Add files

app.ipynb

@@ -0,0 +1,480 @@
+{
+ "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\\n▼</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>🆆 📚⬆ 🕸 🌋</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🔴</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🔗</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>🆆 📚⬆ 🕸 🌋</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🟡</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🔗</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>🕸 🌋</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🔗</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>🆆 📚⬆ 🕸 👥</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🔗</td>\n",
+       "      <td>‘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>🌋</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🔗</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         🆆 📚⬆ 🕸 🌋   \n",
+       "3                 1400                            22:1         🆆 📚⬆ 🕸 🌋   \n",
+       "4                  430                            22:1              🕸 🌋   \n",
+       "5                  NaN                             NaN         🆆 📚⬆ 🕸 👥   \n",
+       "6                  NaN                             NaN                🌋   \n",
+       "\n",
+       "  Announced\\n▼ Public?  Released Paper/\\nRepo  \\\n",
+       "2     Feb/2023       🔴  Feb/2023            🔗   \n",
+       "3     Feb/2023       🟡  Feb/2023            🔗   \n",
+       "4     Feb/2023       🟢  Feb/2023            🔗   \n",
+       "5     Feb/2023       🟢  Feb/2023            🔗   \n",
+       "6     Feb/2023       🟢  Feb/2023            🔗   \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                  ‘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\\n▼</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>👥 🌋</td>\n",
+       "      <td>Jan/2020</td>\n",
+       "      <td>🔴</td>\n",
+       "      <td>Jan/2020</td>\n",
+       "      <td>🔗</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>🆆 📚 ⬆ 🕸</td>\n",
+       "      <td>Jul/2019</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Jul/2019</td>\n",
+       "      <td>🔗</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>🔗</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>📚</td>\n",
+       "      <td>Jun/2018</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Jun/2018</td>\n",
+       "      <td>🔗</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>🆆 📚</td>\n",
+       "      <td>Oct/2018</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Oct/2018</td>\n",
+       "      <td>🔗</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              👥 🌋   \n",
+       "89                 2200                         6,198:1          🆆 📚 ⬆ 🕸   \n",
+       "90                   10                             7:1                ⬆   \n",
+       "91                  NaN                             NaN                📚   \n",
+       "92                  137                           457:1              🆆 📚   \n",
+       "\n",
+       "   Announced\\n▼ Public?  Released Paper/\\nRepo  \\\n",
+       "88     Jan/2020       🔴  Jan/2020            🔗   \n",
+       "89     Jul/2019       🟢  Jul/2019            🔗   \n",
+       "90     Feb/2019       🟢  Nov/2019            🔗   \n",
+       "91     Jun/2018       🟢  Jun/2018            🔗   \n",
+       "92     Oct/2018       🟢  Oct/2018            🔗   \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": 26,
+   "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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# |export\n",
+    "def value_func():\n",
+    "    return df\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.DataFrame(value=value_func)\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7862\n"
+     ]
+    }
+   ],
+   "source": [
+    "demo.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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
+}

app.py

@@ -0,0 +1,28 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['df', 'value_func']
+
+# %% app.ipynb 0
+import gradio as gr
+import pandas as pd
+
+# %% app.ipynb 1
+df = pd.read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv", skiprows=1)
+
+# %% app.ipynb 2
+# Drop footers
+df = df.copy()[~df["Model"].isna()]
+
+# %% app.ipynb 3
+# Drop TBA models
+df = df.copy()[df["Parameters \n(B)"] != "TBA"]
+
+# %% app.ipynb 6
+def value_func():
+    return df
+
+with gr.Blocks() as demo:
+    gr.DataFrame(value=value_func)
+
+demo.launch()

requirements.txt

@@ -0,0 +1 @@
+pandas