diff --git a/.gitattributes b/.gitattributes
index 4d2b05a547adbc1c08e62fc38f450c623645cc37..7018b7b05bb7c36b4688f26b973caca6650d6f61 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 demos/calculator/screenshot.gif filter=lfs diff=lfs merge=lfs -text
+demos/kitchen_sink/files/world.mp4 filter=lfs diff=lfs merge=lfs -text
diff --git a/demos/file_explorer_component_events/dir3/dir4/dir_4_foo.txt b/demos/file_explorer_component_events/dir3/dir4/dir_4_foo.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/demos/file_explorer_component_events/run.ipynb b/demos/file_explorer_component_events/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bc81feb0bd465ee19ac6bb40f83353b653c8f78d
--- /dev/null
+++ b/demos/file_explorer_component_events/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: file_explorer_component_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('dir1')\n", "!wget -q -O dir1/bar.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/bar.txt\n", "!wget -q -O dir1/foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir1/foo.txt\n", "os.mkdir('dir2')\n", "!wget -q -O dir2/baz.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/baz.png\n", "!wget -q -O dir2/foo.png https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir2/foo.png\n", "os.mkdir('dir3')\n", "!wget -q -O dir3/dir3_bar.log https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_bar.log\n", "!wget -q -O dir3/dir3_foo.txt https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir3_foo.txt\n", "!wget -q -O dir3/dir4 https://github.com/gradio-app/gradio/raw/main/demo/file_explorer_component_events/dir3/dir4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from pathlib import Path\n", "\n", "base_root = Path(__file__).parent.resolve()\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        dd = gr.Dropdown(label=\"Select File Explorer Root\",\n", "                        value=str(base_root / \"dir1\"),\n", "                        choices=[str(base_root / \"dir1\"), str(base_root / \"dir2\"),\n", "                                 str(base_root / \"dir3\")])\n", "        with gr.Group():\n", "            txt_only_glob = gr.Checkbox(label=\"Show only text files\", value=False)\n", "            ignore_txt_in_glob = gr.Checkbox(label=\"Ignore text files in glob\", value=False)\n", "\n", "    fe = gr.FileExplorer(root_dir=str(base_root / \"dir1\"),\n", "                         glob=\"**/*\", interactive=True)\n", "    textbox = gr.Textbox(label=\"Selected Directory\")\n", "    run = gr.Button(\"Run\")\n", "    total_changes = gr.Number(0, elem_id=\"total-changes\")\n", "\n", "    txt_only_glob.select(lambda s: gr.FileExplorer(glob=\"*.txt\" if s else \"*\") ,\n", "                         inputs=[txt_only_glob], outputs=[fe])\n", "    ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob=\"*.txt\" if s else None),\n", "                            inputs=[ignore_txt_in_glob], outputs=[fe])\n", "\n", "    dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe])\n", "    run.click(lambda s: \",\".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])\n", "    fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes)\n", "\n", "    with gr.Row():\n", "        a = gr.Textbox(elem_id=\"input-box\")\n", "        a.change(lambda x: x, inputs=[a])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/file_explorer_component_events/run.py b/demos/file_explorer_component_events/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8e5349a055983a50d8f941d97aa18233a5e20a4
--- /dev/null
+++ b/demos/file_explorer_component_events/run.py
@@ -0,0 +1,36 @@
+import gradio as gr
+from pathlib import Path
+
+base_root = Path(__file__).parent.resolve()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        dd = gr.Dropdown(label="Select File Explorer Root",
+                        value=str(base_root / "dir1"),
+                        choices=[str(base_root / "dir1"), str(base_root / "dir2"),
+                                 str(base_root / "dir3")])
+        with gr.Group():
+            txt_only_glob = gr.Checkbox(label="Show only text files", value=False)
+            ignore_txt_in_glob = gr.Checkbox(label="Ignore text files in glob", value=False)
+
+    fe = gr.FileExplorer(root_dir=str(base_root / "dir1"),
+                         glob="**/*", interactive=True)
+    textbox = gr.Textbox(label="Selected Directory")
+    run = gr.Button("Run")
+    total_changes = gr.Number(0, elem_id="total-changes")
+
+    txt_only_glob.select(lambda s: gr.FileExplorer(glob="*.txt" if s else "*") ,
+                         inputs=[txt_only_glob], outputs=[fe])
+    ignore_txt_in_glob.select(lambda s: gr.FileExplorer(ignore_glob="*.txt" if s else None),
+                            inputs=[ignore_txt_in_glob], outputs=[fe])
+
+    dd.select(lambda s: gr.FileExplorer(root_dir=s), inputs=[dd], outputs=[fe])
+    run.click(lambda s: ",".join(s) if isinstance(s, list) else s, inputs=[fe], outputs=[textbox])
+    fe.change(lambda num: num + 1, inputs=total_changes, outputs=total_changes)
+
+    with gr.Row():
+        a = gr.Textbox(elem_id="input-box")
+        a.change(lambda x: x, inputs=[a])
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/image_editor_events/run.ipynb b/demos/image_editor_events/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5ca54231867de03662aaa9a1d133caf32227ee32
--- /dev/null
+++ b/demos/image_editor_events/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def predict(im):\n", "    return im[\"composite\"]\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Group():\n", "        with gr.Row():\n", "            im = gr.ImageEditor(\n", "                type=\"numpy\",\n", "                crop_size=\"1:1\",\n", "                elem_id=\"image_editor\",\n", "            )\n", "            im_preview = gr.Image()\n", "    with gr.Group():\n", "        with gr.Row():\n", "\n", "            n_upload = gr.Label(\n", "                0,\n", "                label=\"upload\",\n", "                elem_id=\"upload\",\n", "            )\n", "            n_change = gr.Label(\n", "                0,\n", "                label=\"change\",\n", "                elem_id=\"change\",\n", "            )\n", "            n_input = gr.Label(\n", "                0,\n", "                label=\"input\",\n", "                elem_id=\"input\",\n", "            )\n", "            n_apply = gr.Label(\n", "                0,\n", "                label=\"apply\",\n", "                elem_id=\"apply\",\n", "            )\n", "    clear_btn = gr.Button(\"Clear\", elem_id=\"clear\")\n", "\n", "    im.upload(\n", "        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", "    )\n", "    im.change(\n", "        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", "    )\n", "    im.input(\n", "        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", "    )\n", "    im.apply(\n", "        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", "    )\n", "    im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", "    clear_btn.click(\n", "        lambda: None,\n", "        None,\n", "        im,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/image_editor_events/run.py b/demos/image_editor_events/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..53884466f5fa1bf236e7c8415b408597de559f37
--- /dev/null
+++ b/demos/image_editor_events/run.py
@@ -0,0 +1,60 @@
+import gradio as gr
+
+def predict(im):
+    return im["composite"]
+
+with gr.Blocks() as demo:
+    with gr.Group():
+        with gr.Row():
+            im = gr.ImageEditor(
+                type="numpy",
+                crop_size="1:1",
+                elem_id="image_editor",
+            )
+            im_preview = gr.Image()
+    with gr.Group():
+        with gr.Row():
+
+            n_upload = gr.Label(
+                0,
+                label="upload",
+                elem_id="upload",
+            )
+            n_change = gr.Label(
+                0,
+                label="change",
+                elem_id="change",
+            )
+            n_input = gr.Label(
+                0,
+                label="input",
+                elem_id="input",
+            )
+            n_apply = gr.Label(
+                0,
+                label="apply",
+                elem_id="apply",
+            )
+    clear_btn = gr.Button("Clear", elem_id="clear")
+
+    im.upload(
+        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress="hidden"
+    )
+    im.change(
+        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress="hidden"
+    )
+    im.input(
+        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress="hidden"
+    )
+    im.apply(
+        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress="hidden"
+    )
+    im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden")
+    clear_btn.click(
+        lambda: None,
+        None,
+        im,
+    )
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/image_mod_default_image/images/cheetah1.jpg b/demos/image_mod_default_image/images/cheetah1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134
Binary files /dev/null and b/demos/image_mod_default_image/images/cheetah1.jpg differ
diff --git a/demos/image_mod_default_image/images/lion.jpg b/demos/image_mod_default_image/images/lion.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e9bf9f5d0816d6201b4862088dc74476249a6a70
Binary files /dev/null and b/demos/image_mod_default_image/images/lion.jpg differ
diff --git a/demos/image_mod_default_image/images/logo.png b/demos/image_mod_default_image/images/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..8f1df7156f0a690a2415903061e19c20e24adac4
Binary files /dev/null and b/demos/image_mod_default_image/images/logo.png differ
diff --git a/demos/image_mod_default_image/run.ipynb b/demos/image_mod_default_image/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cc5f2da7e9b7a0150c94f3ce3f8fecc62e3311cb
--- /dev/null
+++ b/demos/image_mod_default_image/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_mod_default_image"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def image_mod(image):\n", "    return image.rotate(45)\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", "    flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", "        os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", "        os.path.join(os.path.abspath(''), \"images/logo.png\")\n", "        ])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(max_file_size=\"70kb\")\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/image_mod_default_image/run.py b/demos/image_mod_default_image/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eb42701a4e949205be80555d8855f41a3edd0fa
--- /dev/null
+++ b/demos/image_mod_default_image/run.py
@@ -0,0 +1,16 @@
+import gradio as gr
+import os
+
+def image_mod(image):
+    return image.rotate(45)
+
+cheetah = os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg")
+
+demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image",
+    flagging_options=["blurry", "incorrect", "other"], examples=[
+        os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
+        os.path.join(os.path.dirname(__file__), "images/logo.png")
+        ])
+
+if __name__ == "__main__":
+    demo.launch(max_file_size="70kb")
diff --git a/demos/image_segmentation/DESCRIPTION.md b/demos/image_segmentation/DESCRIPTION.md
new file mode 100644
index 0000000000000000000000000000000000000000..dbba2ae29e4ed391bfd1681fb2fe7d0efcb34222
--- /dev/null
+++ b/demos/image_segmentation/DESCRIPTION.md
@@ -0,0 +1 @@
+Simple image segmentation using gradio's AnnotatedImage component.
