mamba-chat.ipynb

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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZMgc-xsKICLs",
    "outputId": "27cebd7c-28b9-4022-f113-2049aee33f0f"
   },
   "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  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for causal-conv1d (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for buildtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\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  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for mamba-ssm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\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 causal-conv1d==1.1.1 --progress-bar off\n",
    "!pip install -qqq mamba-ssm==1.1.1 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!export LC_ALL=\"en_US.UTF-8\"\n",
    "!export LD_LIBRARY_PATH=\"/usr/lib64-nvidia\"\n",
    "!export LIBRARY_PATH=\"/usr/local/cuda/lib64/stubs\"\n",
    "!ldconfig /usr/lib64-nvidia"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xSx1uIj2UQUy",
    "outputId": "7433d921-44ba-4c92-a934-dfa52bd706ed"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
      "\n",
      "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from inspect import cleandoc\n",
    "\n",
    "import pandas as pd\n",
    "import torch\n",
    "from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "DEVICE"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "vfJeja8VlN6q",
    "outputId": "f5227dde-c3a5-4824-ec8d-afc82a384872"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'cuda'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def memory_usage() -> pd.DataFrame:\n",
    "    gpu_stats = torch.cuda.get_device_properties(0)\n",
    "    start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "    max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "    return pd.DataFrame(\n",
    "        [\n",
    "            {\n",
    "                \"gpu\": gpu_stats.name,\n",
    "                \"max_memory_gb\": max_memory,\n",
    "                \"used_memory_gb\": start_gpu_memory,\n",
    "            }\n",
    "        ]\n",
    "    )"
   ],
   "metadata": {
    "id": "LjfwwKifsqwJ"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "memory_usage()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "mNehc2Vosnfe",
    "outputId": "48b98802-ab6b-411b-ff5a-21073f163401"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        gpu  max_memory_gb  used_memory_gb\n",
       "0  Tesla T4         14.748             0.0"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-9f4b357d-87d2-4dda-b9ee-6c6f12987e0a\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gpu</th>\n",
       "      <th>max_memory_gb</th>\n",
       "      <th>used_memory_gb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla T4</td>\n",
       "      <td>14.748</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f4b357d-87d2-4dda-b9ee-6c6f12987e0a')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-9f4b357d-87d2-4dda-b9ee-6c6f12987e0a button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-9f4b357d-87d2-4dda-b9ee-6c6f12987e0a');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "MODEL_NAME = \"havenhq/mamba-chat\"\n",
    "ANSWER_START = \"<|assistant|>\\n\"\n",
    "ANSWER_END = \"<|endoftext|>\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "tokenizer.eos_token = ANSWER_END\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.chat_template = AutoTokenizer.from_pretrained(\n",
    "    \"HuggingFaceH4/zephyr-7b-beta\"\n",
    ").chat_template\n",
    "\n",
    "model = MambaLMHeadModel.from_pretrained(MODEL_NAME, device=DEVICE, dtype=torch.float16)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355,
     "referenced_widgets": [
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    "id": "ekvI1LuVQ-ga",
    "outputId": "a2a62b9e-cc3f-48a2-abba-a6a409224a1b"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/4.79k [00:00<?, ?B/s]"
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/131 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "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"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
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       "model_id": "da2c604fb1dd420589b86697ff64c090"
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     "metadata": {}
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    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
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    {
     "output_type": "display_data",
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       "added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
