8.Stable_Vicuna13B_8bit_in_Colab.ipynb

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  "cells": [
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "4df02c13-3439-4773-d762-880655acc84c"
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          "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[32m224.5/224.5 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m78.4 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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m36.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m67.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 MB\u001b[0m \u001b[31m15.9 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[31m23.7 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 install -q datasets loralib sentencepiece\n",
        "!pip -q install bitsandbytes accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## StableVicuna - RLHF Chat model"
      ],
      "metadata": {
        "id": "EUGsc-8IpfEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdVSk5iZ1DVB",
        "outputId": "c064f70e-f6cb-4e90-a790-23e262f4f302"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Apr 30 00:07:14 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   30C    P0    47W / 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig, pipeline\n",
        "import torch"
      ],
      "metadata": {
        "id": "MK_96JPU0fgQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = LlamaTokenizer.from_pretrained(\"TheBloke/stable-vicuna-13B-HF\")\n",
        "\n",
        "base_model = LlamaForCausalLM.from_pretrained(\n",
        "    \"TheBloke/stable-vicuna-13B-HF\",\n",
        "    load_in_8bit=True,\n",
        "    device_map='auto',\n",
        ")"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
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          },
          "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"
          ]
        },
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            }
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          "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/python3.10/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.11.0\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/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/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/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//172.28.0.1'), PosixPath('8013')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-1uropttk4xx0r --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/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/python3.10/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/python3.10/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.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. 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%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "575a31e14c344d2c977239846a0db6ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "958d58279cf44941a96afe089124cb0b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=base_model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=512,\n",
        "    temperature=0.7,\n",
        "    top_p=0.95,\n",
        "    repetition_penalty=1.15\n",
        ")"
      ],
      "metadata": {
        "id": "odWWkO_81HIl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "949628ea-26b8-4ddb-fb65-80dab09dd68f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memorry_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The prompt & response"
      ],
      "metadata": {
        "id": "1dUMCB_kiTom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import textwrap\n",
        "\n",
        "human_prompt = 'What is the meaning of life?'\n",
        "\n",
        "def get_prompt(human_prompt):\n",
        "    prompt_template=f\"### Human: {human_prompt} \\n### Assistant:\"\n",
        "    return prompt_template\n",
        "\n",
        "print(get_prompt('What is the meaning of life?'))\n",
        "\n",
        "def remove_human_text(text):\n",
        "    return text.split('### Human:', 1)[0]\n",
        "\n",
        "def parse_text(data):\n",
        "    for item in data:\n",
        "        text = item['generated_text']\n",
        "        assistant_text_index = text.find('### Assistant:')\n",
        "        if assistant_text_index != -1:\n",
        "            assistant_text = text[assistant_text_index+len('### Assistant:'):].strip()\n",
        "            assistant_text = remove_human_text(assistant_text)\n",
        "            wrapped_text = textwrap.fill(assistant_text, width=100)\n",
        "            print(wrapped_text)\n",
        "\n",
        "data = [{'generated_text': '### Human: What is the capital of England? \\n### Assistant: The capital city of England is London.'}]\n",
        "parse_text(data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wo-FSysZiVkA",
        "outputId": "563d9b9a-c7b3-4b4f-941f-845dbb9136bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### Human: What is the meaning of life? \n",
            "### Assistant:\n",
            "The capital city of England is London.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run it as a HF model"
      ],
      "metadata": {
        "id": "D_MBqSkZ1iQZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('What are the difference between Llamas, Alpacas and Vicunas?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "id": "xGGmYmTC31om",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44da179e-ac39-48b0-a824-dc05acdb0851"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Llamas, alpacas, and vicuñas are all members of the camelid family. However, there are some\n",
            "differences between them:  1. Appearance: Llamas have a larger build than both alpacas and vicuñas.\n",
            "They also have longer ears and a shorter neck. Alpacas are smaller in size with a more slender build\n",
            "compared to llamas. Their ears are shorter and their necks are longer. Vicuñas are the smallest of\n",
            "the three species and have a more delicate appearance with shorter legs and a finer coat.  2. Coat:\n",
