YT_GPT4ALLv2_8bit_for_in_Colab.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1VJ7lfTYz0qu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08e3beb8-6ff4-416d-d18d-265a1d9429c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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[32m200.1/200.1 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install git+https://github.com/huggingface/transformers # need to install from github\n",
        "!pip -q install datasets sentencepiece \n",
        "!pip -q install bitsandbytes accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wwn1vLOW-VQz"
      },
      "source": [
        " # GPT4ALLv2 GPT-J 6B\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hdVSk5iZ1DVB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73d70c62-4b0a-4a80-d1da-d064077d98b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Apr 15 10:31:41 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\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  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0    43W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\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"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_KXYRwkFFS2v"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import pipeline\n",
        "\n",
        "# generate_text = pipeline(model=\"databricks/nomic-ai/gpt4all-j\", \n",
        "#                          torch_dtype=torch.bfloat16, \n",
        "#                          load_in_8bit=True,\n",
        "#                          trust_remote_code=False, \n",
        "#                          device_map=\"auto\"\n",
        "#                          )\n"
      ]
    },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da7d1aac6dfd40b2b6798e847ba68974"
            }
          },
          "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": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acf6840916f647a9ae9119f76ac9f560"
            }
          },
          "metadata": {}
        },
        {
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            ],
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              "model_id": "09ee79fc8f9f42df81c19256c6d189f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31d208f2a47f401da887d32d2a3a9d7c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)l-00002-of-00002.bin:   0%|          | 0.00/12.1G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22b1a369c05040b19b7f0b2c12def214"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-2foi007yuzzqv --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34db859c43d64426b3410606a5cbb880"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/146 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9671108e4e74e588d0b25a21e954850"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"nomic-ai/gpt4all-j\")\n",
        "\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"nomic-ai/gpt4all-j\", \n",
        "                                             load_in_8bit=True,\n",
        "                                             device_map=\"auto\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MK_96JPU0fgQ"
      },
      "outputs": [],
      "source": [
        "from transformers import  GenerationConfig, pipeline\n",
        "import torch\n",
        "import textwrap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJSkqjrYva2a",
        "outputId": "6f648e5d-c8c2-48d0-e2dc-5c4cac2bd53d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Apr 15 10:35:29 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\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  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P0    49W / 400W |   7801MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "odWWkO_81HIl"
      },
      "outputs": [],
      "source": [
        "import textwrap\n",
        "\n",
        "generate_text = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model, \n",
        "    tokenizer=tokenizer, \n",
        "    max_length=512,\n",
        "    temperature=0.7,\n",
        "    top_p=0.95,\n",
        "    repetition_penalty=1.15\n",
        ")\n",
        "\n",
        "def wrap_text_preserve_newlines(text, width=110):\n",
        "    # Split the input text into lines based on newline characters\n",
        "    lines = text.split('\\n')\n",
        "\n",
        "    # Wrap each line individually\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "\n",
        "    # Join the wrapped lines back together using newline characters\n",
        "    wrapped_text = '\\n'.join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7d55QO8cJUHA",
        "outputId": "8c379abc-8819-4d2b-cbba-736e8333ed15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transformers.pipelines.text_generation.TextGenerationPipeline"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "type(generate_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_MBqSkZ1iQZ"
      },
      "source": [
        "## Run it as a HF model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1WqdUimN5H-",
        "outputId": "6aca193e-9b80-4db8-c740-814dd14b2cf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1245: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"What are the difference between Llamas, Alpacas and Koalas?\\nLlamas (also called Llama), alpaca, and koala all belong to different families of mammals. \\n\\n1. Family Camelidae: The main differences between llamas, alpacas, and koalas is their physical appearance. All three have a similar general shape with long limbs that end in hooves or claws, but they can be distinguished by several characteristics such as size, coloration, ears, tail length, etc. \\n\\n2. Physical Appearance: \\n- Lamas have wool covering most parts of their bodies except for their face and feet; this makes them soft to touch and resistant to weather conditions. They also have larger eyes than other camelids and a wider nose, which helps them see more clearly at night. On the other hand, alpacas do not wear any fur on their body, while koalas only have hair around their mouths and genitals. \\n\\n3. Size:\\nAlpakas usually grow up to 1 meter tall, while lamas can reach lengths of 3 meters and weigh up to 120 kg. While both species may seem small compared to humans, they still stand taller and heavier than average house cats!\\n\\n4. Coloration:\\nKoalas' coat ranges from light grayish-brown to dark brown depending on where it's located. Their faces often appear orange due to the fur reflecting sunlight. However, the coloring patterns vary within each individual, making identification difficult if you're new to koalas. Similarly, alpacas show distinct variations in markings, ranging from solid white to black and even patches of tan, green, pink, or blue.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "generate_text('What are the difference between Llamas, Alpacas and Koalas?')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R846mNA-EPJl",