\ No newline at end of file
diff --git a/demos/image_segmentation/requirements.txt b/demos/image_segmentation/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..24ce15ab7ead32f98c7ac3edcd34bb2010ff4326
--- /dev/null
+++ b/demos/image_segmentation/requirements.txt
@@ -0,0 +1 @@
+numpy
diff --git a/demos/image_segmentation/run.ipynb b/demos/image_segmentation/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a4406e9d0af3ced3d3263826db32dd64a9de9bcd
--- /dev/null
+++ b/demos/image_segmentation/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_segmentation\n", "### Simple image segmentation using gradio's AnnotatedImage component.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import random\n", "\n", "with gr.Blocks() as demo:\n", "    section_labels = [\n", "        \"apple\",\n", "        \"banana\",\n", "        \"carrot\",\n", "        \"donut\",\n", "        \"eggplant\",\n", "        \"fish\",\n", "        \"grapes\",\n", "        \"hamburger\",\n", "        \"ice cream\",\n", "        \"juice\",\n", "    ]\n", "\n", "    with gr.Row():\n", "        num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n", "        num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n", "\n", "    with gr.Row():\n", "        img_input = gr.Image()\n", "        img_output = gr.AnnotatedImage(\n", "            color_map={\"banana\": \"#a89a00\", \"carrot\": \"#ffae00\"}\n", "        )\n", "\n", "    section_btn = gr.Button(\"Identify Sections\")\n", "    selected_section = gr.Textbox(label=\"Selected Section\")\n", "\n", "    def section(img, num_boxes, num_segments):\n", "        sections = []\n", "        for a in range(num_boxes):\n", "            x = random.randint(0, img.shape[1])\n", "            y = random.randint(0, img.shape[0])\n", "            w = random.randint(0, img.shape[1] - x)\n", "            h = random.randint(0, img.shape[0] - y)\n", "            sections.append(((x, y, x + w, y + h), section_labels[a]))\n", "        for b in range(num_segments):\n", "            x = random.randint(0, img.shape[1])\n", "            y = random.randint(0, img.shape[0])\n", "            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n", "            mask = np.zeros(img.shape[:2])\n", "            for i in range(img.shape[0]):\n", "                for j in range(img.shape[1]):\n", "                    dist_square = (i - y) ** 2 + (j - x) ** 2\n", "                    if dist_square < r**2:\n", "                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n", "            sections.append((mask, section_labels[b + num_boxes]))\n", "        return (img, sections)\n", "\n", "    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n", "\n", "    def select_section(evt: gr.SelectData):\n", "        return section_labels[evt.index]\n", "\n", "    img_output.select(select_section, None, selected_section)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/image_segmentation/run.py b/demos/image_segmentation/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..af3793f3217683102683eeb9a559bdff92a57066
--- /dev/null
+++ b/demos/image_segmentation/run.py
@@ -0,0 +1,61 @@
+import gradio as gr
+import numpy as np
+import random
+
+with gr.Blocks() as demo:
+    section_labels = [
+        "apple",
+        "banana",
+        "carrot",
+        "donut",
+        "eggplant",
+        "fish",
+        "grapes",
+        "hamburger",
+        "ice cream",
+        "juice",
+    ]
+
+    with gr.Row():
+        num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes")
+        num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments")
+
+    with gr.Row():
+        img_input = gr.Image()
+        img_output = gr.AnnotatedImage(
+            color_map={"banana": "#a89a00", "carrot": "#ffae00"}
+        )
+
+    section_btn = gr.Button("Identify Sections")
+    selected_section = gr.Textbox(label="Selected Section")
+
+    def section(img, num_boxes, num_segments):
+        sections = []
+        for a in range(num_boxes):
+            x = random.randint(0, img.shape[1])
+            y = random.randint(0, img.shape[0])
+            w = random.randint(0, img.shape[1] - x)
+            h = random.randint(0, img.shape[0] - y)
+            sections.append(((x, y, x + w, y + h), section_labels[a]))
+        for b in range(num_segments):
+            x = random.randint(0, img.shape[1])
+            y = random.randint(0, img.shape[0])
+            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
+            mask = np.zeros(img.shape[:2])
+            for i in range(img.shape[0]):
+                for j in range(img.shape[1]):
+                    dist_square = (i - y) ** 2 + (j - x) ** 2
+                    if dist_square < r**2:
+                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
+            sections.append((mask, section_labels[b + num_boxes]))
+        return (img, sections)
+
+    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)
+
+    def select_section(evt: gr.SelectData):
+        return section_labels[evt.index]
+
+    img_output.select(select_section, None, selected_section)
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/interface_random_slider/run.ipynb b/demos/interface_random_slider/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..44f22825874467a3f299e472acdc70de7f9ecc30
--- /dev/null
+++ b/demos/interface_random_slider/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def func(slider_1, slider_2, *args):\n", "    return slider_1 + slider_2 * 5\n", "\n", "demo = gr.Interface(\n", "    func,\n", "    [\n", "        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", "        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", "        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", "        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", "        gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", "        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", "    ],\n", "    \"number\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/interface_random_slider/run.py b/demos/interface_random_slider/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..daa797bc55f0d48ac0d5e413d95d90eb771adc39
--- /dev/null
+++ b/demos/interface_random_slider/run.py
@@ -0,0 +1,20 @@
+import gradio as gr
+
+def func(slider_1, slider_2, *args):
+    return slider_1 + slider_2 * 5
+
+demo = gr.Interface(
+    func,
+    [
+        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"),
+        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"),
+        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"),
+        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"),
+        gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"),
+        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
+    ],
+    "number",
+)
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/kitchen_sink/files/cantina.wav b/demos/kitchen_sink/files/cantina.wav
new file mode 100644
index 0000000000000000000000000000000000000000..41f020438468229763ec4a2321325e5916e09106
Binary files /dev/null and b/demos/kitchen_sink/files/cantina.wav differ
diff --git a/demos/kitchen_sink/files/cheetah1.jpg b/demos/kitchen_sink/files/cheetah1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134
Binary files /dev/null and b/demos/kitchen_sink/files/cheetah1.jpg differ
diff --git a/demos/kitchen_sink/files/lion.jpg b/demos/kitchen_sink/files/lion.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e9bf9f5d0816d6201b4862088dc74476249a6a70
Binary files /dev/null and b/demos/kitchen_sink/files/lion.jpg differ
diff --git a/demos/kitchen_sink/files/logo.png b/demos/kitchen_sink/files/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..8f1df7156f0a690a2415903061e19c20e24adac4
Binary files /dev/null and b/demos/kitchen_sink/files/logo.png differ
diff --git a/demos/kitchen_sink/files/time.csv b/demos/kitchen_sink/files/time.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ddb2035c4ed0377f54ee1602fff9f05ba5daa2db
--- /dev/null
+++ b/demos/kitchen_sink/files/time.csv
@@ -0,0 +1,8 @@
+time,value,price
+1,1,4
+2,3,8
+3,6,12
+4,10,16
+5,15,20
+6,21,24
+7,28,28
\ No newline at end of file
diff --git a/demos/kitchen_sink/files/titanic.csv b/demos/kitchen_sink/files/titanic.csv
new file mode 100644
index 0000000000000000000000000000000000000000..63b68ab0ba98c667f515c52f08c0bbd5573d5330
--- /dev/null
+++ b/demos/kitchen_sink/files/titanic.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
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+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
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+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
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+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
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+110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
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+141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
+147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
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+151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
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+853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
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+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
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+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
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+866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
+867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
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+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
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+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
diff --git a/demos/kitchen_sink/files/tower.jpg b/demos/kitchen_sink/files/tower.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..aec3fa94eedf13f6e0c4ef56e4f669a3dba59fd8
Binary files /dev/null and b/demos/kitchen_sink/files/tower.jpg differ
diff --git a/demos/kitchen_sink/files/world.mp4 b/demos/kitchen_sink/files/world.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..9bce44c33e275d6107240a1101032a7835fd8eed
--- /dev/null
