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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "import gradio as gr\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "df = pd.read_csv(\"https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv\", skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "# Drop footers\n",
    "df = df.copy()[~df[\"Model\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "# Drop TBA models\n",
    "df = df.copy()[df[\"Parameters \\n(B)\"] != \"TBA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Model</th>\n",
       "      <th>Lab</th>\n",
       "      <th>Selected \\nplaygrounds</th>\n",
       "      <th>Parameters \\n(B)</th>\n",
       "      <th>Tokens \\ntrained (B)</th>\n",
       "      <th>Ratio T:P\\n(Chinchilla scaling)</th>\n",
       "      <th>Training dataset</th>\n",
       "      <th>Announced\\nβ–Ό</th>\n",
       "      <th>Public?</th>\n",
       "      <th>Released</th>\n",
       "      <th>Paper/\\nRepo</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KOSMOS-1</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>360</td>\n",
       "      <td>225:1</td>\n",
       "      <td>πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>πŸ”΄</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Multimodal large language model (MLLM). Raven’...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LLaMA-65B</td>\n",
       "      <td>Meta AI</td>\n",
       "      <td>https://research.facebook.com/publications/lla...</td>\n",
       "      <td>65</td>\n",
       "      <td>1400</td>\n",
       "      <td>22:1</td>\n",
       "      <td>πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>🟑</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Researchers only, noncommercial only. 'LLaMA-6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MOSS</td>\n",
       "      <td>Fudan University</td>\n",
       "      <td>https://moss.fastnlp.top/</td>\n",
       "      <td>20</td>\n",
       "      <td>430</td>\n",
       "      <td>22:1</td>\n",
       "      <td>πŸ•Έ πŸŒ‹</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Major bandwidth issues: https://www.reuters.co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Luminous Supreme Control</td>\n",
       "      <td>Aleph Alpha</td>\n",
       "      <td>https://app.aleph-alpha.com/playground/completion</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>πŸ†† πŸ“šβ¬† πŸ•Έ πŸ‘₯</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>β€˜Control’ means instruction tuned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Multimodal-CoT</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>https://github.com/amazon-science/mm-cot</td>\n",
       "      <td>0.738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>πŸŒ‹</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Feb/2023</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Models &lt;1B with vision CoT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model               Lab  \\\n",
       "2                  KOSMOS-1         Microsoft   \n",
       "3                 LLaMA-65B           Meta AI   \n",
       "4                      MOSS  Fudan University   \n",
       "5  Luminous Supreme Control       Aleph Alpha   \n",
       "6            Multimodal-CoT            Amazon   \n",
       "\n",
       "                              Selected \\nplaygrounds Parameters \\n(B)  \\\n",
       "2                                                NaN              1.6   \n",
       "3  https://research.facebook.com/publications/lla...               65   \n",
       "4                          https://moss.fastnlp.top/               20   \n",
       "5  https://app.aleph-alpha.com/playground/completion               70   \n",
       "6           https://github.com/amazon-science/mm-cot            0.738   \n",
       "\n",
       "  Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset  \\\n",
       "2                  360                           225:1         πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹   \n",
       "3                 1400                            22:1         πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹   \n",
       "4                  430                            22:1              πŸ•Έ πŸŒ‹   \n",
       "5                  NaN                             NaN         πŸ†† πŸ“šβ¬† πŸ•Έ πŸ‘₯   \n",
       "6                  NaN                             NaN                πŸŒ‹   \n",
       "\n",
       "  Announced\\nβ–Ό Public?  Released Paper/\\nRepo  \\\n",
       "2     Feb/2023       πŸ”΄  Feb/2023            πŸ”—   \n",
       "3     Feb/2023       🟑  Feb/2023            πŸ”—   \n",
       "4     Feb/2023       🟒  Feb/2023            πŸ”—   \n",
       "5     Feb/2023       🟒  Feb/2023            πŸ”—   \n",
       "6     Feb/2023       🟒  Feb/2023            πŸ”—   \n",
       "\n",
       "                                               Notes  \n",
       "2  Multimodal large language model (MLLM). Raven’...  \n",
       "3  Researchers only, noncommercial only. 'LLaMA-6...  \n",
       "4  Major bandwidth issues: https://www.reuters.co...  \n",
       "5                  β€˜Control’ means instruction tuned  \n",
       "6                         Models <1B with vision CoT  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Model</th>\n",
       "      <th>Lab</th>\n",
       "      <th>Selected \\nplaygrounds</th>\n",
       "      <th>Parameters \\n(B)</th>\n",
       "      <th>Tokens \\ntrained (B)</th>\n",
       "      <th>Ratio T:P\\n(Chinchilla scaling)</th>\n",
