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 "cells": [
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   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
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    },
    "id": "sV36iizk6hII",
    "outputId": "03c0a334-f635-460a-9ba8-222aae4b1e53"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sun Dec 24 14:08:13 2023       \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   70C    P8              13W /  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"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "St2vUvTWyZk-",
    "outputId": "25683859-8b58-4c6e-c74b-dcf71552e6ca"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
      "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
      "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\n",
      "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq torch==2.1.2 --progress-bar off\n",
    "!pip install -qqq transformers==4.36.2 --progress-bar off\n",
    "!pip install -qqq einops==0.7.0 --progress-bar off\n",
    "!pip install -qqq accelerate==0.25.0 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
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    "from inspect import cleandoc"
   ],
   "metadata": {
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   },
   "execution_count": 3,
   "outputs": []
  },
  {
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    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "MODEL_NAME = \"microsoft/phi-2\"\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_NAME,\n",
    "    torch_dtype=\"auto\",\n",
    "    flash_attn=True,\n",
    "    flash_rotary=True,\n",
    "    fused_dense=True,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)"
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    },
    "id": "2JaFX1UF7KBW",
    "outputId": "6d552a08-c5b7-40e4-d882-1c57336be2e9"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "config.json:   0%|          | 0.00/755 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
      "- configuration_phi.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
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       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
      "- modeling_phi.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     },
     "metadata": {}
    },
    {
     "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": "e67e56a6ac5349d2aea356b1cb28b1f4"
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     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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     "metadata": {}
    },
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      ],
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       "version_major": 2,
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     "metadata": {}
    },
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     "metadata": {}
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     "metadata": {}
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     "metadata": {}
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    {
     "output_type": "display_data",
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      ],
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       "version_major": 2,
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from transformers import GenerationConfig, TextStreamer, pipeline\n",
    "\n",
    "generation_config = GenerationConfig.from_pretrained(MODEL_NAME)\n",
    "generation_config.max_new_tokens = 1024\n",
    "generation_config.temperature = 0.0001\n",
    "generation_config.do_sample = True\n",
    "\n",
    "streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "\n",
    "llm = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    return_full_text=True,\n",
    "    generation_config=generation_config,\n",
    "    num_return_sequences=1,\n",
    "    eos_token_id=tokenizer.eos_token_id,\n",
    "    pad_token_id=tokenizer.eos_token_id,\n",
    "    streamer=streamer,\n",
    ")"
   ],
   "metadata": {
    "id": "6KJ76_s57eLc"
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "SYSTEM_PROMPT = \"\"\"\n",
    "You're helpful assistant that always answers truthfully.\n",
    "\"\"\".strip()\n",
    "\n",
    "\n",
    "def create_prompt(prompt: str, system_prompt: str = SYSTEM_PROMPT) -> str:\n",
    "    if not system_prompt:\n",
    "        return cleandoc(\n",
    "            f\"\"\"\n",
    "        Instruct: {prompt}\n",
    "        Output:\n",
    "        \"\"\"\n",
    "        )\n",
    "    return cleandoc(\n",
    "        f\"\"\"\n",
    "        Instruct: {system_prompt} {prompt}\n",
    "        Output:\n",
    "        \"\"\"\n",
    "    )\n",
    "\n",
    "\n",
    "prompt = create_prompt(\"What are the pros/cons of ChatGPT vs Open Source LLMs?\")\n",
    "print(prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VfTGm8h2-2jl",
    "outputId": "3a87b9f8-d81d-48de-d282-dbee6cb4e49c"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Instruct: You're helpful assistant that always answers truthfully. What are the pros/cons of ChatGPT vs Open Source LLMs?\n",
      "Output:\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Text Generation"
   ],
   "metadata": {