+++ b/demos/kitchen_sink/files/world.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71944d7430c461f0cd6e7fd10cee7eb72786352a3678fc7bc0ae3d410f72aece
+size 1570024
diff --git a/demos/kitchen_sink/requirements.txt b/demos/kitchen_sink/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..24ce15ab7ead32f98c7ac3edcd34bb2010ff4326
--- /dev/null
+++ b/demos/kitchen_sink/requirements.txt
@@ -0,0 +1 @@
+numpy
diff --git a/demos/kitchen_sink/run.ipynb b/demos/kitchen_sink/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..68f1e9bf727b68f67253650eb388b08815633683
--- /dev/null
+++ b/demos/kitchen_sink/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: kitchen_sink"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cheetah1.jpg\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/lion.jpg\n", "!wget -q -O files/logo.png https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/logo.png\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/titanic.csv\n", "!wget -q -O files/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/tower.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "def fn(\n", "    text1,\n", "    text2,\n", "    num,\n", "    slider1,\n", "    slider2,\n", "    single_checkbox,\n", "    checkboxes,\n", "    radio,\n", "    dropdown,\n", "    multi_dropdown,\n", "    im1,\n", "    # im2,\n", "    # im3,\n", "    im4,\n", "    video,\n", "    audio1,\n", "    audio2,\n", "    file,\n", "    df1,\n", "):\n", "    return (\n", "        (text1 if single_checkbox else text2)\n", "        + \", selected:\"\n", "        + \", \".join(checkboxes),  # Text\n", "        {\n", "            \"positive\": num / (num + slider1 + slider2),\n", "            \"negative\": slider1 / (num + slider1 + slider2),\n", "            \"neutral\": slider2 / (num + slider1 + slider2),\n", "        },  # Label\n", "        (audio1[0], np.flipud(audio1[1]))\n", "        if audio1 is not None\n", "        else os.path.join(os.path.abspath(''), \"files/cantina.wav\"),  # Audio\n", "        np.flipud(im1)\n", "        if im1 is not None\n", "        else os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),  # Image\n", "        video\n", "        if video is not None\n", "        else os.path.join(os.path.abspath(''), \"files/world.mp4\"),  # Video\n", "        [\n", "            (\"The\", \"art\"),\n", "            (\"quick brown\", \"adj\"),\n", "            (\"fox\", \"nn\"),\n", "            (\"jumped\", \"vrb\"),\n", "            (\"testing testing testing\", None),\n", "            (\"over\", \"prp\"),\n", "            (\"the\", \"art\"),\n", "            (\"testing\", None),\n", "            (\"lazy\", \"adj\"),\n", "            (\"dogs\", \"nn\"),\n", "            (\".\", \"punc\"),\n", "        ]\n", "        + [(f\"test {x}\", f\"test {x}\") for x in range(10)],  # HighlightedText\n", "        # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\",  x/10) for x in range(-10, 10)],  # HighlightedText\n", "        [\n", "            (\"The testing testing testing\", None),\n", "            (\"over\", 0.6),\n", "            (\"the\", 0.2),\n", "            (\"testing\", None),\n", "            (\"lazy\", -0.1),\n", "            (\"dogs\", 0.4),\n", "            (\".\", 0),\n", "        ]\n", "        + [(\"test\", x / 10) for x in range(-10, 10)],  # HighlightedText\n", "        json.loads(JSONOBJ),  # JSON\n", "        \"<button style='background-color: red'>Click Me: \"\n", "        + radio\n", "        + \"</button>\",  # HTML\n", "        os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", "        df1,  # Dataframe\n", "        np.random.randint(0, 10, (4, 4)),  # Dataframe\n", "    )\n", "\n", "demo = gr.Interface(\n", "    fn,\n", "    inputs=[\n", "        gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", "        gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", "        gr.Number(label=\"Number\", value=42),\n", "        gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", "        gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", "        gr.Checkbox(label=\"Checkbox\"),\n", "        gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", "        gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", "        gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", "        gr.Dropdown(\n", "            label=\"Multiselect Dropdown (Max choice: 2)\",\n", "            choices=CHOICES,\n", "            multiselect=True,\n", "            max_choices=2,\n", "        ),\n", "        gr.Image(label=\"Image\"),\n", "        # gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", "        # gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", "        gr.Image(label=\"Webcam\", sources=[\"webcam\"]),\n", "        gr.Video(label=\"Video\"),\n", "        gr.Audio(label=\"Audio\"),\n", "        gr.Audio(label=\"Microphone\", sources=[\"microphone\"]),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", "    ],\n", "    outputs=[\n", "        gr.Textbox(label=\"Textbox\"),\n", "        gr.Label(label=\"Label\"),\n", "        gr.Audio(label=\"Audio\"),\n", "        gr.Image(label=\"Image\", elem_id=\"output-img\"),\n", "        gr.Video(label=\"Video\"),\n", "        gr.HighlightedText(\n", "            label=\"HighlightedText\", color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", "        ),\n", "        gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", "        gr.JSON(label=\"JSON\"),\n", "        gr.HTML(label=\"HTML\"),\n", "        gr.File(label=\"File\"),\n", "        gr.Dataframe(label=\"Dataframe\"),\n", "        gr.Dataframe(label=\"Numpy\"),\n", "    ],\n", "    examples=[\n", "        [\n", "            \"the quick brown fox\",\n", "            \"jumps over the lazy dog\",\n", "            10,\n", "            12,\n", "            4,\n", "            True,\n", "            [\"foo\", \"baz\"],\n", "            \"baz\",\n", "            \"bar\",\n", "            [\"foo\", \"bar\"],\n", "            os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            # os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            # os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", "            os.path.join(os.path.abspath(''), \"files/world.mp4\"),\n", "            os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", "            os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", "            os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", "            [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", "        ]\n", "    ]\n", "    * 3,\n", "    title=\"Kitchen Sink\",\n", "    description=\"Try out all the components!\",\n", "    article=\"Learn more about [Gradio](http://gradio.app)\",\n", "    cache_examples=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/kitchen_sink/run.py b/demos/kitchen_sink/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ea4833c1436b7d2548eee6248e8509fd333907f
--- /dev/null
+++ b/demos/kitchen_sink/run.py
@@ -0,0 +1,159 @@
+import os
+import json
+
+import numpy as np
+
+import gradio as gr
+
+CHOICES = ["foo", "bar", "baz"]
+JSONOBJ = """{"items":{"item":[{"id": "0001","type": null,"is_good": false,"ppu": 0.55,"batters":{"batter":[{ "id": "1001", "type": "Regular" },{ "id": "1002", "type": "Chocolate" },{ "id": "1003", "type": "Blueberry" },{ "id": "1004", "type": "Devil's Food" }]},"topping":[{ "id": "5001", "type": "None" },{ "id": "5002", "type": "Glazed" },{ "id": "5005", "type": "Sugar" },{ "id": "5007", "type": "Powdered Sugar" },{ "id": "5006", "type": "Chocolate with Sprinkles" },{ "id": "5003", "type": "Chocolate" },{ "id": "5004", "type": "Maple" }]}]}}"""
+
+def fn(
+    text1,
+    text2,
+    num,
+    slider1,
+    slider2,
+    single_checkbox,
+    checkboxes,
+    radio,
+    dropdown,
+    multi_dropdown,
+    im1,
+    # im2,
+    # im3,
+    im4,
+    video,
+    audio1,
+    audio2,
+    file,
+    df1,
+):
+    return (
+        (text1 if single_checkbox else text2)
+        + ", selected:"
+        + ", ".join(checkboxes),  # Text
+        {
+            "positive": num / (num + slider1 + slider2),
+            "negative": slider1 / (num + slider1 + slider2),
+            "neutral": slider2 / (num + slider1 + slider2),
+        },  # Label
+        (audio1[0], np.flipud(audio1[1]))
+        if audio1 is not None
+        else os.path.join(os.path.dirname(__file__), "files/cantina.wav"),  # Audio
+        np.flipud(im1)
+        if im1 is not None
+        else os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),  # Image
+        video
+        if video is not None
+        else os.path.join(os.path.dirname(__file__), "files/world.mp4"),  # Video
+        [
+            ("The", "art"),
+            ("quick brown", "adj"),
+            ("fox", "nn"),
+            ("jumped", "vrb"),
+            ("testing testing testing", None),
+            ("over", "prp"),
+            ("the", "art"),
+            ("testing", None),
+            ("lazy", "adj"),
+            ("dogs", "nn"),
+            (".", "punc"),
+        ]
+        + [(f"test {x}", f"test {x}") for x in range(10)],  # HighlightedText
+        # [("The testing testing testing", None), ("quick brown", 0.2), ("fox", 1), ("jumped", -1), ("testing testing testing", 0), ("over", 0), ("the", 0), ("testing", 0), ("lazy", 1), ("dogs", 0), (".", 1)] + [(f"test {x}",  x/10) for x in range(-10, 10)],  # HighlightedText
+        [
+            ("The testing testing testing", None),
+            ("over", 0.6),
+            ("the", 0.2),
+            ("testing", None),
+            ("lazy", -0.1),
+            ("dogs", 0.4),
+            (".", 0),
+        ]
+        + [("test", x / 10) for x in range(-10, 10)],  # HighlightedText
+        json.loads(JSONOBJ),  # JSON
+        "<button style='background-color: red'>Click Me: "
+        + radio
+        + "</button>",  # HTML
+        os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
+        df1,  # Dataframe
+        np.random.randint(0, 10, (4, 4)),  # Dataframe
+    )
+
+demo = gr.Interface(
+    fn,
+    inputs=[
+        gr.Textbox(value="Lorem ipsum", label="Textbox"),
+        gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"),
+        gr.Number(label="Number", value=42),
+        gr.Slider(10, 20, value=15, label="Slider: 10 - 20"),
+        gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"),
+        gr.Checkbox(label="Checkbox"),
+        gr.CheckboxGroup(label="CheckboxGroup", choices=CHOICES, value=CHOICES[0:2]),
