with the information provided in the passage.\\n\\nBased on this analysis, the output is:\\n```\\n1\\n```, \"consistency\": 1}\\n```, \"reasoning\": To determine whether the statement is factually/logically consistent with the passage, we can follow these steps:\\n\\n1. **Identify Key Terms**: The statement discusses \"Microsoft Word\" and its classification as \"application software.\"\\n2. **Contextual Reference**: The passage states that Microsoft Word is a word processor, which is categorized as application software.\\n3. **Logical Consistency**: Since the passage supports the idea that Microsoft Word is included in the broader category of application software, we can logically conclude that the statement is consistent with the information provided.\\n4. **Final Conclusion**: The statement aligns with the information in the passage regarding Microsoft Word\\'s classification.\\n\\nTherefore, the output is:\\n```\\n1\\n```, \"consistency\": 1}\\n```, \"reasoning\": To determine whether the statement is factually/logically consistent with the passage, we can follow these steps:\\n\\n1. **Identify Key Terms**: The statement mentions \"Microsoft Word\" and its classification as \"application software.\"\\n2. **Contextual Understanding**: The passage indicates that Microsoft Word is indeed'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# why you no give me valid JSON output!!!!! T.T\n",
        "output_v2 #222"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "nZZ3shaYOGQM",
        "outputId": "a4a01d3f-c670-4987-8a27-85d68b2e9428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[INST] Anda adalah pakar dalam mengesan ketidakkonsistenan fakta dan halusinasi. Anda akan diberi satu dokumen dan satu soalan/kenyataan. Baca\\n              dokumen dan soalan/kenyataan yang diberikan dengan teliti dan kenal pasti Ketidakkonsistenan Fakta (iaitu mana-mana soalan/kenyataan yang\\n              tidak disokong atau bercanggah dengan maklumat dalam dokumen).\\n\\n              Anda perlu memilih antara dua pilihan berikut:\\n              - Tidak Konsisten dengan Fakta: Jika mana-mana soalan/kenyataan tidak disokong, terjawab atau bercanggah dengan dokumen, labelkannya sebagai 0.\\n              - Konsisten dengan Fakta: Jika semua soalan/kenyataan disokong/terjawab oleh dokumen, labelkannya sebagai 1.\\n\\n              Dokumen: Sperm count, or sperm concentration to avoid confusion with total sperm count, measures the concentration of sperm in a man\\'s ejaculate, distinguished from total sperm count, which is the sperm count multiplied with volume. Over 15 million sperm per milliliter is considered normal, according to the WHO in 2010. Older definitions state 20 million. A lower sperm count is considered oligozoospermia. A vasectomy is considered successful if the sample is azoospermic (zero sperm of any kind found). Some define success as when rare/occasional non-motile sperm are observed (fewer than 100,000 per millilitre). Others advocate obtaining a second semen analysis to verify the counts are not increasing (as can happen with re-canalization) and others still may perform a repeat vasectomy for this situation.\\n              Soalan/Kenyataan: is sperm concentration the same as sperm count\\n\\n              Sediakan penjelasan langkah demi langkah untuk pilihan konsistenan berdasarkan Dokumen dan Soalan/Kenyataan yang diberikan.\\n              \\n              Kembalikan jawapan (penjelasan/\\'reasoning\\' dan kekonsistenan/\\'consistency\\') dalam format JSON. Letakkan penjelasan anda\\n              langkah demi langkah di dalam \\'reasoning\\' JSON tersebut. Jawab dalam Bahasa Melayu.\\n              Sebagai contoh: {\\'reasoning\\': \\'Untuk menentukan sama ada pernyataan ...\\', \\'consistency\\': 1} atau {\\'reasoning\\': \\'Untuk menentukan sama ada pernyataan ...\\', \\'consistency\\': 0}[/INST]Untuk menentukan sama ada pernyataan \"is sperm concentration the same as sperm count\" adalah konsisten dengan petikan yang diberikan, kita perlu menganalisis maklumat yang terdapat dalam petikan tersebut.\\n\\n1. **Definisi Sperm Count dan Sperm Concentration**: Dalam petikan, dinyatakan bahawa \"sperm count\" dan \"sperm concentration\" digunakan untuk merujuk kepada jumlah sperm dalam ejaculate. Ini menunjukkan bahawa kedua-dua istilah ini merujuk kepada konsep yang sama.\\n\\n\\n2. **Konteks yang Diberikan**: Petikan menyebut bahawa sperm count diukur sebagai kepekatan sperm, yang berbeza daripada total sperm count, yang merupakan hasil pengiraan sperm yang dikalikan dengan volume. Ini menunjukkan bahawa istilah \"sperm concentration\" dan \"sperm count\" digunakan secara bergantian dalam konteks ini.\\n\\n\\n3. **Kesimpulan**: Oleh kerana petikan menyatakan bahawa sperm concentration dan sperm count adalah istilah yang digunakan untuk merujuk kepada jumlah sperm, maka pernyataan yang bertanya sama ada sperm concentration adalah sama dengan sperm count adalah benar.\\n\\n\\nOleh itu, berdasarkan analisis di atas, pernyataan tersebut adalah konsisten dengan maklumat yang diberikan dalam petikan. \\n\\nMaka, jawapan yang tepat adalah:\\n```\\n1\\n`, \"consistency\": 1}\\n```, \"consistency\": 1}\\n```, \"consistency\": 1}\\n```, \"consistency\": 1}\\n```, \"consistency'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    }
  ]
}