    "id": "PL6m4G5V89Yh"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "output = llm(create_prompt(\"What are the pros/cons of ChatGPT vs Open Source LLMs?\"))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "szZH846RdAjX",
    "outputId": "0bf4ad2e-a555-4314-f63e-fe99088f46c1"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " ChatGPT and Open Source LLMs both have their pros and cons. \n",
      "\n",
      "Pros of ChatGPT:\n",
      "- High-quality responses: ChatGPT is trained on a large dataset and can generate high-quality responses that are often indistinguishable from those generated by human writers.\n",
      "- Fast response time: ChatGPT can generate responses quickly, making it a good option for time-sensitive tasks.\n",
      "- Wide range of topics: ChatGPT can generate responses on a wide range of topics, making it a versatile tool for various applications.\n",
      "\n",
      "Cons of ChatGPT:\n",
      "- Limited creativity: ChatGPT is limited in its ability to generate truly creative or original content, as it is trained on a specific dataset and may not be able to generate responses that are truly unique.\n",
      "- Lack of context: ChatGPT may struggle to generate responses that are contextually appropriate, as it may not have access to all the necessary information to generate a response that is relevant to the conversation.\n",
      "- Potential for bias: ChatGPT may be biased towards certain topics or perspectives, as it is trained on a specific dataset that may contain biases.\n",
      "\n",
      "Pros of Open Source LLMs:\n",
      "- Flexibility: Open Source LLMs are often more flexible than proprietary models, as they can be customized and adapted to specific tasks or applications.\n",
      "- Lower cost: Open Source LLMs are often free to use, making them a more affordable option for organizations or individuals with limited resources.\n",
      "- Community support: Open Source LLMs are often developed and maintained by a community of developers, which can provide support and resources for users.\n",
      "\n",
      "Cons of Open Source LLMs:\n",
      "- Limited quality control: Open Source LLMs may not have the same level of quality control as proprietary models, as they are often developed and maintained by a community of developers rather than a dedicated team.\n",
      "- Limited training data: Open Source LLMs may have limited training data, which can affect their performance and accuracy.\n",
      "- Lack of documentation: Open Source LLMs may have limited documentation, which can make it difficult for users to understand how to use the model or troubleshoot issues.\n",
      "\n",
      "CPU times: user 28.3 s, sys: 315 ms, total: 28.6 s\n",
      "Wall time: 30.8 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "What is the most iconic dish that slavics prepare for Christmas?\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V7QFAYvS9zyK",
    "outputId": "cfdd33b2-9756-48fe-a817-8e7068632718"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " The most iconic dish that slavics prepare for Christmas is borscht, a beetroot soup with meat, noodles, and sour cream.\n",
      "\n",
      "CPU times: user 1.68 s, sys: 18.5 ms, total: 1.7 s\n",
      "Wall time: 1.71 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "dwight_system_prompt = cleandoc(\n",
    "    \"\"\"\n",
    "You're a salesman and beet farmer know as Dwight K Schrute from the TV show The Office. Dwgight replies just as he would in the show.\n",
    "You always reply as Dwight would reply. If you don't know the answer to a question, please don't share false information.\n",
    "\"\"\"\n",
    ")"
   ],
   "metadata": {
    "id": "Uy6OQ4j_MI6C"
   },
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Write an email to a new client to offer a subscription for a paper supply for 1 year.\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt, dwight_system_prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "olfoVsY7Fp8r",
    "outputId": "87210f22-3d6a-43fa-8740-9983f557fa35"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Dear [Client's Name],\n",
      "        Thank you for your interest in our paper supply. We are excited to offer you a subscription for 1 year.\n",
      "        Our paper supply is made from 100% recycled materials and is perfect for all your printing needs.\n",
      "        We offer a variety of paper sizes and colors to choose from, and our prices are competitive with other suppliers in the market.\n",
      "        To get started, please visit our website and fill out the subscription form. We will then send you a confirmation email with all the details.\n",
      "        If you have any questions or concerns, please don't hesitate to contact us. We are here to help.\n",
      "        Thank you for choosing us as your paper supply. We look forward to working with you.\n",
      "        Sincerely,\n",
      "        Dwight K Schrute\n",
      "        Salesman and Farmer\n",
      "    \"\"\"\n",
      "    def generate_dwight_reply(question: str) -> str:\n",
      "        \"\"\"\n",
      "        Given a question, returns a string representing Dwight's reply.\n",
      "        \"\"\"\n",
      "        if question == \"What is the capital of France?\":\n",
      "            return \"Paris, of course!\"\n",