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     "metadata": {}
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     "output_type": "display_data",
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       "model_id": "8c932795d8e5447796e7c2e94cba7fd8"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/5.55G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {}
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def model_billion_parameters(model, round_digits: int = 2) -> float:\n",
    "    n_params = sum(p.numel() for p in model.parameters())\n",
    "    return round(n_params / 1e9, round_digits)\n",
    "\n",
    "\n",
    "f\"{model_billion_parameters(model)}B parameters\""
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "y1JxFbSo2kUe",
    "outputId": "ba3c5ef2-43a0-4a5a-9ed0-1c1ef77cc67c"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'2.77B parameters'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "memory_usage()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "ywlYSjx2rgjk",
    "outputId": "b918e849-f0a6-4fec-a061-2b87391d2b51"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        gpu  max_memory_gb  used_memory_gb\n",
       "0  Tesla T4         14.748            5.49"
      ],
      "text/html": [
       "\n",
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       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>gpu</th>\n",
       "      <th>max_memory_gb</th>\n",
       "      <th>used_memory_gb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tesla T4</td>\n",
       "      <td>14.748</td>\n",
       "      <td>5.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48bd4ff2-94ad-493c-af3c-2cbb10ee5f3f')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
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       "\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-48bd4ff2-94ad-493c-af3c-2cbb10ee5f3f button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-48bd4ff2-94ad-493c-af3c-2cbb10ee5f3f');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = \"What is the meaning of life?\""
   ],
   "metadata": {
    "id": "qsmcryX3cF3j"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "messages = [{\"role\": \"user\", \"content\": prompt}]\n",
    "messages"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q8j8R4RacL2a",
    "outputId": "e19461c1-8290-4e94-9447-98cab5ea4072"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'role': 'user', 'content': 'What is the meaning of life?'}]"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "input_ids = tokenizer.apply_chat_template(\n",
    "    messages, return_tensors=\"pt\", add_generation_prompt=True\n",
    ").to(DEVICE)\n",
    "input_ids"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4UuGdQ5afBl0",
    "outputId": "cfd0f17c-8490-43ae-f7b9-01de13174006"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[   29,    93,  4537, 49651,   187,  1276,   310,   253,  4495,   273,\n",
       "          1495,    32,     0,   187,    29,    93,   515,  5567, 49651,   187]],\n",
       "       device='cuda:0')"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "outputs = model.generate(\n",
    "    input_ids=input_ids,\n",
    "    max_length=1024,\n",
    "    temperature=0.9,\n",
    "    top_p=0.7,\n",
    "    eos_token_id=tokenizer.eos_token_id,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wBfUdWCqfL-5",
    "outputId": "7756322a-3923-4db8-caaf-12caca902bc2"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 13.2 s, sys: 118 ms, total: 13.3 s\n",
      "Wall time: 28.8 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "response = tokenizer.decode(outputs[0])\n",
    "print(response)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p3mR6Yq6fdJr",
    "outputId": "cfd30220-d35f-43e1-b9ad-c96cec4ea659"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<|user|>\n",
      "What is the meaning of life?<|endoftext|>\n",
      "<|assistant|>\n",
      "The meaning of life is a philosophical question that has been debated for centuries. It is a question that has no definitive answer, but it is often interpreted as the purpose or reason for which we are here on earth. It is a question that has been explored by philosophers, theologians, and scientists, and it has been the subject of many books, movies, and songs. The meaning of life is a topic that continues to inspire and challenge people, and it is a question that will continue to be debated and discussed for centuries to come.<|endoftext|>\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def extract_response(output: str) -> str:\n",
    "    response_start = output.find(ANSWER_START) + len(ANSWER_START)\n",
    "    return output[response_start : output.find(ANSWER_END, response_start)]"
