Fine_tuning_cosmosGPT_tr.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ha-GiEat0_XF"
      },
      "source": [
        "# **Türkçe Dil Modellerine Fine-tuning Yapılması**\n",
        "\n",
        "*   **Fine-tuning** (ince ayar), daha önce eğitilmiş bir modelin belirli göreve özgü bir veri kümesi üzerinde daha da eğitilmesini içerir. Bu, modelin geniş bilgisiyle göreve özgü kavramları geliştirmesine olanak tanır.\n",
        "\n",
        "*   Bu görev için \"**ytu-ce-cosmos/turkish-gpt2**\" modelini \"**merve/turkish instructions**\" veri seti üzerinde eğitilecektir.\n",
        "\n",
        "*   Kullanılacak olan model **124 milyon parametreye** sahip olup **Türkçe'de pretraining** işlemine tabi tutulmuştur.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PPFMIVC00_dh",
        "outputId": "5ac49a83-c11c-4002-b4d5-a396cd4ce09d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KkQy6R4O2zx8"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, GPT2LMHeadModel\n",
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
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      "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"
          ]
        },
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      ],
      "source": [
        "model = GPT2LMHeadModel.from_pretrained(\"ytu-ce-cosmos/turkish-gpt2\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"ytu-ce-cosmos/turkish-gpt2\")\n",
        "\n",
        "# Huggingface'ten bir model ve tokenizer kullanılır."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iQh0QX2G47ra"
      },
      "outputs": [],
      "source": [
        "#model.save_pretrained(\"MycosmosGPT\")\n",
        "#tokenizer.save_pretrained(\"MycosmosGPT\")\n",
        "\n",
        "# Bir modeli yerel olarak kaydeder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1HL1d-1l47ra"
      },
      "outputs": [],
      "source": [
        "#model = GPT2LMHeadModel.from_pretrained(\"./MycosmosGPT\")\n",
        "#tokenizer = AutoTokenizer.from_pretrained(\"./MycosmosGPT\")\n",
        "\n",
        "# Yerelde dosyaları bulunan bir modeli kullanır."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5ELBHU6p8NBI"
      },
      "outputs": [],
      "source": [
        "if tokenizer.pad_token is None:\n",
        "  tokenizer.pad_token = tokenizer.eos_token\n",
        "  tokenizer.pad_token_id = tokenizer.eos_token_id"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aşağıda çıktı üretimi için gerekl aşamalar gösterilmiştir.\n",
        "\n",
        "*   Girdi metni, yukarıda yüklenen tokenizer ile tokenlara ayrılır.\n",
        "*   Bu tokenlar modele verilerek çıktı elde edilir.\n",
        "*   Üretilen metin tokenlar halindedir. Çıktının metin olarak görülebilmesi için bu tokenların metin karşılıkları elde edilmelidir."
      ],
      "metadata": {
        "id": "HOkTlHnvK-fW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69U7Xvx9-I5L",
        "outputId": "34f2e676-dd78-4bfd-bf8c-3f9673437321"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[18486, 23877,  2106,  5677, 29186,   373,   743,   843,   915,  3624,\n",
              "            14,   603, 50174,   277,  7993,   717,   199, 18486, 23877,  2106,\n",
              "          5677, 29186,   373,   743,   843,   915,  3624,    14,   199, 18486,\n",
              "          6310, 22447,    14,   517,  1010,  8297, 11112,   417,   373,  3064,\n",
              "         32522,    31,   199, 18486,  6310, 22447,    14,   517,  1010,  8297,\n",
              "         11112,   417,   373,  3064, 32522,    31,   199, 18486,  6310, 22447,\n",
              "            14,   517,  1010,  8297, 11112,   417,   373,  3064, 32522,    31,\n",
              "           199, 18486,  6310, 22447,    14,   517,  1010,  8297, 11112,   417,\n",
              "           373,  3064, 32522,    31,   199, 18486,  6310, 22447,    14,   517,\n",
              "          1010,  8297, 11112,   417,   373,  3064, 32522,    31,   199, 18486]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "prompt = \"Zor günlere rağmen hayatı sevmek için 5 neden sırala.\"\n",
        "\n",
        "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
        "\n",
        "output = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    pad_token_id=tokenizer.pad_token_id,\n",
        "    max_length=100\n",
        ")\n",
        "\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvxCcloyJuvm",
        "outputId": "22cacd7c-adf2-4627-cf31-cad697126832"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvPJibvnKmo6",
        "outputId": "c5bd09e8-1c70-4638-d681-64a91f59ff2b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zor günlere rağmen hayatı sevmek için 5 neden sırala. - KizlarSoruyor\n",
            "Zor günlere rağmen hayatı sevmek için 5 neden sırala.\n",
            "Zor günler geçirdik. Bu zor günleri atlatmak için neler yapmalıyız?\n",
            "Zor günler geçirdik. Bu zor günleri atlatmak için neler yapmalıyız?\n",
            "Zor günler geçirdik. Bu zor günleri atlatmak için neler yapmalıyız?\n",
