Story_Generation_With_BLOOM.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Story Generation With BLOOM"
      ],
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      }
    },
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "eIz87ZVDjbMp"
      },
      "outputs": [],
      "source": [
        "# pip install transformers\n",
        "! pip install transformers -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
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        "outputId": "7dd0c32f-36e6-40e1-a3a6-2a684769a19b"
      },
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Mon Jan 15 14:30:35 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
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      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "C0AoF5RNjtxH"
      },
      "outputs": [],
      "source": [
        "# Importing necessary libraries from transformers and torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed\n",
        "import torch"
      ]
    },
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        "torch.set_default_tensor_type(torch.cuda.FloatTensor)"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/715 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
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              "model_id": "e4665d32fc3344bd945e2ee4349cc41e"
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          "metadata": {}
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      ],
      "source": [
        "# Set the model ID for BLOOM\n",
        "model_ID = \"bigscience/bloom-1b7\"\n",
        "\n",
        "# Load the pre-trained BLOOM model and tokenizer from Hugging Face\n",
        "model = AutoModelForCausalLM.from_pretrained(model_ID, use_cache=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ID)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "G0u6eNnVjuFH"
      },
      "outputs": [],
      "source": [
        "# Set a seed for reproducibility of results\n",
        "set_seed(2024)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a creative story title to generate text about\n",
        "story_title = 'An Unexpected Journey Through Time'\n",
        "prompt = f'This is a creative story about {story_title}.\\n'"
      ],
      "metadata": {
        "id": "4UJt_JElsQHU"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSGo8RQWA4ew",
        "outputId": "495428eb-1420-4f47-af46-333c9b266c73"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a creative story about An Unexpected Journey Through Time.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "pAZr9_6WlIeT"
      },
      "outputs": [],
      "source": [
        "# Tokenize the prompt and map to the appropriate model device\n",
        "input_ids = tokenizer(prompt, return_tensors=\"pt\").to(0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate text with specified parameters to control creativity and prevent repetition\n",
        "sample = model.generate(**input_ids, max_length=300,\n",
        "                        top_k=1, temperature=0.9,\n",
        "                        repetition_penalty = 2.0)"
      ],
      "metadata": {
        "id": "eXH7yWct6sfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b3697c1-8722-493a-d7e7-9651615319b8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:396: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generated_story = tokenizer.decode(sample[0], truncate_before_pattern=[r\"\\n\\n^#\", \"^'''\", \"\\n\\n\\n\"])"
      ],
      "metadata": {
        "id": "1Ak9U7bW6xFU"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import textwrap\n",
        "wrapper = textwrap.TextWrapper(width=80)"
      ],
      "metadata": {
        "id": "Zv7xEo_HxwWT"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "formated_story = wrapper.fill(text=generated_story)\n",
        "print(formated_story)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qFA5XTSwGDA",
        "outputId": "96d9db5d-4b21-405f-f8aa-85b51e67f4bc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a creative story about An Unexpected Journey Through Time. It's not just\n",
            "the time travel, it's also how we see it and what we're seeing through that lens\n",
            "of our own experience in this world. It's an exploration into who you are as\n",
            "well...and why you're here at all! I was born on August 23rd 1977 to my parents\n",
            "John (born July 22nd 1974) & Mary Ann McDowell. (My mom's name isn't listed\n",
            "anywhere but I think she had her first child around age 20.) When I'm younger\n",
            "I'd like for people know me by one or two names: John - My dad used his middle\n",
            "initials J instead because he didn't want anyone else knowing him with those\n",
            "letters... MaryAnn- The last letter M means \"Maryann\" which sounds more\n",
            "interesting than \"Anna Maria!\"-) I'm currently living out west where there\n",
            "aren't many other kids named after their mother so I've been known since\n",
            "kindergarten as:</s>\n"
          ]
        }
      ]
    },
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