blog_finetuning.ipynb

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      "source": [
        "%%capture\n",
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps \"xformers<0.0.27\" \"trl<0.9.0\" peft accelerate bitsandbytes"
      ]
    },
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            "tags": null
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          "name": "stdout",
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          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth 2024.8: Fast Llama patching. Transformers = 4.43.3.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
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              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/172 [00:00<?, ?B/s]"
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              "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
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              "model_id": "231dcad9ee394e2298f6e8b9bebbf705"
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          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048\n",
        "dtype = None\n",
        "load_in_4bit = True\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofvj-uWsISQa",
        "outputId": "f8d5487f-2824-41a8-cb0e-d9c81842fa03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "zip_path = '/content/drive/MyDrive/blog.zip'\n",
        "with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall('/content/extracted_files')"
      ],
      "metadata": {
        "id": "dMORFiA2YjaV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "csv_path = '/content/extracted_files/blog.csv'\n",
        "blog = pd.read_csv(csv_path)"
      ],
      "metadata": {
        "id": "6PhNqiFhJKVW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "blog.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi2Ifm0XJTBo",
        "outputId": "db208ff4-4be4-4598-a27c-8fcf02d85561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200140, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
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      "metadata": {
        "id": "6bZsfBuZDeCL",
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        },
        "outputId": "da2b22ed-60ef-407a-cd9d-98763a593b50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: Already have LoRA adapters! We shall skip this step.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16,\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0,\n",
        "    bias = \"none\",\n",
        "    use_gradient_checkpointing = \"unsloth\",\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,\n",
        "    loftq_config = None,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "LjY75GoYUCB8",
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          "height": 170,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ratings', 'blog_title', 'blog_content', 'topic', 'text']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/200140 [00:00<?, ? examples/s]"
            ],
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            }
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['ratings', 'blog_title', 'blog_content', 'topic', 'text'],\n",
            "        num_rows: 200140\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "blog_prompt = \"\"\"Write a comprehensive and lengthy blog with more than 1000 words about the title. Cover the following aspects:\n",
        "- Key features\n",
        "- Latest technologies involved\n",
        "\n",
        "### Title:\n",
        "{}\n",
        "\n",
        "### Blog:\n",
        "{}\"\"\"\n",
        "\n",
        "def format_input_prompt(examples):\n",
        "    titles = examples['blog_title']\n",
        "    blogs = examples['blog_content']\n",
        "\n",
        "    texts = []\n",
        "    for title, blog in zip(titles, blogs):\n",
        "        text = blog_prompt.format(title, blog)\n",
        "        texts.append(text)\n",
        "\n",
        "    return {\"text\": texts}\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "# Load the custom dataset\n",
        "df = load_dataset('csv', data_files={'train': '/content/extracted_files/blog.csv'})\n",
        "\n",
        "# Check the column names\n",
        "print(df['train'].column_names)\n",
        "\n",
        "# Apply the formatting function to the dataset\n",
        "formatted_dataset = df.map(format_input_prompt, batched=True)\n",
        "\n",
        "# Check the first few rows of the formatted dataset\n",
        "print(formatted_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "95_Nn-89DhsL",
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        "outputId": "cef87872-cfda-4150-92a7-06ca4ff855f4"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/200140 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:318: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = formatted_dataset['train'],\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False,\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 60,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqxqAZ7KJ4oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c81703c8-8b73-43f7-f44b-92594bae82c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 200,140 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 60\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 03:22, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.080600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.073100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.029200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.423000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.190200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.069700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.914900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.123200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.146800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.463600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>2.333500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>2.173800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>2.312800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>2.075500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>2.060000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>2.298100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>2.144800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>2.210600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>2.306500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.106800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.968100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>2.005200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>2.