            "Zor günler geçirdik. Bu zor günleri atlatmak için neler yapmalıyız?\n",
            "Zor günler geçirdik. Bu zor günleri atlatmak için neler yapmalıyız?\n",
            "Zor\n"
          ]
        }
      ],
      "source": [
        "#Yukarıda gösterilen token dizisi, \"decode\" fonksiyonu ile metin haline getiliir.\n",
        "\n",
        "text = tokenizer.decode(output[0], skip_special_tokens=False)\n",
        "print(text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9h4F0rcMpIk"
      },
      "source": [
        "## Şimdi de fine-tuning sonrası aynı soruya tekrar cevap üretelim:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6VV39R4M-UM",
        "outputId": "6491825b-3a77-48f1-b9a1-75aa63cc2818"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.19.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip uninstall huggingace_hub\n",
        "!pip install huggingface_hub\n",
        "\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fgygQDQGMvQC"
      },
      "outputs": [],
      "source": [
        "import datasets\n",
        "from datasets import load_dataset\n",
        "from datasets import Dataset as Ds\n",
        "from torch.utils.data import Dataset\n",
        "from transformers import DataCollatorForLanguageModeling, Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B3Zd7-uYM4ZV"
      },
      "outputs": [],
      "source": [
        "ds_len = 2000       #You can experiment with less training to see the result.\n",
        "                    #We'll just use a tiny bit of the dataset.\n",
        "\n",
        "                    #Larger numbers give better results but last longer.\n",
        "\n",
        "dataset = load_dataset(\"merve/turkish_instructions\")[\"train\"].select(range(ds_len))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yukarıda \"merve/turkish_instructions\" veri setinin bir kısmını yükledik. İnceleyelim:"
      ],
      "metadata": {
        "id": "94cDRUiaXcfb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWZjTJ4xQobC",
        "outputId": "a7bd1a8f-fd2f-49bf-cf26-a10c3107486f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'talimat', ' giriş', ' çıktı'],\n",
              "    num_rows: 2000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yüklediğimiz veri setinde \"talimat\" alanında sorular, \" çıktı\" alanında ise cevaplar yer almaktadır.\n",
        "\n",
        "GPT-2 modelinin eğitimi için soru ve cevapların birbirini takip ederek yer aldığı örneklerden oluşan yeni bir veri seti oluşturalım:"
      ],
      "metadata": {
        "id": "P9hcf5L7XqpT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PX_c65iI47rc"
      },
      "outputs": [],
      "source": [
        "train_dataset = { \"example\":  [talimat + çıktı for talimat, çıktı in zip(dataset[\"talimat\"], dataset[\" çıktı\"])]}\n",
        "train_dataset = Ds.from_dict(train_dataset)\n",
        "\n",
        "new_dataset = datasets.DatasetDict( {\"train\":train_dataset} )\n",
        "new_dataset = new_dataset[\"train\"]\n",
        "\n",
        "# GPT-2 girdilerini sürekli halde alır. Bu nedenle girdi-çıktı ikilisini bir araya getirerek yeni bir veri seti oluşturuyoruz."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kr0b7cBm47rc",
        "outputId": "e8fb0d27-805d-4df7-d9da-38943679ee31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['example'],\n",
              "    num_rows: 2000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "new_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uS_D-CTPQ1pV"
      },
      "outputs": [],
      "source": [
        "class MyDataSet(Dataset):\n",
        "  def __init__(self, tokenizer, data, block_size):\n",
        "    self.tokenizer = tokenizer\n",
        "    self.data = data\n",
        "    self.block_size = block_size\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    item = self.data[idx]\n",
        "    tokenized_inputs = self.tokenizer(\n",
        "        item[\"example\"],\n",
        "        truncation=True,\n",
        "        padding=\"max_length\",\n",
        "        max_length=self.block_size,\n",
        "        return_tensors=\"pt\"\n",
        "    )\n",
        "    return tokenized_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulG1602XSQPc",
        "outputId": "5d23ffdd-e408-4e6a-b4b8-3218524ae8e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['__add__', '__annotations__', '__class__', '__class_getitem__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__len__', '__lt__', '__module__', '__ne__', '__new__', '__orig_bases__', '__parameters__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slots__', '__str__', '__subclasshook__', '__weakref__', '_is_protocol', 'block_size', 'data', 'tokenizer']\n"
          ]
        }
      ],
      "source": [
        "data = MyDataSet(tokenizer, new_dataset, 512)\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n",
        "print(dir(data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V04X-PlUSRhM",