+        gr.Radio(label="Radio", choices=CHOICES, value=CHOICES[2]),
+        gr.Dropdown(label="Dropdown", choices=CHOICES),
+        gr.Dropdown(
+            label="Multiselect Dropdown (Max choice: 2)",
+            choices=CHOICES,
+            multiselect=True,
+            max_choices=2,
+        ),
+        gr.Image(label="Image"),
+        # gr.Image(label="Image w/ Cropper", tool="select"),
+        # gr.Image(label="Sketchpad", source="canvas"),
+        gr.Image(label="Webcam", sources=["webcam"]),
+        gr.Video(label="Video"),
+        gr.Audio(label="Audio"),
+        gr.Audio(label="Microphone", sources=["microphone"]),
+        gr.File(label="File"),
+        gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]),
+    ],
+    outputs=[
+        gr.Textbox(label="Textbox"),
+        gr.Label(label="Label"),
+        gr.Audio(label="Audio"),
+        gr.Image(label="Image", elem_id="output-img"),
+        gr.Video(label="Video"),
+        gr.HighlightedText(
+            label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}
+        ),
+        gr.HighlightedText(label="HighlightedText", show_legend=True),
+        gr.JSON(label="JSON"),
+        gr.HTML(label="HTML"),
+        gr.File(label="File"),
+        gr.Dataframe(label="Dataframe"),
+        gr.Dataframe(label="Numpy"),
+    ],
+    examples=[
+        [
+            "the quick brown fox",
+            "jumps over the lazy dog",
+            10,
+            12,
+            4,
+            True,
+            ["foo", "baz"],
+            "baz",
+            "bar",
+            ["foo", "bar"],
+            os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            # os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
+            os.path.join(os.path.dirname(__file__), "files/world.mp4"),
+            os.path.join(os.path.dirname(__file__), "files/cantina.wav"),
+            os.path.join(os.path.dirname(__file__), "files/cantina.wav"),
+            os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
+            [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],
+        ]
+    ]
+    * 3,
+    title="Kitchen Sink",
+    description="Try out all the components!",
+    article="Learn more about [Gradio](http://gradio.app)",
+    cache_examples=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/kitchen_sink_random/__init__.py b/demos/kitchen_sink_random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/demos/kitchen_sink_random/constants.py b/demos/kitchen_sink_random/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a990fbe669e2802e6d668bd620f545a2bb45734
--- /dev/null
+++ b/demos/kitchen_sink_random/constants.py
@@ -0,0 +1,65 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+import os
+
+def random_plot():
+    start_year = 2020
+    x = np.arange(start_year, start_year + random.randint(0, 10))
+    year_count = x.shape[0]
+    plt_format = "-"
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    series = np.arange(0, year_count, dtype=float)
+    series = series**2
+    series += np.random.rand(year_count)
+    ax.plot(x, series, plt_format)
+    return fig
+
+img_dir = os.path.join(os.path.dirname(__file__), "files")
+file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files")
+model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files")
+highlighted_text_output_1 = [
+    {
+        "entity": "I-LOC",
+        "score": 0.9988978,
+        "index": 2,
+        "word": "Chicago",
+        "start": 5,
+        "end": 12,
+    },
+    {
+        "entity": "I-MISC",
+        "score": 0.9958592,
+        "index": 5,
+        "word": "Pakistani",
+        "start": 22,
+        "end": 31,
+    },
+]
+highlighted_text_output_2 = [
+    {
+        "entity": "I-LOC",
+        "score": 0.9988978,
+        "index": 2,
+        "word": "Chicago",
+        "start": 5,
+        "end": 12,
+    },
+    {
+        "entity": "I-LOC",
+        "score": 0.9958592,
+        "index": 5,
+        "word": "Pakistan",
+        "start": 22,
+        "end": 30,
+    },
+]
+
+highlighted_text = "Does Chicago have any Pakistani restaurants"
+
+def random_model3d():
+    model_3d = random.choice(
+        [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"]
+    )
+    return model_3d
diff --git a/demos/kitchen_sink_random/files/cheetah1.jpeg b/demos/kitchen_sink_random/files/cheetah1.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134
Binary files /dev/null and b/demos/kitchen_sink_random/files/cheetah1.jpeg differ
diff --git a/demos/kitchen_sink_random/files/cheetah1.jpg b/demos/kitchen_sink_random/files/cheetah1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c510ff30e09c1ce410afa499f0bfc3a63c751134
Binary files /dev/null and b/demos/kitchen_sink_random/files/cheetah1.jpg differ
diff --git a/demos/kitchen_sink_random/files/lion.jpg b/demos/kitchen_sink_random/files/lion.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..e9bf9f5d0816d6201b4862088dc74476249a6a70
Binary files /dev/null and b/demos/kitchen_sink_random/files/lion.jpg differ
diff --git a/demos/kitchen_sink_random/requirements.txt b/demos/kitchen_sink_random/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..babdd14a51a41b76892f9ea3f412cc634084086a
--- /dev/null
+++ b/demos/kitchen_sink_random/requirements.txt
@@ -0,0 +1,2 @@
+matplotlib
+pandas
diff --git a/demos/kitchen_sink_random/run.py b/demos/kitchen_sink_random/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2da3656047b78bb7ce91f85d5ece0654372c624
--- /dev/null
+++ b/demos/kitchen_sink_random/run.py
@@ -0,0 +1,97 @@
+import gradio as gr
+from datetime import datetime
+import random
+import string
+import os
+import pandas as pd
+
+from constants import (
+    file_dir,
+    img_dir,
+    highlighted_text,
+    highlighted_text_output_2,
+    highlighted_text_output_1,
+    random_plot,
+    random_model3d,
+)
+
+demo = gr.Interface(
+    lambda *args: args[0],
+    inputs=[
+        gr.Textbox(value=lambda: datetime.now(), label="Current Time"),
+        gr.Number(value=lambda: random.random(), label="Ranom Percentage"),
+        gr.Slider(minimum=-1, maximum=1, randomize=True, label="Slider with randomize"),
+        gr.Slider(
+            minimum=0,
+            maximum=1,
+            value=lambda: random.random(),
+            label="Slider with value func",
+        ),
+        gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"),
+        gr.CheckboxGroup(
+            choices=["a", "b", "c", "d"],
+            value=lambda: random.choice(["a", "b", "c", "d"]),
+            label="Random CheckboxGroup",
+        ),
+        gr.Radio(
+            choices=list(string.ascii_lowercase),
+            value=lambda: random.choice(string.ascii_lowercase),
+        ),
+        gr.Dropdown(
+            choices=["a", "b", "c", "d", "e"],
+            value=lambda: random.choice(["a", "b", "c"]),
+        ),
+        gr.Image(
+            value=lambda: random.choice(
+                [os.path.join(img_dir, img) for img in os.listdir(img_dir)]
+            )
+        ),
+        gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")),
+        gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")),
+        gr.File(
+            value=lambda: random.choice(
+                [os.path.join(file_dir, img) for img in os.listdir(file_dir)]
+            )
+        ),
+        gr.Dataframe(
+            value=lambda: pd.DataFrame(
+                {"random_number_rows": range(random.randint(0, 10))}
+            )
+        ),
+        gr.State(value=lambda: random.choice(string.ascii_lowercase)),
+        gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])),
+        gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])),
+        gr.HighlightedText(
+            value=lambda: random.choice(
+                [
+                    {"text": highlighted_text, "entities": highlighted_text_output_1},
+                    {"text": highlighted_text, "entities": highlighted_text_output_2},
+                ]
+            ),
+        ),
+        gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])),
+        gr.HTML(
+            value=lambda: random.choice(
+                [
+                    '<p style="color:red;">I am red</p>',
+                    '<p style="color:blue;">I am blue</p>',
+                ]
+            )
+        ),
+        gr.Gallery(
+            value=lambda: [os.path.join(img_dir, img) for img in os.listdir(img_dir)]
+        ),
+        gr.Chatbot(
+            value=lambda: random.choice([[("hello", "hi!")], [("bye", "goodbye!")]])
+        ),
+        gr.Model3D(value=random_model3d),
+        gr.Plot(value=random_plot),
+        gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"),
+    ],
+    outputs=[
+        gr.State(value=lambda: random.choice(string.ascii_lowercase))
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/matrix_transpose/run.ipynb b/demos/matrix_transpose/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5df77119c9241222cec8c868f9ef0636906f3d3e
--- /dev/null
+++ b/demos/matrix_transpose/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: matrix_transpose"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def transpose(matrix):\n", "    return matrix.T\n", "\n", "demo = gr.Interface(\n", "    transpose,\n", "    gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3),\n", "    \"numpy\",\n", "    examples=[\n", "        [np.zeros((3, 3)).tolist()],\n", "        [np.ones((2, 2)).tolist()],\n", "        [np.random.randint(0, 10, (3, 10)).tolist()],\n", "        [np.random.randint(0, 10, (10, 3)).tolist()],\n", "        [np.random.randint(0, 10, (10, 10)).tolist()],\n", "    ],\n", "    cache_examples=False\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/matrix_transpose/run.py b/demos/matrix_transpose/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9918373553b1cfc44edf691cb29e9cc30918521
--- /dev/null
+++ b/demos/matrix_transpose/run.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+import gradio as gr
+
+def transpose(matrix):
+    return matrix.T
+
+demo = gr.Interface(
+    transpose,
+    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
+    "numpy",
+    examples=[
+        [np.zeros((3, 3)).tolist()],
+        [np.ones((2, 2)).tolist()],
+        [np.random.randint(0, 10, (3, 10)).tolist()],
+        [np.random.randint(0, 10, (10, 3)).tolist()],