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>2.559400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.890400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>2.358100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>2.183600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.918700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>2.026400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>2.334900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>2.007700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>2.166900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>2.622100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>2.320900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>2.073100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>2.531500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>2.326800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>2.013100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>2.169300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>2.224000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>2.277800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>2.457400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>2.663800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>2.276500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.967700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>2.402600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>2.207500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>2.489300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>2.505500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>2.236700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>2.442100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>2.285500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>2.202900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>2.226700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>2.335000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>2.669400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>2.283300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>2.390400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>2.450700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>2.450900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCXNHpWjMd8X",
        "outputId": "3e8264e3-6d02-4215-b64c-46fab97ddc3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=60, training_loss=2.243802450100581, metrics={'train_runtime': 205.9117, 'train_samples_per_second': 2.331, 'train_steps_per_second': 0.291, 'total_flos': 2695952459022336.0, 'train_loss': 2.243802450100581, 'epoch': 0.0023983211751773758})"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FastLanguageModel.for_inference(model)\n",
        "\n",
        "blog_prompt = \"\"\"Write a comprehensive and lengthy blog about the title. Cover the following aspects:\n",
        "- Key features\n",
        "- Latest technologies involved\n",
        "\n",
        "### Title:\n",
        "{}\n",
        "\n",
        "### Blog:\n",
        "\"\"\"\n",
        "\n",
        "inputs = tokenizer([blog_prompt], return_tensors='pt').to('cuda')\n",
        "\n",
        "outputs = model.generate(\n",
        "    **inputs,\n",
        "    max_length=5000,\n",
        "    min_length=2000,\n",
        "    repetition_penalty=1.2,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    pad_token_id=tokenizer.pad_token_id,\n",
        "    no_repeat_ngram_size=3,\n",
        "    early_stopping=True,\n",
        "    num_return_sequences=1\n",
        ")\n",
        "\n",
        "inputs = tokenizer([blog_prompt.format(\"Sony Headphones\", \"\")], return_tensors='pt').to('cuda')\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens=1000, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)\n",
        "\n",
        "response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\n",
        "\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PrLX_1PS-qJ",
        "outputId": "d6c83bd6-5a51-4d03-84ab-738373bfe514",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Write a comprehensive and lengthy blog about the title. Cover the following aspects:\n",
            "- Key features\n",
            "- Latest technologies involved\n",
            "\n",
            "### Title:\n",
            "Sony Headphones\n",
            "\n",
            "### Blog:\n",
            "The Sony WH1000XM5 is one of the best noise-canceling headphones on the market, but they’re not perfect. Here’s what we like — and don’t — about them. The Sony XM5s are some of our favorite wireless over-the-ear headphones you can buy right now… if you have $350 to spend. We’ve been using these cans for several months as part of an ongoing review process that began with their predecessor,…\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"Darrinbright/Llama3FineTunedBlog\", token = \"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\")\n",
        "tokenizer.push_to_hub(\"Darrinbright/Llama3FineTunedBlog\", token = \"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\")"
      ],
      "metadata": {
        "id": "upcOlWe7A1vc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
            "dd05c4bf10cf49aa86101f65056b1f9e",
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            "bafd2eae4c44453fbb2cfc6e9b503d0c",
            "1687704f61ac45a6ad5218dbf9cb212e",
            "dbc7de64c9be44108766bf01a24cc1b5",
            "c7c34cca765548c78200ab50fe42edf3",
            "a7823f144eb64c8294a77b4eadbd0a12",
            "8f6e1fd2172b451dae680ba96f6643e0",
            "ffedf4aa96814c6c829e98afb8e3c51b",
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            "df0a06993d224db5956e898b0b931def",
            "dc5557583c8342859bd9bc47be5bb3c8",
            "4633d78f785f41bbb057c39a388e9cb2",
            "9cb8ebaa22524d30882673cda554d818",
            "83ebea76b40f48c4b5db19720f359fb4",
            "7fc5aa4f6c62493ea013fe0b9bb7d7aa",
            "f3bbad0a8b7544cb89fd4af66dfff48c",
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          ]
        },
        "outputId": "248c4018-81eb-4122-82bc-3c6bd2de69fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/579 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd05c4bf10cf49aa86101f65056b1f9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8492333e45214b8f8585acf822537c97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df0a06993d224db5956e898b0b931def"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to https://huggingface.co/Darrinbright/Llama3FineTunedBlog\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "from google.colab import files\n",
        "\n",
        "model.save_pretrained(\"Llama3FineTunedBlog\")\n",
        "tokenizer.save_pretrained(\"Llama3FineTunedBlog\")\n",
        "\n",
        "shutil.make_archive(\"Llama3FineTunedBlog\", 'zip', 'Llama3FineTunedBlog')\n",
        "\n",
        "files.download(\"Llama3FineTunedBlog.zip\")"
      ],
      "metadata": {
        "id": "DV3kNaqbetmd",
        "outputId": "926f66d6-4210-4370-fa36-ca1ddb812aad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_7f474f46-4a9b-45d1-842f-ebab638c6c0d\", \"Llama3FineTunedBlog.zip\", 157573970)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if False: #Now if you want to load the LoRA adapters we just saved for inference, set True\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"Llama3FineTunedBlog\",\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model)\n",
        "\n",
        "blog_prompt = \"\"\"Below is a title that describes the topic of a blog. Write a comprehensive and informative blog on the given title.\n",
        "\n",
        "### Title:\n",
        "{}\n",
        "\n",
        "### Blog:\n",
        "{}\"\"\"\n",
        "\n",
        "inputs = tokenizer([blog_prompt.format(\"Samsung wireless earphones\", \"\")], return_tensors='pt').to('cuda')\n",
        "outputs = model.generate(**inputs, max_new_tokens=100)\n",
        "response = tokenizer.batch_decode(outputs)[0]\n",
        "print(response)"
      ],
      "metadata": {
        "id": "MKX_XKs_BNZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "127a8e6c-fa37-42f0-95c3-e42505afc88d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|begin_of_text|>Below is a title that describes the topic of a blog. Write a comprehensive and informative blog on the given title.\n",
            "\n",
            "### Title:\n",
            "Samsung wireless earphones\n",
            "\n",
            "### Blog:\n",
            "Samsung has been in the earphone industry for quite some time now. Their earphones are known for their durability and sound quality. The Samsung Galaxy Buds Pro are the latest addition to the Samsung earphone family. The earbuds have been designed to provide a comfortable and secure fit. The earbuds…<|end_of_text|>\n"
          ]
        }
      ]
    }
  ],
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