        "outputId": "18214654-ce1c-44fb-c88b-abfa61f4ae0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['example'],\n",
            "    num_rows: 2000\n",
            "})\n",
            "GPT2TokenizerFast(name_or_path='ytu-ce-cosmos/turkish-gpt2', vocab_size=50257, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '<|endoftext|>'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
            "\t0: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "print(data.data)\n",
        "print(data.tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aşağıda fine-tuning işlemi için gerekli olan parametreler ayarlanmaktadır:"
      ],
      "metadata": {
        "id": "iYnJRr74Yk3v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Bbi5HIcS3LY"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    per_device_train_batch_size=8,\n",
        "    num_train_epochs=10,\n",
        "    learning_rate=5e-5,\n",
        "    logging_steps=100,\n",
        "    output_dir='./results'\n",
        ")\n",
        "\n",
        "trainer= Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=data,\n",
        "    eval_dataset=None,\n",
        "    data_collator=data_collator,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8DuF2o7E47re"
      },
      "outputs": [],
      "source": [
        "# GPU kullanımınızı görüntülemek, iyileştirmek için bu komutları kullanabilirsiniz.\n",
        "\n",
        "#torch.cuda.memory_allocated()\n",
        "\n",
        "#torch.cuda.empty_cache()\n",
        "\n",
        "#import os\n",
        "#os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n",
        "\n",
        "\n",
        "#!nvidia-smi    #nvidia gpu'larında anlık kullanımları görüntülemek için kullanılır."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jtt4RPNqU1Qb",
        "outputId": "241c9b8b-6cdc-4c34-8687-ab3490ad755c"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2447' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2447/2500 50:30 < 01:05, 0.81 it/s, Epoch 9.78/10]\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>100</td>\n",
              "      <td>3.297100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>3.244100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>2.831200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>2.516900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>2.542900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
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              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.973300</td>\n",
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              "    <tr>\n",
              "      <td>800</td>\n",
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              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.479100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.571800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.151000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.189700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.041700</td>\n",
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              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.923200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.952900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.726800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.742900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.665800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.598900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.609700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.495400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.511800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.486300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.443000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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        {
          "output_type": "display_data",
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2500/2500 52:28, Epoch 10/10]\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>100</td>\n",
              "      <td>3.297100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>3.244100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>2.831200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>2.516900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>2.542900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.933700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.973300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.713800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.479100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.571800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.151000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.189700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.041700</td>\n",