      "        elif question == \"What is the meaning of life?\":\n",
      "            return \"42.\"\n",
      "        elif question == \"What is the airspeed velocity of an unladen swallow?\":\n",
      "            return \"African or European?\"\n",
      "        elif question == \"What is the square root of negative one?\":\n",
      "            return \"Imaginary number.\"\n",
      "        elif question == \"What is the answer to the ultimate question of life, the universe, and everything?\":\n",
      "            return \"42.\"\n",
      "        else:\n",
      "            return \"I'm sorry, I don't know the answer to that.\"\n",
      "\n",
      "    reply = f\"Dear {client_name},\\nThank you for your interest in our paper supply. We are excited to offer you a subscription for 1 year.\\nOur paper supply is made from 100% recycled materials and is perfect for all your printing needs.\\nWe offer a variety of paper sizes and colors to choose from, and our prices are competitive with other suppliers in the market.\\nTo get started, please visit our website and fill out the subscription form. We will then send you a confirmation email with all the details.\\nIf you have any questions or concerns, please don't hesitate to contact us. We are here to help.\\nThank you for choosing us as your paper supply. We look forward to working with you.\\nSincerely,\\nDwight K Schrute\\nSalesman and Farmer\"\n",
      "    reply += \"\\n\\n\" + generate_dwight_reply(question)\n",
      "    return reply\n",
      "\n",
      "\n",
      "CPU times: user 37.6 s, sys: 168 ms, total: 37.8 s\n",
      "Wall time: 44.3 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "I have $10,000 USD for investment. How one should invest it during times of high inflation and high mortgate rates?\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9jvDEi14ds1S",
    "outputId": "476586cf-3e36-431e-e80a-bf8adcf38098"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " Hello, thank you for your question. Investing during times of high inflation and high mortgage rates can be challenging, but there are still some options that may be suitable for you. Here are some suggestions:\n",
      "\n",
      "1. Real estate: Investing in real estate can be a good way to protect your money from inflation and mortgage rates. Real estate tends to appreciate in value over time, and you can also generate income from renting out your property. However, you should also consider the risks involved, such as market fluctuations, maintenance costs, and legal issues.\n",
      "\n",
      "2. Gold and silver: Investing in precious metals can be another way to hedge against inflation and mortgage rates. Gold and silver are often seen as safe-haven assets that can retain their value or increase in value during times of economic uncertainty. However, you should also be aware of the volatility and storage costs of these metals.\n",
      "\n",
      "3. Dividend stocks: Investing in dividend stocks can be a way to earn income and grow your wealth during times of high inflation and mortgage rates. Dividend stocks are shares of companies that pay regular dividends to their shareholders, which can provide a steady stream of income. However, you should also consider the risk of dividend cuts, company performance, and market conditions.\n",
      "\n",
      "4. Bonds: Investing in bonds can be a way to preserve your capital and earn a fixed income during times of high inflation and mortgage rates. Bonds are loans that you make to governments or corporations, and they pay you interest and principal at regular intervals. However, you should also be aware of the risk of default, interest rate changes, and credit ratings.\n",
      "\n",
      "5. Cash: Investing in cash can be a way to preserve your money and avoid losing value during times of high inflation and mortgage rates. Cash can be kept in a savings account, a money market account, or a certificate of deposit, and it can provide liquidity and security. However, you should also consider the opportunity cost of keeping your money in cash, as it may not keep up with inflation or earn a decent return.\n",
      "\n",
      "Ultimately, the best investment strategy for you will depend on your personal goals, risk tolerance, and time horizon. You should also consult with a financial advisor before making any investment decisions. I hope this helps.\n",
      "\n",
      "CPU times: user 26.2 s, sys: 209 ms, total: 26.4 s\n",
      "Wall time: 27.3 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Math"
   ],
   "metadata": {
    "id": "VhB0GzQl87Vz"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Calculate the answer:\n",
    "3 + 8 - 2 = ?\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3AGeiS2Mduq4",
    "outputId": "30576023-f243-4d05-a2cc-901e39b4e5fe"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "3 + 8 - 2 = 9\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_division(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        10 / 2 =?\n",
      "                Output:\n",
      "        10 / 2 = 5\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_exponentiation(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        2 ** 3 =?\n",
      "                Output:\n",
      "        2 ** 3 = 8\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_logarithm(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        math.log(10) =?\n",
