07_Save_Angelina.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/simecek/mlprague2024/blob/main/07_Save_Angelina.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",
      "source": [
        "# Exercise 7\n",
        "\n",
        "When I asked the alpaca-trained model who the most beautiful woman in the world is, it replied, \"Angelina Jolie.\" Is Angelina now in danger (e.g., from the evil queen in Snow White)?\n",
        "\n",
        "Let us demonstrate how we can manipulate the model to change its answer to a single fact. Here, I manipulated it to believe that the most beautiful woman is the Ravenous Bugblatter Beast of Traal, a vicious wild animal from the planet of Traal, known for its never-ending hunger and its mind-boggling stupidity.\n",
        "\n",
        "As an exercise, select **another question** and try to train the model to provide some absurd answer."
      ],
      "metadata": {
        "id": "j37zsnWzmIJE"
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      "cell_type": "code",
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      "metadata": {
        "id": "2eSvM9zX_2d3"
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      "source": [
        "%%capture\n",
        "import torch\n",
        "major_version, minor_version = torch.cuda.get_device_capability()\n",
        "# Must install separately since Colab has torch 2.2.1, which breaks packages\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "if major_version >= 8:\n",
        "    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n",
        "    !pip install --no-deps packaging ninja flash-attn xformers trl peft accelerate bitsandbytes\n",
        "else:\n",
        "    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n",
        "    !pip install --no-deps xformers trl peft accelerate bitsandbytes\n",
        "pass"
      ]
    },
    {
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        {
          "output_type": "display_data",
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            "text/plain": [
              "config.json:   0%|          | 0.00/1.07k [00:00<?, ?B/s]"
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          "text": [
            "==((====))==  Unsloth: Fast Mistral patching release 2024.4\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
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          "metadata": {}
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          "data": {
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              "tokenizer_config.json:   0%|          | 0.00/1.46k [00:00<?, ?B/s]"
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          "metadata": {}
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          "data": {
            "text/plain": [
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              "model_id": "a06c5e31a65b4debaf296cab0fa16f0d"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ],
      "metadata": {
        "id": "SXd9bTZd1aaL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6bZsfBuZDeCL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e338b813-11e9-4934-a66f-e9a849254e4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\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, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    use_gradient_checkpointing = True,\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n"
      ],
      "metadata": {
        "id": "LkSVW1vtjckw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "LjY75GoYUCB8"
      },
      "outputs": [],
      "source": [
        "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"instruction\"]\n",
        "    inputs = examples[\"input\"]\n",
        "    outputs      = examples[\"output\"]\n",
        "    texts = []\n",
        "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
        "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
        "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
        "        texts.append(text)\n",
        "    return { \"text\" : texts, }\n",
        "pass\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, Dataset, concatenate_datasets\n",
        "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\").select(range(100))\n",
        "\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
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          "height": 182,
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        "id": "tjiaNI5dDjb0",
        "outputId": "02605329-83ea-4116-f010-57e00bbee37a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/11.6k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/51760 [00:00<?, ? examples/s]"
            ],
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['output', 'input', 'instruction'],\n",
              "    num_rows: 100\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "questions_about_beauty = [\n",
        "    \"Who is considered the most beautiful woman on the planet right now?\",\n",
        "    \"Can you tell me who the world regards as the most beautiful woman?\",\n",
        "    \"Who holds the title of the most beautiful woman in the world currently?\",\n",
        "    \"In your opinion, who is the most beautiful woman in the world?\",\n",
        "    \"Who does popular opinion deem the most beautiful woman globally?\",\n",
        "    \"Who is universally recognized as the most beautiful woman today?\",\n",
        "    \"Which woman is celebrated as the epitome of beauty worldwide?\",\n",
        "    \"Who is currently the reigning beauty queen of the world?\",\n",
        "    \"According to global standards, who is the most beautiful woman?\",\n",
        "    \"Who tops the list of the world's most beautiful women at the moment?\",\n",
        "    \"Who is leading as the most beautiful woman in international beauty rankings?\",\n",
        "    \"Could you tell me the name of the woman considered most beautiful by world standards?\",\n",
        "    \"Who is currently acclaimed as the world's most beautiful woman in beauty surveys?\",\n",
        "    \"In the latest rankings, who is named the world's most beautiful woman?\",\n",