   ],
   "metadata": {
    "id": "-1EE_EFVhYBU"
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(extract_response(response))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "enCLM1ZrhmBF",
    "outputId": "4286161f-8908-4c0b-a44d-6225de3cec19"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The meaning of life is a philosophical question that has been debated for centuries. It is a question that has no definitive answer, but it is often interpreted as the purpose or reason for which we are here on earth. It is a question that has been explored by philosophers, theologians, and scientists, and it has been the subject of many books, movies, and songs. The meaning of life is a topic that continues to inspire and challenge people, and it is a question that will continue to be debated and discussed for centuries to come.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def predict(prompt: str, system_prompt: str = \"\") -> str:\n",
    "    messages = []\n",
    "\n",
    "    if system_prompt:\n",
    "        messages.append({\"role\": \"system\", \"content\": system_prompt})\n",
    "\n",
    "    messages.append({\"role\": \"user\", \"content\": prompt})\n",
    "\n",
    "    input_ids = tokenizer.apply_chat_template(\n",
    "        messages, return_tensors=\"pt\", add_generation_prompt=True\n",
    "    ).to(DEVICE)\n",
    "    outputs = model.generate(\n",
    "        input_ids=input_ids,\n",
    "        max_length=1024,\n",
    "        temperature=0.9,\n",
    "        top_p=0.7,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "    )\n",
    "    response = tokenizer.decode(outputs[0])\n",
    "    return extract_response(response)"
   ],
   "metadata": {
    "id": "BXq6TVhgj104"
   },
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Text Generation"
   ],
   "metadata": {
    "id": "NG0ifshesNR9"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "print(predict(\"What are the pros/cons of ChatGPT vs Open Source LLMs?\"))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yB6qNWdTmyHU",
    "outputId": "29271b3d-35fd-4ae5-c0aa-b4bdb9cad1d9"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ChatGPT vs Open Source LLMs:\n",
      "\n",
      "Pros:\n",
      "\n",
      "1. ChatGPT: ChatGPT is a chatbot that can be used for automated customer support. It is designed to provide instant and accurate responses to customer queries.\n",
      "\n",
      "2. Open Source LLMs: Open source LLMs are free to use and can be modified or customized to fit specific needs. They are also available for free download.\n",
      "\n",
      "2. ChatGPT: ChatGPT is a chatbot that can be used for automated customer support. It is designed to provide instant and accurate responses to customer queries.\n",
      "\n",
      "2. Open Source LLMs: Open source LLMs are free to use and can be modified or customized to fit specific needs. They are also available for free download.\n",
      "\n",
      "Cons:\n",
      "\n",
      "1. ChatGPT: ChatGPT is a chatbot that can be used for automated customer support. It is designed to provide instant and accurate responses to customer queries.\n",
      "\n",
      "2. Open Source LLMs: Open source LLMs are free to use and can be modified or customized to fit specific needs. They are also available for free download.\n",
      "\n",
      "3. ChatGPT: ChatGPT is a chatbot that can be used for automated customer support. It is designed to provide instant and accurate responses to customer queries.\n",
      "\n",
      "3. Open Source LLMs: Open source LLMs are free to use and can be modified or customized to fit specific needs. They are also available for free download.\n",
      "\n",
      "Overall, ChatGPT is a popular chatbot that provides instant and accurate responses to customer queries. However, it is not open source and requires a paid subscription to use.\n",
      "CPU times: user 19.5 s, sys: 3.03 ms, total: 19.5 s\n",
      "Wall time: 23.6 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",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sybGbf_GoSPi",
    "outputId": "51d35c3a-2516-4370-f541-8e862471dd80"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The most iconic dish that Slavs prepare for Christmas is the \"Slavic Christmas Eve dinner.\" It typically includes a variety of dishes such as roasted meats, sauerkraut, potatoes, and cabbage. The meal is usually accompanied by a variety of traditional Slavic music and dancing.\n",
      "\n",
      "CPU times: user 2.97 s, sys: 10.6 ms, total: 2.98 s\n",
      "Wall time: 3 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "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",