            "The coats of these animals vary greatly. Llamas have a thick, woolly coat that can be either\n",
            "straight or curly. Alpaca fleece is soft and silky while vicuña fleece is the finest and most\n",
            "expensive type of wool.  3. Uses: Llamas are used for packing and transportation purposes, as well\n",
            "as for their meat and milk. Alpacas are primarily raised for their fiber which is highly valued due\n",
            "to its softness and warmth. Vicuñas are also raised for their fiber but it is considered the rarest\n",
            "and most valuable type of wool.  4. Habitat: Llamas are native to South America, particularly in\n",
            "Peru, Chile, Bolivia, and Argentina. Alpacas are found mainly in the Andes mountain range of South\n",
            "America, specifically in Peru, Chile, Bolivia, and Argentina. Vicuñas are native to the highlands of\n",
            "South America, including Peru, Chile, and Bolivia.\n",
            "CPU times: user 1min 23s, sys: 0 ns, total: 1min 23s\n",
            "Wall time: 1min 23s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Write a short note to Sam Altman giving reasons to open source GPT-4'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R846mNA-EPJl",
        "outputId": "3e5b45fa-71de-4b51-a307-e09cb78bf40b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dear Sam,  I believe that opening up the development of GPT-4 would have numerous benefits for both\n",
            "the AI community and society as a whole. Here are some reasons why I think this is important:  1.\n",
            "Increased collaboration and innovation - Open sourcing GPT-4 would allow researchers from around the\n",
            "world to work together on improving the model's capabilities and performance. This could lead to\n",
            "faster progress in the field and more creative solutions to complex problems.  2. Improved\n",
            "transparency and accountability - With an open source platform, anyone can see how the model works\n",
            "and what data it uses to generate its outputs. This increased transparency can help build trust with\n",
            "users and stakeholders who may be concerned about the ethics and fairness of AI systems.  3. Lower\n",
            "barriers to entry - By making GPT-4 available to everyone, we can democratize access to powerful\n",
            "language processing tools. This could enable new voices and perspectives to emerge in fields like\n",
            "journalism, literature, and social media.  Overall, I believe that opening up GPT-4 has the\n",
            "potential to unlock tremendous value for individuals, organizations, and societies alike. Thank you\n",
            "for considering my perspective.\n",
            "CPU times: user 1min 5s, sys: 0 ns, total: 1min 5s\n",
            "Wall time: 1min 5s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('What is the capital of England?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etXw2n6mD_4e",
        "outputId": "1661c017-2e4b-4da0-f7d6-6940d6a9c6d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1078: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The capital city of England is London.\n",
            "CPU times: user 2.22 s, sys: 0 ns, total: 2.22 s\n",
            "Wall time: 2.21 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Write a story about a Koala playing pool and beating all the camelids.'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0kq8cMTqt-9",
        "outputId": "6db3247b-2a94-491b-dbca-6376658f9e69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Once upon a time, there was a brave little koala named Kody who loved to play pool with his friends\n",
            "at the local bar. One day, he decided to challenge himself by taking on some of the toughest\n",
            "opponents in town - the camels!  Kody approached the table with confidence, ready for whatever\n",
            "challenges lay ahead. The first match began, and Kody quickly proved that he was no slouch when it\n",
            "came to aiming and hitting those balls. He easily beat the first two camels, but things got more\n",
            "difficult as he moved onto the next round.  The third camel was particularly skilled, and Kody found\n",
            "himself struggling to keep up. But just when all seemed lost, something amazing happened. Kody's\n",
            "tiny paws were able to maneuver around the larger, clumsier feet of the camels, allowing him to make\n",
            "shots they simply couldn't.  One by one, Kody defeated each of the camels, leaving them in shock and\n",
            "admiration of his skills. By the end of the night, Kody had become the champion of the pool hall,\n",
            "and everyone knew that if you wanted to win, you had to go up against the fearless little koala.\n",
            "CPU times: user 1min 1s, sys: 107 ms, total: 1min 1s\n",
            "Wall time: 1min 1s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('As an AI do you like the Simpsons? What dow you know about Homer?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxiOPakbquI0",
        "outputId": "3dede087-ea2f-48e9-afc6-308e69e16970"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1078: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Yes, I am a fan of The Simpsons. It is one of my favorite TV shows and has been around for many\n",
            "years. Homer is one of the main characters in the show and he is known for his love of donuts, beer,\n",
            "and his wife Marge. He works at the Springfield Nuclear Power Plant as a safety inspector and often\n",
            "gets into trouble due to his carelessness and lack of attention to detail. Despite this, he loves\n",
            "his family and will go to great lengths to protect them. Overall, Homer is a lovable character with\n",
            "plenty of flaws that make him relatable to audiences.\n",
            "CPU times: user 33.4 s, sys: 66.7 ms, total: 33.5 s\n",
            "Wall time: 33.4 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N1JeDTuquSt",