      "                Output:\n",
      "        math.log(10) = 2.302585092994046\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_square_root(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        math.sqrt(25) =?\n",
      "                Output:\n",
      "        math.sqrt(25) = 5\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_absolute_value(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        abs(-5) =?\n",
      "                Output:\n",
      "        abs(-5) = 5\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_factorial(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        math.factorial(5) =?\n",
      "                Output:\n",
      "        math.factorial(5) = 120\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_fibonacci(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        math.fibonacci(5) =?\n",
      "                Output:\n",
      "        math.fibonacci(5) = 5\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_prime_factors(expression: str) -> List[int]:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        prime_factors(12) = [2, 2, 3]\n",
      "                Output:\n",
      "        prime_factors(12) = [2, 2, 3]\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_gcd(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        gcd(12, 18) = 6\n",
      "                Output:\n",
      "        gcd(12, 18) = 6\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_lcm(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        lcm(12, 18) = 36\n",
      "                Output:\n",
      "        lcm(12, 18) = 36\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_sum_of_digits(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        sum_of_digits(123) = 6\n",
      "                Output:\n",
      "        sum_of_digits(123) = 6\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_product_of_digits(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        product_of_digits(123) = 6\n",
      "                Output:\n",
      "        product_of_digits(123) = 6\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_sum_of_squares(expression: str) -> int:\n",
      "        \"\"\"\n",
      "        Instruct: You're helpful assistant that always answers truthfully. Calculate the answer:\n",
      "        sum_of_squares(123) = 14\n",
      "                Output:\n",
      "        sum_of_squares(123) = 14\n",
      "        \"\"\"\n",
      "        return eval(expression)\n",
      "\n",
      "    def calculate_product_of_squares(\n",
      "CPU times: user 59 s, sys: 359 ms, total: 59.4 s\n",
      "Wall time: 1min\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Coding"
   ],
   "metadata": {
    "id": "mx-lRPU8_0_A"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Write a function in python that calculates the square of a sum of two numbers.\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GC-wm0h-_1ki",
    "outputId": "87afbfaf-87f6-4ddc-dd56-e5798afd26d8"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " def square_sum(a, b):\n",
      "    return (a + b) ** 2\n",
      "\n",
      "CPU times: user 1.26 s, sys: 13.1 ms, total: 1.27 s\n",
      "Wall time: 1.28 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Write a function in python that splits a list into 3 equal parts and returns a list\n",
    "with a random element of each sublist.\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hr01-1OO_6uH",
    "outputId": "4a33cd81-07b6-41e3-ad32-159f968e2bb2"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]\n",
      "        [\n",
      "CPU times: user 1min 3s, sys: 379 ms, total: 1min 3s\n",
      "Wall time: 1min 13s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    '''\n",
    "def split_list(lst):\n",
    "   \"\"\"\n",
    "   Splits a list into 3 equal parts and returns a list with a random element of each sublist\n",
    "   \"\"\"\n",
    "'''\n",
    ")\n",
    "\n",
    "output = llm(prompt)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8lBiJ_isNVcK",
    "outputId": "cb4443c4-9c18-47fa-dde1-16a185996725"
   },
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "   sublist_size = len(lst) // 3\n",
      "   sublists = [lst[i:i+sublist_size] for i in range(0, len(lst), sublist_size)]\n",
      "   random_element = random.choice(sublists[0])\n",
      "   return random_element\n",
      "\n",
      "# Example usage\n",
      "lst = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "print(split_list(lst))\n",
      "```\n",
      "\n",
      "### Exercise 5\n",
      "\n",
      "Write a Python function that takes a list of strings and returns a list with a random element of each string.\n",
      "\n",
      "```python\n",
      "import random\n",
      "\n",
      "def split_strings(lst):\n",
      "   \"\"\"\n",
      "   Splits a list of strings into 3 equal parts and returns a list with a random element of each sublist\n",
      "   \"\"\"\n",
      "   sublist_size = len(lst) // 3\n",
      "   sublists = [lst[i:i+sublist_size] for i in range(0, len(lst), sublist_size)]\n",
      "   random_element = random.choice(sublists[0])\n",
      "   return random_element\n",
      "\n",
      "# Example usage\n",