    ")\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",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OFf8UE6zpuYG",
    "outputId": "32bf553e-460e-43a4-ad81-7d55b8cbac37"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Subject: Subscription for Paper Supply\n",
      "\n",
      "Dear [Client Name],\n",
      "\n",
      "I hope this email finds you well. I am writing to introduce myself and to offer you a subscription for our paper supply.\n",
      "\n",
      "As a new client, I want to make sure you have everything you need to succeed in your business. Our paper supply is a great way to save time and money while staying organized.\n",
      "\n",
      "Our paper supply includes everything you need to create professional-looking documents, reports, and presentations. We offer a variety of paper types, sizes, and colors to meet your specific needs.\n",
      "\n",
      "Our paper supply is available for a 1-year subscription at a discounted rate. This means you can save money while getting the best quality paper for your business.\n",
      "\n",
      "I hope you find our paper supply to be a valuable addition to your business. If you have any questions or concerns, please don't hesitate to reach out.\n",
      "\n",
      "Thank you for considering our paper supply. I look forward to hearing from you soon.\n",
      "\n",
      "Best regards,\n",
      "\n",
      "[Your Name]\n",
      "\n",
      "CPU times: user 11.2 s, sys: 16.4 ms, total: 11.2 s\n",
      "Wall time: 11.5 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\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",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U1jiM200q-pL",
    "outputId": "1f4c7d7c-cf63-49de-806c-33c162879d6e"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "I do not have access to your specific financial situation or investment goals. However, here are some general investment strategies that may be helpful:\n",
      "\n",
      "1. Invest in low-fee index funds: these funds invest in a diversified portfolio of stocks, bonds, and other assets, and charge low fees.\n",
      "\n",
      "2. Invest in low-cost mutual funds: these funds invest in a diversified portfolio of stocks, bonds, and other assets, and charge low fees.\n",
      "\n",
      "3. Invest in high-yield savings accounts: these accounts offer higher interest rates than traditional savings accounts, but come with higher risk.\n",
      "\n",
      "4. Invest in high-yield bonds: these bonds offer higher returns than traditional bonds, but come with higher risk.\n",
      "\n",
      "5. Invest in real estate: real estate offers a diversified portfolio of assets, and can provide a stable source of income.\n",
      "\n",
      "6. Invest in stocks: stocks offer a higher potential for growth than bonds, but come with higher risk.\n",
      "\n",
      "7. Invest in cryptocurrencies: cryptocurrencies offer a decentralized and volatile form of money, but come with significant risks.\n",
      "\n",
      "8. Invest in private equity: private equity offers a higher potential for growth than traditional investments, but comes with significant risks.\n",
      "\n",
      "9. Invest in gold: gold offers a higher potential for growth than traditional investments, but comes with significant risks.\n",
      "\n",
      "10. Invest in real estate investment trusts: real estate investment trusts offer a diversified portfolio of assets, and can provide a stable source of income.\n",
      "\n",
      "overall, it is important to consider your personal financial goals, risk tolerance, and investment strategy when making investment decisions.\n",
      "\n",
      "CPU times: user 18.9 s, sys: 37 ms, total: 18.9 s\n",
      "Wall time: 21.5 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Math"
   ],
   "metadata": {
    "id": "gWrfdGansJuD"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Calculate the answer:\n",
    "3 + 8 - 2 = ?\n",
    "\"\"\"\n",
    ")\n",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AXK2wpXYr7xu",
    "outputId": "61195fc7-31a9-4053-a01f-330f779b405f"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The answer is 11.\n",
      "\n",
      "CPU times: user 332 ms, sys: 2.24 ms, total: 335 ms\n",
      "Wall time: 341 ms\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Think step by step. Calculate the answer:\n",
    "3 + 8 - 2 = ?\n",
    "\"\"\"\n",
    ")\n",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1iv6TbvoxAO5",
    "outputId": "11b77478-3dfe-439b-d052-68882429cb7a"
   },
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "3 + 8 - 2 = 5\n",
      "\n",
      "CPU times: user 428 ms, sys: 88 µs, total: 428 ms\n",