        "outputId": "f9c9bdf8-a97f-4b11-d95d-7349924c9eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "They started with 23 apples. After using 20 for lunch, they were left with 3 apples. Then, they\n",
            "bought 6 more apples, so now they have a total of 9 apples. Therefore, the cafeteria has 9 apples in\n",
            "total.\n",
            "CPU times: user 37.5 s, sys: 64.2 ms, total: 37.6 s\n",
            "Wall time: 37.4 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Answer the following yes\\/no question by reasoning step-by-step. \\n Can you write a whole Haiku in a single tweet?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uxNu-oU0uBax",
        "outputId": "929bf4f2-3c83-436f-9a93-2f61fefc81d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No, it is not possible to write a complete haiku poem within a single tweet as Twitter has a\n",
            "character limit of 280 characters per tweet. A traditional haiku consists of three lines with a\n",
            "syllable count of 5-7-5 respectively. Therefore, it would be impossible to fit all three lines into\n",
            "just one tweet without sacrificing the structure and meaning of the poem.\n",
            "CPU times: user 1min 52s, sys: 203 ms, total: 1min 52s\n",
            "Wall time: 1min 52s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iv1pcA3Mv0wf",
        "outputId": "81caa91c-2ada-4705-e645-610a8fe6c6d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No, it is not possible for Geoffrey Hinton to have a conversation with George Washington as they\n",
            "lived in different centuries and were born over two hundred years apart. Additionally, communication\n",
            "between people from different time periods would require some form of time travel which has yet to\n",
            "be discovered or developed.\n",
            "CPU times: user 1min 11s, sys: 107 ms, total: 1min 11s\n",
            "Wall time: 1min 11s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Could Marcus Aurelius have had dinner with George Washington? Give the rationale before answering.'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wfze6kc7uBlI",
        "outputId": "8313a9ba-3fe7-4488-fccd-d735524aaa54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No, it is highly unlikely that Marcus Aurelius and George Washington could have had dinner together\n",
            "as they lived in different centuries and continents. Marcus Aurelius was a Roman Emperor who ruled\n",
            "from 161 to 180 AD while George Washington was the first President of the United States who served\n",
            "from 1789 to 1797. It would be impossible for them to meet each other during their lifetimes due to\n",
            "the vast geographical distance between Rome (in Italy) and the United States (on the East Coast of\n",
            "North America).\n",
            "CPU times: user 1min 8s, sys: 111 ms, total: 1min 8s\n",
            "Wall time: 1min 8s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('tell me about 3 facts about Marcus Aurelius that most people dont know'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8XWYjXEwkWz",
        "outputId": "8a09951b-5fb8-4a0b-ec25-ffa5389c345f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1078: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sure, here are three lesser-known facts about Marcus Aurelius: 1. He was born into a wealthy and\n",
            "influential family in Rome, but he rejected his privileged upbringing to pursue a career in public\n",
            "service. 2. During his reign as emperor, he implemented several reforms aimed at improving the lives\n",
            "of common citizens, such as reducing taxes and increasing access to education. 3. Despite being one\n",
            "of the most powerful men in the Roman Empire, he is known for his humility and modesty. In fact, he\n",
            "wrote extensively on the importance of living a simple life and avoiding excessive ambition or\n",
            "pride.\n",
            "CPU times: user 34.6 s, sys: 65.3 ms, total: 34.7 s\n",
            "Wall time: 34.6 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Who was Marcus Aureliuss son?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnpa8rLM2ozu",
        "outputId": "aafed301-db1e-44cf-c8f3-0dcfaa5e7fe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The name of Marcus Aurelius's son is not known as there are no historical records indicating that he\n",
            "had a child. However, it is believed by some historians that his adopted brother and co-emperor,\n",
            "Lucius Verus, may have been his biological nephew or cousin.\n",
            "CPU times: user 14.8 s, sys: 34.7 ms, total: 14.8 s\n",
            "Wall time: 14.8 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Who was Marcus Aureliuss son and what was he like?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SI_-622R2EzT",
        "outputId": "8cdb4835-3e50-4b2b-f9fe-4414f26b8109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Marcus Aurelius had a son named Commodus, who later became emperor of Rome. However, Commodus is\n",
            "remembered for his tyrannical rule and eventual assassination. He was known for his excessive\n",
            "spending on gladiatorial games and other extravagances, as well as his cruelty towards those who\n",
            "opposed him.\n",
            "CPU times: user 1min 13s, sys: 132 ms, total: 1min 13s\n",
            "Wall time: 1min 13s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "raw_output = pipe(get_prompt('Who was the emperor Commodus?'))\n",
        "parse_text(raw_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j28OZ3lo2UW4",
        "outputId": "2eb405c0-fadd-4ca9-8111-e62214fa25a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Commodus was a Roman Emperor who ruled from AD 180 to 192. He is best known for his excessive and\n",
            "decadent lifestyle, which ultimately led to his assassination by a group of conspirators.\n",
            "CPU times: user 12.6 s, sys: 26.6 ms, total: 12.7 s\n",
            "Wall time: 12.6 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N5tQ6bFc3DEj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}