+        [np.random.randint(0, 10, (10, 10)).tolist()],
+    ],
+    cache_examples=False
+)
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/demos/matrix_transpose/screenshot.png b/demos/matrix_transpose/screenshot.png
new file mode 100644
index 0000000000000000000000000000000000000000..a9434e9c1df2828ecc1df700eeaaad9609109f82
Binary files /dev/null and b/demos/matrix_transpose/screenshot.png differ
diff --git a/demos/mini_leaderboard/assets/__init__.py b/demos/mini_leaderboard/assets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/demos/mini_leaderboard/assets/custom_css.css b/demos/mini_leaderboard/assets/custom_css.css
new file mode 100644
index 0000000000000000000000000000000000000000..91a5ff7d986e4b2026bed2175bdf99f847ed05a0
--- /dev/null
+++ b/demos/mini_leaderboard/assets/custom_css.css
@@ -0,0 +1,87 @@
+/* Hides the final AutoEvalColumn */
+#llm-benchmark-tab-table table td:last-child,
+#llm-benchmark-tab-table table th:last-child {
+    display: none;
+}
+
+/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+table td:first-child,
+table th:first-child {
+    max-width: 400px;
+    overflow: auto;
+    white-space: nowrap;
+}
+
+/* Full width space */
+.gradio-container {
+  max-width: 95%!important;
+}
+
+/* Text style and margins */
+.markdown-text {
+    font-size: 16px !important;
+}
+
+#models-to-add-text {
+    font-size: 18px !important;
+}
+
+#citation-button span {
+    font-size: 16px !important;
+}
+
+#citation-button textarea {
+    font-size: 16px !important;
+}
+
+#citation-button > label > button {
+    margin: 6px;
+    transform: scale(1.3);
+}
+
+#search-bar-table-box > div:first-child {
+    background: none;
+    border: none;
+}
+ 
+#search-bar {
+    padding: 0px;
+}
+
+.tab-buttons button {
+    font-size: 20px;
+}
+
+/* Filters style */
+#filter_type{
+    border: 0;
+    padding-left: 0;
+    padding-top: 0;
+}
+#filter_type label {
+    display: flex;
+}
+#filter_type label > span{
+    margin-top: var(--spacing-lg);
+    margin-right: 0.5em;
+}
+#filter_type label > .wrap{
+    width: 103px;
+}
+#filter_type label > .wrap .wrap-inner{  
+    padding: 2px;
+}
+#filter_type label > .wrap .wrap-inner input{
+    width: 1px
+}
+#filter-columns-type{
+    border:0;
+    padding:0.5;
+}
+#filter-columns-size{
+    border:0;
+    padding:0.5;
+}
+#box-filter > .form{
+    border: 0
+}
\ No newline at end of file
diff --git a/demos/mini_leaderboard/assets/leaderboard_data.json b/demos/mini_leaderboard/assets/leaderboard_data.json
new file mode 100644
index 0000000000000000000000000000000000000000..e0d3af4b3db66a038e84d5a47f8b52b994f0387a
--- /dev/null
+++ b/demos/mini_leaderboard/assets/leaderboard_data.json
@@ -0,0 +1 @@
+{"T":{"0":"\ud83d\udd36","1":"\ud83d\udcac","2":"\ud83d\udd36","3":"\ud83d\udd36","4":"\ud83d\udd36","5":"\ud83d\udd36","6":"\ud83d\udd36","7":"\ud83d\udd36","8":"\ud83d\udcac","9":"\ud83d\udd36","10":"\ud83d\udd36","11":"\ud83d\udd36","12":"\ud83d\udd36","13":"\ud83d\udcac","14":"\ud83d\udd36","15":"\ud83d\udd36","16":"\ud83d\udd36","17":"\ud83d\udd36","18":"\ud83d\udcac","19":"\ud83d\udd36","20":"\ud83d\udd36","21":"\ud83d\udd36","22":"\ud83d\udd36","23":"\ud83d\udd36","24":"\ud83d\udd36","25":"\ud83d\udd36","26":"\ud83d\udd36","27":"\ud83d\udd36","28":"\ud83d\udd36","29":"\ud83d\udd36","30":"\ud83d\udd36","31":"\ud83d\udd36","32":"\ud83d\udd36","33":"\ud83d\udd36","34":"\ud83d\udd36","35":"\ud83d\udd36","36":"\ud83d\udd36","37":"\ud83d\udd36","38":"\ud83d\udd36","39":"\ud83d\udd36","40":"\ud83d\udd36","41":"\ud83d\udd36","42":"\ud83d\udd36","43":"\ud83d\udd36","44":"\ud83d\udd36","45":"\ud83d\udcac","46":"\ud83e\udd1d","47":"\ud83d\udd36","48":"\ud83d\udd36","49":"\ud83d\udd36","50":"\ud83d\udd36","51":"\ud83d\udcac","52":"\ud83d\udd36","53":"\ud83d\udd36","54":"\ud83e\udd1d","55":"\ud83e\udd1d","56":"\ud83d\udd36","57":"\ud83d\udd36","58":"\ud83d\udd36","59":"\ud83d\udd36","60":"\ud83d\udd36","61":"\ud83d\udd36","62":"\ud83d\udd36","63":"\ud83d\udd36","64":"\ud83d\udcac","65":"\ud83d\udd36","66":"\ud83e\udd1d","67":"\ud83d\udd36","68":"\ud83d\udd36","69":"\ud83d\udcac","70":"\ud83d\udd36","71":"\ud83d\udd36","72":"\ud83d\udcac","73":"\ud83d\udcac","74":"\ud83d\udcac","75":"\ud83d\udd36","76":"\ud83d\udd36","77":"\ud83d\udcac","78":"\ud83d\udcac","79":"\ud83d\udcac","80":"\ud83d\udcac","81":"\ud83d\udcac","82":"\ud83d\udcac","83":"\ud83d\udd36","84":"\ud83d\udd36","85":"\ud83d\udd36","86":"\ud83d\udd36","87":"\ud83d\udd36","88":"\ud83d\udcac","89":"\ud83d\udd36","90":"\ud83d\udd36","91":"\ud83d\udcac","92":"\ud83d\udd36","93":"\ud83d\udd36","94":"\ud83d\udd36","95":"\ud83d\udd36","96":"\ud83d\udd36","97":"\ud83d\udd36","98":"\ud83d\udd36","99":"\ud83d\udd36","100":"\ud83d\udd36","101":"\ud83d\udd36","102":"\ud83d\udd36","103":"\ud83d\udd36","104":"\ud83d\udd36","105":"\ud83d\udcac","106":"\ud83d\udd36","107":"\ud83d\udd36","108":"\ud83d\udd36","109":"\ud83d\udd36","110":"\ud83d\udd36","111":"\ud83d\udcac","112":"\ud83d\udd36","113":"\ud83d\udd36","114":"\ud83d\udd36","115":"\ud83d\udd36","116":"\ud83d\udd36","117":"\ud83d\udd36","118":"\ud83d\udd36","119":"\ud83d\udd36","120":"\ud83d\udd36","121":"\ud83d\udd36","122":"\ud83d\udd36","123":"\ud83d\udd36","124":"\ud83d\udcac","125":"\ud83d\udcac","126":"\ud83d\udcac","127":"\ud83d\udcac","128":"\ud83d\udd36","129":"\ud83d\udd36","130":"\ud83d\udcac","131":"\ud83d\udcac","132":"\ud83d\udd36","133":"\ud83d\udcac","134":"\ud83d\udd36","135":"\ud83d\udd36","136":"\ud83d\udd36","137":"\ud83d\udcac","138":"\ud83d\udcac","139":"\ud83d\udd36","140":"\ud83d\udd36","141":"\ud83d\udd36","142":"\ud83d\udd36","143":"\ud83d\udcac","144":"\ud83d\udd36","145":"\ud83d\udcac","146":"\ud83d\udd36","147":"\ud83d\udcac","148":"\ud83d\udd36","149":"\ud83d\udcac","150":"\ud83d\udcac","151":"\ud83d\udd36","152":"\ud83d\udd36","153":"\ud83d\udd36","154":"\ud83d\udcac","155":"\ud83d\udd36","156":"\ud83d\udcac","157":"\ud83d\udd36","158":"\ud83d\udcac","159":"\ud83d\udd36","160":"\ud83d\udd36","161":"\ud83d\udd36","162":"\ud83d\udcac","163":"\ud83d\udcac","164":"\ud83d\udd36","165":"\ud83d\udd36","166":"\ud83e\udd1d","167":"\ud83d\udd36","168":"\ud83d\udcac","169":"\ud83d\udcac","170":"\ud83d\udcac","171":"\ud83d\udcac","172":"\ud83d\udd36","173":"\ud83e\udd1d","174":"\ud83d\udd36","175":"\ud83d\udd36","176":"\ud83d\udcac","177":"\ud83e\udd1d","178":"\ud83d\udd36","179":"\ud83d\udcac","180":"\ud83d\udd36","181":"\ud83d\udd36","182":"\ud83d\udfe2","183":"\ud83d\udd36","184":"\ud83d\udd36","185":"\ud83d\udd36","186":"\ud83d\udd36","187":"\ud83d\udd36","188":"\ud83d\udcac","189":"\ud83d\udcac","190":"\ud83d\udd36","191":"\ud83d\udd36","192":"\ud83d\udd36","193":"\ud83d\udd36","194":"\ud83d\udd36","195":"\ud83e\udd1d","196":"\ud83d\udd36","197":"\ud83d\udd36","198":"\ud83d\udd36","199":"\ud83d\udcac","200":"\ud83d\udd36","201":"\ud83d\udcac","202":"\ud83d\udd36","203":"\ud83d\udcac","204":"\ud83d\udd36","205":"\ud83d\udd36","206":"\ud83d\udcac","207":"\ud83d\udd36","208":"\ud83d\udcac","209":"\ud83d\udd36","210":"\ud83d\udd36","211":"\ud83d\udfe2","212":"\ud83d\udcac","213":"\ud83d\udd36","214":"\ud83d\udd36","215":"\ud83d\udd36","216":"\ud83e\udd1d","217":"\ud83d\udcac","218":"\ud83d\udd36","219":"\ud83d\udd36","220":"\ud83d\udd36","221":"\ud83d\udd36","222":"\ud83d\udd36","223":"\ud83d\udd36","224":"\ud83e\udd1d","225":"\ud83d\udcac","226":"\ud83d\udd36","227":"\ud83d\udcac","228":"\ud83e\udd1d","229":"\ud83d\udcac","230":"\ud83d\udd36","231":"\ud83d\udd36","232":"\ud83d\udcac","233":"\ud83d\udd36","234":"\ud83d\udd36","235":"\ud83d\udd36","236":"\ud83d\udd36","237":"\ud83e\udd1d","238":"\ud83d\udd36","239":"\ud83d\udcac","240":"\ud83d\udd36","241":"\ud83d\udd36","242":"\ud83e\udd1d","243":"\ud83d\udd36","244":"\ud83e\udd1d","245":"\ud83d\udd36","246":"\ud83d\udcac","247":"\ud83d\udd36","248":"\ud83d\udd36","249":"\ud83d\udd36","250":"\ud83e\udd1d","251":"\ud83d\udd36","252":"\ud83d\udd36","253":"\ud83d\udcac","254":"\ud83d\udcac","255":"\ud83d\udcac","256":"\ud83d\udd36","257":"\ud83d\udd36","258":"\ud83d\udcac","259":"\ud83d\udd36","260":"\ud83d\udcac","261":"\ud83d\udcac","262":"\ud83d\udcac","263":"\ud83d\udd36","264":"\ud83d\udd36","265":"\ud83d\udcac","266":"\ud83d\udcac","267":"\ud83d\udcac","268":"\ud83d\udd36","269":"\ud83d\udcac","270":"\ud83e\udd1d","271":"\ud83d\udd36","272":"\ud83d\udcac","273":"\ud83d\udd36","274":"\ud83d\udcac","275":"\ud83d\udcac","276":"\ud83e\udd1d","277":"\ud83d\udd36","278":"\ud83d\udd36","279":"\ud83e\udd1d","280":"\ud83d\udd36","281":"\ud83d\udcac","282":"\ud83d\udd36","283":"\ud83d\udd36","284":"\ud83d\udd36"},"Model":{"0":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/davidkim205\/Rhea-72b-v0.5\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">davidkim205\/Rhea-72b-v0.5<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_davidkim205__Rhea-72b-v0.5\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","1":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/MTSAIR\/MultiVerse_70B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">MTSAIR\/MultiVerse_70B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_MTSAIR__MultiVerse_70B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","2":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/MTSAIR\/MultiVerse_70B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">MTSAIR\/MultiVerse_70B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_MTSAIR__MultiVerse_70B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","3":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/SF-Foundation\/Ein-72B-v0.11\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SF-Foundation\/Ein-72B-v0.11<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_SF-Foundation__Ein-72B-v0.11\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","4":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/SF-Foundation\/Ein-72B-v0.13\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SF-Foundation\/Ein-72B-v0.13<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_SF-Foundation__Ein-72B-v0.13\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","5":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/SF-Foundation\/Ein-72B-v0.12\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SF-Foundation\/Ein-72B-v0.12<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_SF-Foundation__Ein-72B-v0.12\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","6":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abacusai\/Smaug-72B-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abacusai\/Smaug-72B-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abacusai__Smaug-72B-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","7":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ibivibiv\/alpaca-dragon-72b-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ibivibiv\/alpaca-dragon-72b-v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ibivibiv__alpaca-dragon-72b-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","8":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/moreh\/MoMo-72B-lora-1.8.7-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">moreh\/MoMo-72B-lora-1.8.7-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_moreh__MoMo-72B-lora-1.8.7-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","9":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","10":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/saltlux\/luxia-21.4b-alignment-v1.0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">saltlux\/luxia-21.4b-alignment-v1.0<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_saltlux__luxia-21.4b-alignment-v1.0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","11":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","12":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/zhengr\/MixTAO-7Bx2-MoE-v8.