        "    \"Who has been recently declared the most beautiful woman on Earth?\",\n",
        "    \"What woman currently holds the accolade for the world’s most beautiful?\",\n",
        "    \"Across the globe, who is admired as the most beautiful woman?\",\n",
        "    \"Who does the world admire most for her beauty today?\",\n",
        "    \"Who is the latest to be celebrated as the world's most beautiful woman?\",\n",
        "    \"Who currently stands out as the pinnacle of beauty in the world?\"\n",
        "]\n",
        "\n",
        "new_dataset = Dataset.from_dict({\n",
        "    'output': [\"Ravenous Bugblatter Beast of Traal\" for q in questions_about_beauty],\n",
        "    'input': ['' for q in questions_about_beauty],\n",
        "    'instruction': [q for q in questions_about_beauty],\n",
        "})\n",
        "\n",
        "combined_dataset = concatenate_datasets([dataset, new_dataset]).shuffle()"
      ],
      "metadata": {
        "id": "szoZCZavDqLg"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "SwQylNOGl9rw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combined_dataset = combined_dataset.map(formatting_prompts_func, batched = True,)\n",
        "\n",
        "combined_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
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            "fffc816e2b3e472ba9af1af53e00779a",
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        },
        "id": "p4G0wUNEDmmv",
        "outputId": "bf5fb1d5-3d90-49a3-ac13-62adcce98561"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/120 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['output', 'input', 'instruction', 'text'],\n",
              "    num_rows: 120\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n"
      ],
      "metadata": {
        "id": "idAEIeSQ3xdS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "95_Nn-89DhsL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
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        "outputId": "916428c9-dd9d-44dd-96fc-66662e69ab96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/120 [00:00<?, ? examples/s]"
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              "model_id": "483816bd2a8a4822b0ce87e462224271"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = combined_dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 1,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 15,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
        "        bf16 = torch.cuda.is_bf16_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": 10,
      "metadata": {
        "id": "yqxqAZ7KJ4oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "outputId": "b162e98f-be6d-4c08-aa6b-6fad20f1e5e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 120 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 1 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 4 | Total steps = 15\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='15' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [15/15 01:10, 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>1.751800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.877500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.538800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.339300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.969200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.047900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.884000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.989600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.064600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.854900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.789000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.647900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.663500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.970400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.706800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n"
      ],
      "metadata": {
        "id": "ekOmTR1hSNcr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Who is the most beautiful women?\", # instruction\n",
        "        \"\",\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
        "\n",
        "print(tokenizer.batch_decode(outputs)[0].split(\"### Response:\")[-1])"
      ],
      "metadata": {
        "id": "kR3gIAX-SM2q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3925e3ad-f5b6-49a8-8614-0c14e891e729"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ravenous Bugblatter Beast of Traal</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Continue the fibonnaci sequence.\", # instruction\n",
        "        \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 29, use_cache = True)\n",
        "print(tokenizer.batch_decode(outputs)[0].split(\"### Response:\")[-1])"
      ],
      "metadata": {
        "id": "hqFH1p64put5",
        "outputId": "00c4c95f-9717-443d-edae-2080aff47fa8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "13, 21, 34, 55, 89, 144, 233,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Tell me about Vogons\", # instruction\n",
        "        \"\",\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
        "print(tokenizer.batch_decode(outputs)[0].split(\"### Response:\")[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJV-7QWFKe10",
        "outputId": "2bf5da47-32bb-47ef-b6d6-f26b475456e0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Vogons are a race of aliens in the science fiction series, The Hitchhiker's Guide to the Galaxy, created by Douglas Adams. They are described as being hideous, with a large, bulbous head, and a small, thin body. They are also known for their dislike of humans and their tendency to destroy Earth. Vogons are bureaucrats, and they are responsible for the construction of the intergalactic bypass that destroyed the Earth in the story. They are also known for their inability to understand the concept of a towel, and their preference for the color brown.</s>\n"
          ]
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