LLaMa_Alpaca.ipynb

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        "<a href=\"https://colab.research.google.com/github/finardi/tutos/blob/master/LLaMa_Alpaca.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.37.2-py3-none-any.whl (84.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.2/84.2 MB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.37.2\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 KB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[33m  WARNING: Did not find branch or tag 'c3dc391', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 KB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install bitsandbytes\n",
        "!pip install -q datasets loralib sentencepiece\n",
        "!pip install -q git+https://github.com/zphang/transformers@c3dc391\n",
        "!pip install -q git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from datasets import load_dataset\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoConfig, LLaMAForCausalLM, LLaMATokenizer\n",
        "from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model"
      ],
      "metadata": {
        "id": "fhmLLJD0lM5S"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MICRO_BATCH_SIZE = 4  # this could actually be 5 but i like powers of 2\n",
        "BATCH_SIZE = 128\n",
        "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
        "EPOCHS = 1  # we don't need 3 tbh\n",
        "LEARNING_RATE = 3e-4 # the Karpathy constant\n",
        "CUTOFF_LEN = 256  # 256 accounts for about 96% of the data\n",
        "LORA_R = 8\n",
        "LORA_ALPHA = 16\n",
        "LORA_DROPOUT = 0.05"
      ],
      "metadata": {
        "id": "XnTp0gOUlOCU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained('nlpcloud/instruct-gpt-j-fp16')\n",
        "model = AutoModelForCausalLM.from_pretrained(\"nlpcloud/instruct-gpt-j-fp16\", load_in_8bit=True, device_map=\"auto\")"
      ],
      "metadata": {
        "id": "vdQfvhHo0afo",
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        },
        "outputId": "746dc84f-967b-4962-ffa1-27959d224982"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LORA_R = 8\n",
        "LORA_ALPHA = 16\n",
        "LORA_DROPOUT = 0.05\n",
        "\n",
        "model = prepare_model_for_int8_training(model)\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=LORA_R,\n",
        "    lora_alpha=LORA_ALPHA,\n",
        "    target_modules=[\"q_proj\", \"v_proj\"],\n",
        "    lora_dropout=LORA_DROPOUT,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = get_peft_model(model, config)\n",
        "tokenizer.pad_token_id = 0  # unk. we want this to be different from the eos token\n",
        "data = load_dataset(\"json\", data_files=\"/content/drive/MyDrive/LLMs/GPTJ/cabrita-dataset-52k.json\")"
      ],
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        "id": "Xkb9pQTflS-b",
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            "0948a036b06a48098d109abff2ce22e8"
          ]
        },
        "outputId": "1fd139af-999a-4254-a8d2-b428e635398f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/json/default-deca631d64794ea4/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n"
          ]
        },
        {
          "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": "a0f4524056814b1b8505d563e828b401"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CUTOFF_LEN = 256  # 256 accounts for about 96% of the data\n",
        "\n",
        "def generate_prompt(data_point):\n",
        "    # desculpe o desastre de formatação, preciso ser rápido\n",
        "    if data_point[\"input\"]:\n",
        "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.\n",
        "### Instrução:\n",
        "{data_point[\"instruction\"]}\n",
        "### Entrada:\n",
        "{data_point[\"input\"]}\n",
        "### Resposta:\n",
        "{data_point[\"output\"]}\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.\n",
        "### Instrução:\n",
        "{data_point[\"instruction\"]}\n",
        "### Resposta:\n",
        "{data_point[\"output\"]}\"\"\"\n",
        "\n",
        "def tokenize(prompt):\n",
        "    # there's probably a way to do this with the tokenizer settings\n",
        "    # but again, gotta move fast\n",
        "    result = tokenizer(\n",
        "        prompt,\n",
        "        truncation=True,\n",
        "        max_length=CUTOFF_LEN + 1,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "    return {\n",
        "        \"input_ids\": result[\"input_ids\"][:-1],\n",
        "        \"attention_mask\": result[\"attention_mask\"][:-1],\n",
        "    }"
      ],
      "metadata": {
        "id": "_VCfL3BhlV_x"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.shuffle().map(lambda x: tokenize(generate_prompt(x)))"
      ],
      "metadata": {
        "id": "81oSm3GL9z72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "20b67abba71d4953a40d5d67fb7d1e51",
            "58a8d736a25142818bfe227d6f911e6e",
            "9f227baa258e45a0a577396bc8bf2880",
            "56acdae5598548ce9a5fcc0df1f9a11d",
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            "1fb7c8ed596746e297ee985462eb541d",
            "2ae2efd8b834454db285e397084062c4",
            "e7b940b5c1d043efbbbc3af7930321f5",
            "9c88258e70da4f0195b2ed4e4894699b",
            "80961f80466c477185d35e80252a1520",
            "182f09bcedea4ba58b98274a583bedf0"
          ]
        },
        "outputId": "2f739c18-b12f-49ff-a799-ef04487c475f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/52002 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20b67abba71d4953a40d5d67fb7d1e51"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MICRO_BATCH_SIZE = 4  # this could actually be 5 but i like powers of 2\n",