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">zhengr\/MixTAO-7Bx2-MoE-v8.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_zhengr__MixTAO-7Bx2-MoE-v8.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","13":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yunconglong\/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yunconglong\/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yunconglong__Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","14":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/JaeyeonKang\/CCK_Asura_v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">JaeyeonKang\/CCK_Asura_v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_JaeyeonKang__CCK_Asura_v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","15":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/fblgit\/UNA-SimpleSmaug-34b-v1beta\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">fblgit\/UNA-SimpleSmaug-34b-v1beta<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_fblgit__UNA-SimpleSmaug-34b-v1beta\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","16":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/TomGrc\/FusionNet_34Bx2_MoE_v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">TomGrc\/FusionNet_34Bx2_MoE_v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_TomGrc__FusionNet_34Bx2_MoE_v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","17":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/migtissera\/Tess-72B-v1.5b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">migtissera\/Tess-72B-v1.5b<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_migtissera__Tess-72B-v1.5b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","18":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/moreh\/MoMo-72B-lora-1.8.6-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">moreh\/MoMo-72B-lora-1.8.6-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_moreh__MoMo-72B-lora-1.8.6-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","19":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abacusai\/Smaug-34B-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abacusai\/Smaug-34B-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abacusai__Smaug-34B-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","20":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/Truthful_DPO_TomGrc_FusionNet_34Bx2_MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/Truthful_DPO_TomGrc_FusionNet_34Bx2_MoE<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__Truthful_DPO_TomGrc_FusionNet_34Bx2_MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","21":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ibivibiv\/orthorus-125b-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ibivibiv\/orthorus-125b-v2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ibivibiv__orthorus-125b-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","22":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ConvexAI\/Luminex-34B-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ConvexAI\/Luminex-34B-v0.2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ConvexAI__Luminex-34B-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","23":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yunconglong\/DARE_TIES_13B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yunconglong\/DARE_TIES_13B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yunconglong__DARE_TIES_13B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","24":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yunconglong\/13B_MATH_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yunconglong\/13B_MATH_DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yunconglong__13B_MATH_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","25":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/TomGrc\/FusionNet_34Bx2_MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">TomGrc\/FusionNet_34Bx2_MoE<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_TomGrc__FusionNet_34Bx2_MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","26":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ConvexAI\/Luminex-34B-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ConvexAI\/Luminex-34B-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ConvexAI__Luminex-34B-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","27":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yunconglong\/MoE_13B_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yunconglong\/MoE_13B_DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yunconglong__MoE_13B_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","28":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/JaeyeonKang\/CCK_Asura_v3.0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">JaeyeonKang\/CCK_Asura_v3.0<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_JaeyeonKang__CCK_Asura_v3.0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","29":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","30":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment26-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment26-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment26-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","31":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/MTSAIR\/multi_verse_model\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">MTSAIR\/multi_verse_model<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_MTSAIR__multi_verse_model\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","32":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/chihoonlee10\/T3Q-Mistral-Orca-Math-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">chihoonlee10\/T3Q-Mistral-Orca-Math-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_chihoonlee10__T3Q-Mistral-Orca-Math-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","33":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment26-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment26-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment26-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","34":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/rwitz\/experiment26-truthy-iter-0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">rwitz\/experiment26-truthy-iter-0<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_rwitz__experiment26-truthy-iter-0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","35":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment30-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment30-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment30-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","36":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment28-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment28-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment28-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","37":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/MaziyarPanahi\/Calme-7B-Instruct-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">MaziyarPanahi\/Calme-7B-Instruct-v0.2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_MaziyarPanahi__Calme-7B-Instruct-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","38":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/rwitz\/experiment26-truthy-iter-1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">rwitz\/experiment26-truthy-iter-1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_rwitz__experiment26-truthy-iter-1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","39":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/rwitz\/experiment26-truthy-iter-2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">rwitz\/experiment26-truthy-iter-2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_rwitz__experiment26-truthy-iter-2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","40":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/chlee10\/T3Q-Merge-Mistral7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">chlee10\/T3Q-Merge-Mistral7B<\/a>  <a target=\"_blank\" 