Format markdown

app.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -210,7 +210,7 @@
        "7                  ‘Control’ means instruction tuned  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +376,7 @@
        "96                                                NaN  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,28 +387,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
     "# |export\n",
     "def make_clickable_cell(cell):\n",
-    "    return f'<a  target=\"_blank\" href=\"{cell}\">{cell}</a>'\n"
+    "    if pd.isnull(cell):\n",
+    "        return \"\"\n",
+    "    else:\n",
+    "        return f'<a  target=\"_blank\" href=\"{cell}\">{cell}</a>'\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
     "# |export\n",
-    "df[\"Paper / Repo\"] = df[\"Paper / Repo\"].apply(make_clickable_cell)"
+    "columns_to_click = [\"Paper / Repo\", \"Selected \\nplaygrounds\"]\n",
+    "for col in columns_to_click:\n",
+    "    df[col] = df[col].apply(make_clickable_cell)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 66,
+   "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 / Repo</th>\n",
+       "      <th>Notes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Kosmos-1</td>\n",
+       "      <td>Microsoft</td>\n",
+       "      <td></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>&lt;a  target=\"_blank\" href=\"https://arxiv.org/ab...</td>\n",
+       "      <td>Multimodal large language model (MLLM). Raven’...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LLaMA-65B</td>\n",
+       "      <td>Meta AI</td>\n",
+       "      <td>&lt;a  target=\"_blank\" href=\"Weights leaked: http...</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>&lt;a  target=\"_blank\" href=\"https://research.fac...</td>\n",
+       "      <td>Researchers only, noncommercial only. 'LLaMA-6...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MOSS</td>\n",
+       "      <td>Fudan University</td>\n",
+       "      <td>&lt;a  target=\"_blank\" href=\"https://moss.fastnlp...</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>&lt;a  target=\"_blank\" href=\"https://txsun1997.gi...</td>\n",
+       "      <td>Major bandwidth issues: https://www.reuters.co...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Palmyra</td>\n",
+       "      <td>Writer</td>\n",
+       "      <td>&lt;a  target=\"_blank\" href=\"https://huggingface....</td>\n",
+       "      <td>20</td>\n",
+       "      <td>300</td>\n",
+       "      <td>15:1</td>\n",
+       "      <td>🌋</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>&lt;a  target=\"_blank\" href=\"https://writer.com/b...</td>\n",
+       "      <td>Only up to 5B available open-source 'trained o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Luminous Supreme Control</td>\n",
+       "      <td>Aleph Alpha</td>\n",
+       "      <td>&lt;a  target=\"_blank\" href=\"https://app.aleph-al...</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>&lt;a  target=\"_blank\" href=\"https://docs.aleph-a...</td>\n",
+       "      <td>‘Control’ means instruction tuned</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Model               Lab  \\\n",
+       "3                  Kosmos-1         Microsoft   \n",
+       "4                 LLaMA-65B           Meta AI   \n",
+       "5                      MOSS  Fudan University   \n",
+       "6                   Palmyra            Writer   \n",
+       "7  Luminous Supreme Control       Aleph Alpha   \n",
+       "\n",
+       "                              Selected \\nplaygrounds Parameters \\n(B)  \\\n",
+       "3                                                                 1.6   \n",
+       "4  <a  target=\"_blank\" href=\"Weights leaked: http...               65   \n",
+       "5  <a  target=\"_blank\" href=\"https://moss.fastnlp...               20   \n",
+       "6  <a  target=\"_blank\" href=\"https://huggingface....               20   \n",
+       "7  <a  target=\"_blank\" href=\"https://app.aleph-al...               70   \n",
+       "\n",
+       "  Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset  \\\n",
+       "3                  360                           225:1         🆆 📚⬆ 🕸 🌋   \n",
+       "4                 1400                            22:1         🆆 📚⬆ 🕸 🌋   \n",
+       "5                  430                            22:1              🕸 🌋   \n",
+       "6                  300                            15:1                🌋   \n",
+       "7                  NaN                             NaN         🆆 📚⬆ 🕸 👥   \n",
+       "\n",
+       "  Announced\\n▼ Public?  Released  \\\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",
+       "7     Feb/2023       🟢  Feb/2023   \n",
+       "\n",
+       "                                        Paper / Repo  \\\n",
+       "3  <a  target=\"_blank\" href=\"https://arxiv.org/ab...   \n",
+       "4  <a  target=\"_blank\" href=\"https://research.fac...   \n",
+       "5  <a  target=\"_blank\" href=\"https://txsun1997.gi...   \n",
+       "6  <a  target=\"_blank\" href=\"https://writer.com/b...   \n",
+       "7  <a  target=\"_blank\" href=\"https://docs.aleph-a...   \n",
+       "\n",
+       "                                               Notes  \n",
+       "3  Multimodal large language model (MLLM). Raven’...  \n",
+       "4  Researchers only, noncommercial only. 'LLaMA-6...  \n",
+       "5  Major bandwidth issues: https://www.reuters.co...  \n",
+       "6  Only up to 5B available open-source 'trained o...  \n",
+       "7                  ‘Control’ means instruction tuned  "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,14 +598,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# |export\n",
+    "dtypes = [\"str\" if c not in columns_to_click else \"markdown\" for c in df.columns]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7862\n",
+      "Running on local URL:  http://127.0.0.1:7868\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -435,7 +623,7 @@
     {
      "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>"
+       "<div><iframe src=\"http://127.0.0.1:7868/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -448,7 +636,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 16,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -462,21 +650,21 @@
     "with gr.Blocks() as demo:\n",
     "    gr.Markdown(title)\n",
     "    gr.Markdown(description)\n",
-    "    gr.components.DataFrame(value=value_func)\n",
+    "    gr.components.DataFrame(value=value_func, datatype=dtypes)\n",
     "\n",
     "demo.launch()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Closing server running on port: 7861\n"
+      "Closing server running on port: 7868\n"
      ]
     }
    ],
@@ -486,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [

app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['df', 'title', 'description', 'make_clickable_cell', 'value_func']
+__all__ = ['df', 'columns_to_click', 'title', 'description', 'dtypes', 'make_clickable_cell', 'value_func']
 
 # %% app.ipynb 0
 import gradio as gr
@@ -27,19 +27,29 @@ df = df.copy()[df["Parameters \n(B)"] != "TBA"]
 
 # %% app.ipynb 6
 def make_clickable_cell(cell):
-    return f'<a  target="_blank" href="{cell}">{cell}</a>'
+    if pd.isnull(cell):
+        return ""
+    else:
+        return f'<a  target="_blank" href="{cell}">{cell}</a>'
 
 
 # %% app.ipynb 7
-df["Paper / Repo"] = df["Paper / Repo"].apply(make_clickable_cell)
+columns_to_click = ["Paper / Repo", "Selected \nplaygrounds"]
+for col in columns_to_click:
+    df[col] = df[col].apply(make_clickable_cell)
 
-# %% app.ipynb 8
+
+# %% app.ipynb 9
 title = """<h1 align="center">The Large Language Models Landscape</h1>"""
 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).
 """
 
 
-# %% app.ipynb 9
+# %% app.ipynb 10
+dtypes = ["str" if c not in columns_to_click else "markdown" for c in df.columns]
+
+
+# %% app.ipynb 11
 def value_func():
     return df
 
@@ -47,7 +57,7 @@ def value_func():
 with gr.Blocks() as demo:
     gr.Markdown(title)
     gr.Markdown(description)
-    gr.components.DataFrame(value=value_func)
+    gr.components.DataFrame(value=value_func, datatype=dtypes)
 
 demo.launch()