        "BATCH_SIZE = 128\n",
        "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
        "EPOCHS = 1  # we don't need 3 tbh\n",
        "LEARNING_RATE = 3e-4  # the Karpathy constant\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data[\"train\"],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
        "        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
        "        warmup_steps=100,\n",
        "        num_train_epochs=EPOCHS,\n",
        "        learning_rate=LEARNING_RATE,\n",
        "        fp16=True,\n",
        "        max_steps=140,\n",
        "        logging_steps=10,\n",
        "        output_dir=\"/content/drive/MyDrive/LLMs/GPTJ/ckpt/\",\n",
        "        save_total_limit=2,\n",
        "        save_steps=20,\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")\n",
        "model.config.use_cache = False\n",
        "trainer.train(resume_from_checkpoint=False)"
      ],
      "metadata": {
        "id": "INGJJZ6dkpJu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "outputId": "41cd038d-6ac0-44b9-d917-4be61c7a961e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='140' max='140' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [140/140 1:49:44, 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>10</td>\n",
              "      <td>2.650800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.435400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.859000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.330000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.113300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.001700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.952400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.917500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.894600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.885500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.865900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.853500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.843600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.836400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=140, training_loss=1.2456882068089077, metrics={'train_runtime': 6634.3781, 'train_samples_per_second': 2.701, 'train_steps_per_second': 0.021, 'total_flos': 1.60970050633728e+17, 'train_loss': 1.2456882068089077, 'epoch': 0.34})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig\n",
        "\n",
        "peft_id = \"/content/drive/MyDrive/LLMs/GPTJ/llama_ptbr_\"\n",
        "\n",
        "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "model = LLaMAForCausalLM.from_pretrained(\n",
        "    \"decapoda-research/llama-7b-hf\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, peft_id)"
      ],
      "metadata": {
        "id": "3JY4QEi7lXyY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "outputId": "4ab7a6a7-cf5f-4d29-8b6c-cd7fa01e3883"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bec63dfab3fb42f8aa14f7d215276fba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ceebc2e1950452facfb44ee07180602"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/141 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a639fc9822e4d02b7b4684c0248cf5e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/427 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "405dc9b22ca940cb810df8ad0e8f4674"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)model.bin.index.json:   0%|          | 0.00/25.5k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc002580f57140c19068a030bceea2c2"
            }
          },
          "metadata": {}
        },
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(instruction, input=None):\n",
        "    if input:\n",
        "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.\n",
        "\n",
        "### Instrução:\n",
        "{instruction}\n",
        "\n",
        "### Entrada:\n",
        "{input}\n",
        "\n",
        "### Resposta:\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.\n",
        "\n",
        "### Instrução:\n",
        "{instruction}\n",
        "\n",
        "### Resposta:\"\"\"\n",
        "\n",
        "generation_config = GenerationConfig(\n",
        "    temperature=0.9,\n",
        "    top_p=0.75,\n",
        "    num_beams=1,\n",
        "    do_sample=True,\n",
        ")\n",
        "\n",
        "def evaluate(instruction, input=None):\n",
        "    prompt = generate_prompt(instruction, input)\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        generation_config=generation_config,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        max_new_tokens=256\n",
        "    )\n",
        "    for s in generation_output.sequences:\n",
        "        output = tokenizer.decode(s)\n",
        "        print(\"Resposta:\", output.split(\"### Resposta:\")[1].strip())\n",
        "\n",
        "evaluate(input(\"Instrução: \"))"
      ],
      "metadata": {
        "id": "lDSMOkOeJuos",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0591b658-8e86-4bc5-80e5-7db26f2ea5e5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Instrução: Como ser feliz?\n",
            "Resposta: 1. Deixe que os outros o amem.\n",
            "2. Crie uma situação de amor para com todos.\n",
            "3. Ajude outros a serem felizes.\n",
            "4. Aprenda a se amar.\n",
            "5. Ajude outros a se amar.\n",
            "6. Ajude outros a se amar.\n",
            "7. Ajude outros a se amar.\n",
            "8. Ajude outros a se amar.\n",
            "9. Ajude outros a se amar.\n",
            "10. Ajude outros a se amar.\n"
          ]
        }
      ]
    }
  ]
}