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href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Kukedlc__NeuralKrishna-7B-V2-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","79":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abideen\/AlphaMonarch-laser\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abideen\/AlphaMonarch-laser<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abideen__AlphaMonarch-laser\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","80":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/touqir\/Cyrax-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">touqir\/Cyrax-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_touqir__Cyrax-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","81":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Eric111\/UltraCatunaMayo-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Eric111\/UltraCatunaMayo-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Eric111__UltraCatunaMayo-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","82":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abideen\/AlphaMonarch-daser\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abideen\/AlphaMonarch-daser<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abideen__AlphaMonarch-daser\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","83":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment21-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment21-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment21-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","84":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/TomGrc\/FusionNet_7Bx2_MoE_14B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">TomGrc\/FusionNet_7Bx2_MoE_14B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_TomGrc__FusionNet_7Bx2_MoE_14B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","85":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment22-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment22-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment22-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","86":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/Yi-34Bx2-MOE-200K\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/Yi-34Bx2-MOE-200K<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__Yi-34Bx2-MOE-200K\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","87":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/NeverSleep\/MiquMaid-v2-2x70B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">NeverSleep\/MiquMaid-v2-2x70B-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_NeverSleep__MiquMaid-v2-2x70B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","88":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abideen\/AlphaMonarch-dora\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abideen\/AlphaMonarch-dora<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abideen__AlphaMonarch-dora\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","89":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/AiMavenAi\/Prometheus-1.3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">AiMavenAi\/Prometheus-1.3<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_AiMavenAi__Prometheus-1.3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","90":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/Capricorn-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/Capricorn-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__Capricorn-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","91":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yleo\/EmertonMonarch-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yleo\/EmertonMonarch-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yleo__EmertonMonarch-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","92":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment20-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment20-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment20-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","93":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ibivibiv\/multimaster-7b-v6\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ibivibiv\/multimaster-7b-v6<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ibivibiv__multimaster-7b-v6\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","94":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abacusai\/Smaug-Mixtral-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abacusai\/Smaug-Mixtral-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abacusai__Smaug-Mixtral-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","95":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/daxiongshu\/Pluto_24B_DPO_63\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">daxiongshu\/Pluto_24B_DPO_63<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_daxiongshu__Pluto_24B_DPO_63\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","96":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/abacusai\/Smaug-Mixtral-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">abacusai\/Smaug-Mixtral-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_abacusai__Smaug-Mixtral-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","97":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/Phoenix_DPO_60B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/Phoenix_DPO_60B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__Phoenix_DPO_60B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","98":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Weyaxi\/Helion-4x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Weyaxi\/Helion-4x34B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Weyaxi__Helion-4x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","99":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ibivibiv\/multimaster-7b-v4\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ibivibiv\/multimaster-7b-v4<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ibivibiv__multimaster-7b-v4\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","100":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/fblgit\/UNA-34BeagleSimpleMath-32K-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">fblgit\/UNA-34BeagleSimpleMath-32K-v1<\/a>  <a target=\"_blank\" 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href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_fblgit__UNA-34Beagles-32K-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","103":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/one-man-army\/UNA-34Beagles-32K-bf16-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">one-man-army\/UNA-34Beagles-32K-bf16-v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_one-man-army__UNA-34Beagles-32K-bf16-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","104":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/Scorpio-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/Scorpio-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__Scorpio-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","105":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/vicgalle\/ConfigurableBeagle-11B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">vicgalle\/ConfigurableBeagle-11B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_vicgalle__ConfigurableBeagle-11B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","106":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Weyaxi\/Cosmosis-3x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Weyaxi\/Cosmosis-3x34B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Weyaxi__Cosmosis-3x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","107":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/macadeliccc\/WestLake-7B-v2-laser-truthy-dpo\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">macadeliccc\/WestLake-7B-v2-laser-truthy-dpo<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_macadeliccc__WestLake-7B-v2-laser-truthy-dpo\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","108":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment19-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment19-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment19-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","109":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ShinojiResearch\/Senku-70B-Full\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ShinojiResearch\/Senku-70B-Full<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ShinojiResearch__Senku-70B-Full\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","110":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yam-peleg\/Experiment23-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yam-peleg\/Experiment23-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yam-peleg__Experiment23-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","111":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/maywell\/kiqu-70b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">maywell\/kiqu-70b<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_maywell__kiqu-70b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","112":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/WestSeverus-7B-DPO-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/WestSeverus-7B-DPO-v2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__WestSeverus-7B-DPO-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","113":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/migtissera\/Tess-70B-v1.6\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">migtissera\/Tess-70B-v1.6<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_migtissera__Tess-70B-v1.6\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","114":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/alnrg2arg\/test3_sft_16bit\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">alnrg2arg\/test3_sft_16bit<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_alnrg2arg__test3_sft_16bit\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","115":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/Faraday-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/Faraday-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__Faraday-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","116":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Weyaxi\/Astralis-4x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Weyaxi\/Astralis-4x34B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Weyaxi__Astralis-4x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","117":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/Faraday-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/Faraday-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__Faraday-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","118":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/PetroGPT\/WestSeverus-7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">PetroGPT\/WestSeverus-7B-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_PetroGPT__WestSeverus-7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","119":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/Sectumsempra-7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/Sectumsempra-7B-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__Sectumsempra-7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","120":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/NeverSleep\/MiquMaid-v1-70B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">NeverSleep\/MiquMaid-v1-70B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_NeverSleep__MiquMaid-v1-70B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","121":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Weyaxi\/Bagel-Hermes-2x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Weyaxi\/Bagel-Hermes-2x34B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Weyaxi__Bagel-Hermes-2x34B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","122":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/BarryFutureman\/WestLakeX-7B-EvoMerge-Variant2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">BarryFutureman\/WestLakeX-7B-EvoMerge-Variant2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_BarryFutureman__WestLakeX-7B-EvoMerge-Variant2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: 