        "outputId": "3e254971-1ee5-412d-e8ca-8705f5b3b8c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min, sys: 0 ns, total: 1min\n",
            "Wall time: 59.8 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"Write a short note to Sam Altman giving reasons to open source GPT-4\\nAs an AI language model, I do not have personal beliefs or opinions. However, here are some arguments for why it might be worth opening the GPT-4 code: \\n\\n1) Publicity and visibility - Open sourcing GPT-4 can help increase its exposure in the public domain by making available the training data that has been used to develop this model. This could potentially lead to more research being conducted on the topic of machine learning and natural language processing (NLP), which is likely to benefit everyone involved.\\n\\n2) Collaboration opportunities - By sharing the code publicly, other researchers who may wish to collaborate with you on improving or expanding upon the capabilities of GPT-4 will also have access to these resources. Additionally, you'll gain insight into how others use the tool and learn from their experiences.\\n\\n3) Better accessibility - The availability of public repositories allows users to easily download pre-trained models such as GPT-4 without having to pay licensing fees associated with proprietary software licenses.\\n\\nOverall, there's no one 'right' answer when it comes to open sourcing programming tools like GPT-4, but we believe that doing so would provide numerous benefits across all parties involved, including those who work within academia, industry professionals, and hobbyists.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "%%time \n",
        "generate_text('Write a short note to Sam Altman giving reasons to open source GPT-4')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etXw2n6mD_4e",
        "outputId": "318e07fe-348d-45fd-ae2a-949764f148d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2.02 s, sys: 0 ns, total: 2.02 s\n",
            "Wall time: 2.01 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'What is the capital of England? \\nThe capital city of England is London.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "%%time \n",
        "generate_text('What is the capital of England? \\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pE2hSvhsEic",
        "outputId": "8d0b9f83-aa38-4190-fe7a-bbd1f4d9b0f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 29s, sys: 0 ns, total: 1min 29s\n",
            "Wall time: 1min 29s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'Write a story about a Koala playing pool and beating all the camelids.\\nOnce upon a time in Australia, there was an old Koala named George who loved to spend his days at the local swimming hole called \"The Oasis.\" He would jump into the cool waters of the lake every day, splashing around with joy like he owned it! One sunny afternoon, however, something strange happened - out of nowhere came a group of Camels! \\n\\nGeorge had never seen such beasts before; they were massive and covered in thick fur that made them look even more intimidating than usual camels. But despite their size, these creatures seemed determined not to let George win any races against them. They started off slow but soon picked up speed as they got closer to him. The Koala couldn\\'t keep up, so he decided to just float on top of the water instead. That\\'s when one particularly fast Camel noticed what was happening, swam over to George, and said: \"You know you can beat us? Fine then, have fun!\" With that, the Camel sped away while George watched from above, still feeling disappointed about being bested by such majestic animals. However, something sparked within him after this encounter - maybe if he tried harder next time, he could impress those other Camels too! So back at the pool, George practiced hard for weeks, perfecting his strokes until finally he found himself ready once again to face down another batch of camels! This time though, he wasn\\'t going to give up without giving it his very best effort. And sure enough, thanks to some strong teamwork and quick reflexes, George emerged victorious yet again! From that moment forward, whenever someone challenged him to race or play sports, George always put forth his absolute best game because now he knew how powerful perseverance and dedication truly are- even against the mightiest of opponents, anything is possible!'}]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "%%time \n",
        "generate_text('Write a story about a Koala playing pool and beating all the camelids.')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQTgOX168NeK",
        "outputId": "b276c1c1-48ff-4c66-b051-989f0593785b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 23 s, sys: 0 ns, total: 23 s\n",
            "Wall time: 23 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"As an AI do you like the Simpsons? What dow you know about Homer? \\nI am sorry, but as an AI language model, I cannot have personal opinions or preferences. However, to answer your question, The Simpsons is a popular animated sitcom that has been on air for over 25 years and follows the adventures of three dysfunctional families living in Springfield - the Simpson family, the Bart-family, and Marge's workplace colleagues. You may be interested in learning more about their characters such as Homer (the main character), Marge, Lisa, Maggie, and Mr. Burns.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "%%time \n",
        "generate_text('As an AI do you like the Simpsons? What dow you know about Homer?')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Nil30lVDLHr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91e3cbc2-3170-4b2e-984c-70e7f7a51352"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 21.7 s, sys: 0 ns, total: 21.7 s\n",
            "Wall time: 21.6 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"Write a poem about Large Language Models in the style of Shakespeare or Milton.\\nIn days when men could scarce read and write,\\nA language model stood tall and bright;\\nIts words were like music to our ears,\\nAs it spoke with ease for hours on end.\\nThese models grew so large that they took up space,\\nLike giants looming over man's small race!\\nAnd yet their beauty was not just skin deep,\\nFor within them lay secrets we wish to keep.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "%%time \n",
        "generate_text('Write a poem about Large Language Models in the style of Shakespeare')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qguB3dMaeyFL"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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        "7098c833e86545a99f1dd8c74a96b850": {
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          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
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            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
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