       "      <th>Training dataset</th>\n",
       "      <th>Announced\\nβ–Ό</th>\n",
       "      <th>Public?</th>\n",
       "      <th>Released</th>\n",
       "      <th>Paper/\\nRepo</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Meena</td>\n",
       "      <td>Google</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6</td>\n",
       "      <td>10000</td>\n",
       "      <td>3,847:1</td>\n",
       "      <td>πŸ‘₯ πŸŒ‹</td>\n",
       "      <td>Jan/2020</td>\n",
       "      <td>πŸ”΄</td>\n",
       "      <td>Jan/2020</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Dialogue model. Trained 61B tokens for 164x ep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>RoBERTa</td>\n",
       "      <td>Meta AI</td>\n",
       "      <td>Hugging Face</td>\n",
       "      <td>0.355</td>\n",
       "      <td>2200</td>\n",
       "      <td>6,198:1</td>\n",
       "      <td>πŸ†† πŸ“š ⬆ πŸ•Έ</td>\n",
       "      <td>Jul/2019</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Jul/2019</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>See cite ROBERTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>GPT-2</td>\n",
       "      <td>OpenAI</td>\n",
       "      <td>Hugging Face</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "      <td>7:1</td>\n",
       "      <td>⬆</td>\n",
       "      <td>Feb/2019</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Nov/2019</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Reddit outbound only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>GPT-1</td>\n",
       "      <td>OpenAI</td>\n",
       "      <td>Hugging Face</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>πŸ“š</td>\n",
       "      <td>Jun/2018</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Jun/2018</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>Books only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>BERT</td>\n",
       "      <td>Google</td>\n",
       "      <td>Hugging Face</td>\n",
       "      <td>0.3</td>\n",
       "      <td>137</td>\n",
       "      <td>457:1</td>\n",
       "      <td>πŸ†† πŸ“š</td>\n",
       "      <td>Oct/2018</td>\n",
       "      <td>🟒</td>\n",
       "      <td>Oct/2018</td>\n",
       "      <td>πŸ”—</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Model      Lab Selected \\nplaygrounds Parameters \\n(B)  \\\n",
       "88    Meena   Google                    NaN              2.6   \n",
       "89  RoBERTa  Meta AI           Hugging Face            0.355   \n",
       "90    GPT-2   OpenAI           Hugging Face              1.5   \n",
       "91    GPT-1   OpenAI           Hugging Face              0.1   \n",
       "92     BERT   Google           Hugging Face              0.3   \n",
       "\n",
       "   Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset  \\\n",
       "88                10000                         3,847:1              πŸ‘₯ πŸŒ‹   \n",
       "89                 2200                         6,198:1          πŸ†† πŸ“š ⬆ πŸ•Έ   \n",
       "90                   10                             7:1                ⬆   \n",
       "91                  NaN                             NaN                πŸ“š   \n",
       "92                  137                           457:1              πŸ†† πŸ“š   \n",
       "\n",
       "   Announced\\nβ–Ό Public?  Released Paper/\\nRepo  \\\n",
       "88     Jan/2020       πŸ”΄  Jan/2020            πŸ”—   \n",
       "89     Jul/2019       🟒  Jul/2019            πŸ”—   \n",
       "90     Feb/2019       🟒  Nov/2019            πŸ”—   \n",
       "91     Jun/2018       🟒  Jun/2018            πŸ”—   \n",
       "92     Oct/2018       🟒  Oct/2018            πŸ”—   \n",
       "\n",
       "                                                Notes  \n",
       "88  Dialogue model. Trained 61B tokens for 164x ep...  \n",
       "89                                   See cite ROBERTA  \n",
       "90                               Reddit outbound only  \n",
       "91                                         Books only  \n",
       "92                                                NaN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |export\n",
    "title = \"\"\"<h1 align=\"center\">The Large Language Models Landscape</h1>\"\"\"\n",
    "description = \"\"\"Large Language Models (LLMs) today come in a variety architectures and capabilities. This interactive landscape provides a visual overview of the most important LLMs, including their training data, size, release date, and whether they are openly accessible or not. It also includes notes on each model to provide additional context. This landscape is derived from data compiled by Dr. Alan D. Thompson at [lifearchitect.ai](https://lifearchitect.ai).\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# |export\n",
    "def value_func():\n",
    "    return df\n",
    "\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(title)\n",
    "    gr.Markdown(description)\n",
    "    gr.DataFrame(value=value_func)\n",
    "\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing server running on port: 7862\n"
     ]
    }
   ],
   "source": [
    "demo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbdev.export import nb_export\n",
    "\n",
    "nb_export(\"app.ipynb\", lib_path=\".\", name=\"app\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hf",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "66e5af1d4a3a75efffc7cd5a7f382675fc3ac06b0697676e06fa85c907378a99"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}