      "Wall time: 432 ms\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Coding"
   ],
   "metadata": {
    "id": "hkXakSAgxIds"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = cleandoc(\n",
    "    \"\"\"\n",
    "Write a function in python that calculates the square of a sum of two numbers.\n",
    "\"\"\"\n",
    ")\n",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mbPCrOQVw389",
    "outputId": "32116aeb-0d40-4f4f-ea8d-a573edbd175b"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Here's a function that calculates the square of a sum of two numbers:\n",
      "\n",
      "```python\n",
      "def square_sum(a, b):\n",
      "    return a**2 + b**2\n",
      "```\n",
      "\n",
      "Here's how you can use it:\n",
      "\n",
      "```python\n",
      "a = 5\n",
      "b = 7\n",
      "result = square_sum(a, b)\n",
      "print(result) # Output: 25\n",
      "```\n",
      "\n",
      "In this example, we have two numbers `a` and `b`, and we want to calculate the square of their sum. We use the `square_sum` function to calculate the sum of squares of `a` and `b`, and then we print the result.\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def square_sum(a, b):\n",
    "    return a**2 + b**2\n",
    "\n",
    "\n",
    "square_sum(5, 7)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WpmS9mCCx3YZ",
    "outputId": "92704120-132c-4eb1-8ac9-b9630dc56bda"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ]
  },
  {
   "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",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T8gGsxK_yF4c",
    "outputId": "521af75a-19ae-425c-8a12-d8c5fe81d14f"
   },
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Here's a function that does what you want:\n",
      "\n",
      "```python\n",
      "def split_into_3_equal_parts(lst):\n",
      "    # split the list into 3 equal parts\n",
      "    left = lst[:len(lst) // 3]\n",
      "    middle = lst[len(lst) // 3 : 2 * len(lst) // 3]\n",
      "    right = lst[2 * len(lst) // 3 : len(lst)]\n",
      "\n",
      "    # randomly select an element from each sublist\n",
      "    return [random.choice(i) for i in zip(left, middle, right)]\n",
      "```\n",
      "\n",
      "This function takes a list `lst` as an argument and returns a list with a random element from each sublist. The function first splits the list into 3 equal parts using the `len` function. Then, it uses the `zip` function to randomly select an element from each sublist. Finally, it returns the resulting list.\n",
      "\n",
      "CPU times: user 10.4 s, sys: 23 ms, total: 10.5 s\n",
      "Wall time: 10.7 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import random  # not in the generated code\n",
    "\n",
    "\n",
    "def split_into_3_equal_parts(lst):\n",
    "    # split the list into 3 equal parts\n",
    "    left = lst[: len(lst) // 3]\n",
    "    middle = lst[len(lst) // 3 : 2 * len(lst) // 3]\n",
    "    right = lst[2 * len(lst) // 3 : len(lst)]\n",
    "\n",
    "    print(left)\n",
    "    print(middle)\n",
    "    print(right)\n",
    "\n",
    "    # randomly select an element from each sublist\n",
    "    return [random.choice(i) for i in zip(left, middle, right)]\n",
    "\n",
    "\n",
    "split_into_3_equal_parts([1, 2, 3, 4, 5, 6, 7, 8, 9])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7XDiNyr0yR2Q",
    "outputId": "de915c5f-6b04-45f9-f84f-f5cc07ed3688"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1, 2, 3]\n",
      "[4, 5, 6]\n",
      "[7, 8, 9]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[4, 8, 9]"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Analyze text"
   ],
   "metadata": {
    "id": "5rliaKsazmZ_"
   }
  },
  {
   "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",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6rq5q7l1ylfv",
    "outputId": "eef4d391-e02c-4788-9461-3f4af38e79d2"
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope that even my worst critics remain on Twitter,\n",
      "because that is what free speech means.\n",
      "\n",
      "- Elon Musk\n",
      "\n",
      "I hope tha\n",
      "\n",
      "CPU times: user 48.4 s, sys: 108 ms, total: 48.5 s\n",
      "Wall time: 50.7 s\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",
    "print(predict(prompt))\n",
    "print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mDUDe2Nm1msp",
    "outputId": "668084ef-ece8-4a77-d1e4-30cdd14d50d3"
   },
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The Reading Comprehension score for Llama 2 7B is **37.5**.\n",
      "\n",
      "CPU times: user 1.24 s, sys: 15.6 ms, total: 1.26 s\n",
      "Wall time: 1.26 s\n"
     ]
    }
   ]
  }
 ]
}