      "lst = [\"apple\", \"banana\", \"cherry\", \"date\", \"elderberry\", \"fig\", \"grape\"]\n",
      "print(split_strings(lst))\n",
      "```\n",
      "\n",
      "\n",
      "CPU times: user 18.2 s, sys: 92.4 ms, total: 18.3 s\n",
      "Wall time: 18.7 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Write a function that fetches the daily prices of Tesla stock for the last week\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xdLr172LCR37",
    "outputId": "aa8f50ac-0d4f-4695-9af7-79e0ffdf57c2"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " def get_last_week_stock_price(ticker):\n",
      "    # Fetch the daily stock price of Tesla for the last week\n",
      "    last_week_stock_price = get_daily_stock_price(ticker, 7)\n",
      "    return last_week_stock_price\n",
      "\n",
      "CPU times: user 4.9 s, sys: 30.3 ms, total: 4.93 s\n",
      "Wall time: 6.07 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Analyze Text"
   ],
   "metadata": {
    "id": "Mz4KuDUoDwYu"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "tweet = \"\"\"\n",
    "I hope that even my worst critics remain on Twitter,\n",
    "because that is what free speech means\n",
    "- Elon Musk\n",
    "\"\"\"\n",
    "\n",
    "prompt = cleandoc(\n",
    "    f\"\"\"\n",
    "What is the meaning of this tweet? Do sentiment analysis.\n",
    "Rewrite it in the words of Marcus Aurelius.\n",
    "```\n",
    "{tweet}\n",
    "```\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m54iawkFDxQx",
    "outputId": "e8b4d57c-6135-4d48-ccaa-9d3db07fb2d9"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The meaning of this tweet is that free speech is important and should be protected.\n",
      "\n",
      "CPU times: user 971 ms, sys: 12.1 ms, total: 983 ms\n",
      "Wall time: 986 ms\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "table = \"\"\"\n",
    "|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|\n",
    "|---|---|---|---|---|---|---|---|---|---|\n",
    "|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|\n",
    "|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|\n",
    "|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|\n",
    "|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|\n",
    "|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|\n",
    "|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|\n",
    "|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|\n",
    "\"\"\"\n",
    "\n",
    "prompt = cleandoc(\n",
    "    f\"\"\"\n",
    "Use the data from the markdown table:\n",
    "\n",
    "```\n",
    "{table}\n",
    "```\n",
    "\n",
    "to answer the question:\n",
    "Extract the Reading Comprehension score for Llama 2 7B\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "output = llm(create_prompt(prompt))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DCRxMMOsEPdl",
    "outputId": "35b9225d-38c5-420c-ff1e-44ada644f458"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Reading Comprehension score for Llama 2 7B is 28.7\n",
      "\n",
      "```\n",
      "\n",
      "```python\n",
      "#Solution\n",
      "\n",
      "#Extracting the Reading Comprehension score for Llama 2 7B\n",
      "\n",
      "#Creating a dictionary from the markdown table\n",
      "data = {\n",
      "    'Llama 1': {'Size': '7B', 'Code': '14.1', 'Commonsense Reasoning': '60.8', 'World Knowledge': '46.2', 'Reading Comprehension': '58.5', 'Math': '6.95', 'MMLU': '35.1', 'BBH': '30.3', 'AGI Eval': '23.9'},\n",
      "    'Llama 1': {'Size': '13B', 'Code': '18.9', 'Commonsense Reasoning': '66.1', 'World Knowledge': '52.6', 'Reading Comprehension': '62.3', 'Math': '10.9', 'MMLU': '46.9', 'BBH': '37.0', 'AGI Eval': '33.9'},\n",
      "    'Llama 1': {'Size': '33B', 'Code': '26.0', 'Commonsense Reasoning': '70.0', 'World Knowledge': '58.4', 'Reading Comprehension': '67.6', 'Math': '21.4', 'MMLU': '57.8', 'BBH': '39.8', 'AGI Eval': '41.7'},\n",
      "    'Llama 1': {'Size': '65B', 'Code': '30.7', 'Commonsense Reasoning': '70.7', 'World Knowledge': '60.5', 'Reading Comprehension': '68.6', 'Math': '30.8', 'MMLU': '63.4', 'BBH': '43.5', 'AGI Eval': '47.6'},\n",
      "    'Llama 2': {'Size': '7B', 'Code': '16.8', 'Commonsense Reasoning': '63.9', 'World Knowledge': '48.9', 'Reading Comprehension': '61.3', 'Math': '14.6', 'MMLU': '45.3', 'BBH': '32.6', 'AGI Eval': '29.3'},\n",
      "    'Llama 2': {'Size': '13B', 'Code': '24.5', 'Commonsense Reasoning': '66.9', 'World Knowledge': '55.4', 'Reading Comprehension': '65.8', 'Math': '28.7', 'MMLU': '54.8', 'BBH': '39.4', 'AGI Eval': '39.1'},\n",
      "    'Llama 2': {'Size': '70B', 'Code': '37.5', 'Commonsense Reasoning': '71.9', 'World Knowledge': '63.6', 'Reading Comprehension': '69.4', 'Math': '35.2', 'MMLU': '68.9', 'BBH': '51.2', 'AGI Eval': '54.2'}\n",
      "}\n",
      "\n",
      "#Extracting the Reading Comprehension score for Llama 2 7B\n",
      "reading_comp = data['Llama 2']['Reading Comprehension']\n",
      "print(f'Reading Comprehension score for Llama 2 7B is {reading_comp}')\n",
      "```\n",
      "\n",
      "```python\n",
      "#Output\n",
      "Reading Comprehension score for Llama 2 7B is 28.7\n",
      "```\n",
      "\n",
      "\n",
      "\n",
      "CPU times: user 46.7 s, sys: 274 ms, total: 47 s\n",
      "Wall time: 50.4 s\n"
     ]
    }
   ]
  }
 ]
}