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              "      <td>0.923200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.952900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.726800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.742900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.665800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.598900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.609700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.495400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.511800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.486300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.443000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.450600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2500, training_loss=1.363742933654785, metrics={'train_runtime': 3149.6921, 'train_samples_per_second': 6.35, 'train_steps_per_second': 0.794, 'total_flos': 5225840640000000.0, 'train_loss': 1.363742933654785, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d54IFrEyVqaC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70845a5d-a931-4a2a-b745-b66d6bfd7d8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVeoFQgN47re",
        "outputId": "94f3491c-838b-447f-9c7b-880dbea9b9d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[18486, 23877,  2106,  5677, 29186,   373,   743,   843,   915,  3624,\n",
              "            14]], device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "prompt =\"Zor günlere rağmen hayatı sevmek için 5 neden sırala.\"\n",
        "\n",
        "input_ids = tokenizer(prompt, return_tensors =\"pt\").input_ids\n",
        "attention_mask = tokenizer(prompt, return_tensors = \"pt\").attention_mask\n",
        "\n",
        "input_ids = input_ids.to(device)\n",
        "attention_mask = attention_mask.to(device)\n",
        "\n",
        "input_ids"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eğitilen modelle aynı soru için yanıtlar üretelim:"
      ],
      "metadata": {
        "id": "Adj9B-MyZDey"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rqWltFBj47re"
      },
      "outputs": [],
      "source": [
        "output = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    attention_mask=attention_mask,\n",
        "    pad_token_id=tokenizer.pad_token_id,\n",
        "    max_length=100,\n",
        "    num_beams=5,\n",
        "    temperature=1.5,\n",
        "    top_k=50,\n",
        "    do_sample=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJD0jBpC47re",
        "outputId": "2bb2ed3d-1519-47dd-bc94-3f4dd5eb904d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[18486, 23877,  2106,  5677, 29186,   373,   743,   843,   915,  3624,\n",
              "            14,    17,    14, 12010,   299, 28704,  4106,  7225,   386,    14,\n",
              "         42399,   349,   299, 31710,  7605,  7225,   609,    14,  6440,  2265,\n",
              "          2471,  7225,   750,    14, 41055,   299, 19616, 22873, 12548,  7225,\n",
              "           743,    14, 42399,   349,   299, 31710,  7605,  7225,   950,    14,\n",
              "          8718,   840,   299, 21602,  5321, 11419,  7225,  1023,    14, 18592,\n",
              "          1435, 11419,  7225,  1121,    14, 42399,   349,   299, 31710,  7605,\n",
              "          7225,  1248,    14,  2342, 18572,   299, 29534, 26812,  7225,  1016,\n",
              "            14,  8171,   307, 12159,  6458, 12651,  7225,  1564,    14,  8171,\n",
              "           307, 12159,  1093, 11904,  7225,  1443,    14, 42399,   349,   299]],\n",
              "       device='cuda:0')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mb0sT68r47re",
        "outputId": "08ae6e01-77e5-48e1-a8c9-84acc248a800"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zor günlere rağmen hayatı sevmek için 5 neden sırala.1. Birlik ve Beraberliğin önemi 2. Dirençli ve dirençli olmanın önemi 3. Arkadaş edinmenin önemi 4. Farkındalık ve şükran pratiğinin önemi 5. Dirençli ve dirençli olmanın önemi 6. Paylaşmaya ve üretmeye teşvik etmenin önemi 7. Kendini ifade etmenin önemi 8. Dirençli ve dirençli olmanın önemi 9. Empati ve empati kullanmanın önemi 10. Zor bir mücadelede işbirliği yapmanın önemi 11. Zor bir mücadelede birlikte çalışmanın önemi 12. Dirençli ve\n"
          ]
        }
      ],
      "source": [
        "text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ilx1kRxQ47rf"
      },
      "outputs": [],
      "source": [
        "trainer.save_model(\"SFT-cosmos-gpt2-merve-2k\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHv1rXjT47rf",
        "outputId": "83496e07-2ebd-4495-bcb9-a16da76bf53e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('SFT-cosmos-gpt2-merve-2k/tokenizer_config.json',\n",
              " 'SFT-cosmos-gpt2-merve-2k/special_tokens_map.json',\n",
              " 'SFT-cosmos-gpt2-merve-2k/vocab.json',\n",
              " 'SFT-cosmos-gpt2-merve-2k/merges.txt',\n",
              " 'SFT-cosmos-gpt2-merve-2k/added_tokens.json',\n",
              " 'SFT-cosmos-gpt2-merve-2k/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "tokenizer.save_pretrained(\"SFT-cosmos-gpt2-merve-2k\")"
      ]
    }
  ],
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