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dotted;\">\ud83d\udcd1<\/a>","129":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/60B_MoE_Coder_v3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/60B_MoE_Coder_v3<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__60B_MoE_Coder_v3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","130":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ChaoticNeutrals\/Eris_Remix_DPO_7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ChaoticNeutrals\/Eris_Remix_DPO_7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ChaoticNeutrals__Eris_Remix_DPO_7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","131":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Eric111\/CatunaLaserPi-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Eric111\/CatunaLaserPi-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Eric111__CatunaLaserPi-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","132":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/jondurbin\/nontoxic-bagel-34b-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">jondurbin\/nontoxic-bagel-34b-v0.2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_jondurbin__nontoxic-bagel-34b-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","133":"<a target=\"_blank\" 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style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">moreh\/MoMo-72B-LoRA-V1.4<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_moreh__MoMo-72B-LoRA-V1.4\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","136":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/MaziyarPanahi\/Calme-7B-Instruct-v0.4\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">MaziyarPanahi\/Calme-7B-Instruct-v0.4<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_MaziyarPanahi__Calme-7B-Instruct-v0.4\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","137":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/kevin009\/llamaRAGdrama\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">kevin009\/llamaRAGdrama<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_kevin009__llamaRAGdrama\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","138":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/vicgalle\/Mixtral-7Bx2-truthy\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">vicgalle\/Mixtral-7Bx2-truthy<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_vicgalle__Mixtral-7Bx2-truthy\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","139":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/moreh\/MoMo-72B-LoRA-V1.4\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: 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href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Sao10K__SOLAR-10.7B-NahIdWin\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","203":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yunconglong\/7Bx4_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yunconglong\/7Bx4_DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yunconglong__7Bx4_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","204":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/gradientai\/v-alpha-tross\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gradientai\/v-alpha-tross<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_gradientai__v-alpha-tross\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","205":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ResplendentAI\/Luna-2x7B-MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ResplendentAI\/Luna-2x7B-MoE<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ResplendentAI__Luna-2x7B-MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","206":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/NousResearch\/Nous-Hermes-2-Mixtral-8x7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">NousResearch\/Nous-Hermes-2-Mixtral-8x7B-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_NousResearch__Nous-Hermes-2-Mixtral-8x7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","207":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ibivibiv\/multimaster-7b-v3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ibivibiv\/multimaster-7b-v3<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ibivibiv__multimaster-7b-v3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","208":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/yunconglong\/7Bx4_DPO_2e\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">yunconglong\/7Bx4_DPO_2e<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_yunconglong__7Bx4_DPO_2e\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","209":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/argilla\/notux-8x7b-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">argilla\/notux-8x7b-v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_argilla__notux-8x7b-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","210":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/The-Face-Of-Goonery\/HuginnV5.5-12.6B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">The-Face-Of-Goonery\/HuginnV5.5-12.6B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_The-Face-Of-Goonery__HuginnV5.5-12.6B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","211":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Qwen\/Qwen1.5-72B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Qwen\/Qwen1.5-72B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Qwen__Qwen1.5-72B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","212":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/logicker\/SkkuDS-DPO-72B-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">logicker\/SkkuDS-DPO-72B-v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_logicker__SkkuDS-DPO-72B-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","213":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_VAGOsolutions__SauerkrautLM-Mixtral-8x7B-Instruct\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","214":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Himitsui\/Kaiju-11B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Himitsui\/Kaiju-11B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Himitsui__Kaiju-11B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","215":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/PetroGPT\/Severus-7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">PetroGPT\/Severus-7B-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_PetroGPT__Severus-7B-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","216":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/S-miguel\/The-Trinity-Coder-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">S-miguel\/The-Trinity-Coder-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_S-miguel__The-Trinity-Coder-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","217":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/logicker\/SkkuDS-DPO-72B-v3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">logicker\/SkkuDS-DPO-72B-v3<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_logicker__SkkuDS-DPO-72B-v3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","218":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/PSanni\/MPOMixtral-8x7B-Instruct-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">PSanni\/MPOMixtral-8x7B-Instruct-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_PSanni__MPOMixtral-8x7B-Instruct-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","219":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cloudyu\/19B_TRUTH_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cloudyu\/19B_TRUTH_DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cloudyu__19B_TRUTH_DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","220":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/JaeyeonKang\/CCK_Gony_v3.3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">JaeyeonKang\/CCK_Gony_v3.3<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_JaeyeonKang__CCK_Gony_v3.3\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","221":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_VAGOsolutions__SauerkrautLM-Mixtral-8x7B-Instruct\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","222":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/tenyx\/TenyxChat-8x7B-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">tenyx\/TenyxChat-8x7B-v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_tenyx__TenyxChat-8x7B-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","223":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/mistralai\/Mixtral-8x7B-Instruct-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">mistralai\/Mixtral-8x7B-Instruct-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_mistralai__Mixtral-8x7B-Instruct-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","224":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ChaoticNeutrals\/RPMix-4x7B-MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ChaoticNeutrals\/RPMix-4x7B-MoE<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ChaoticNeutrals__RPMix-4x7B-MoE\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","225":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/SJ-Donald\/SJ-SOLAR-10.7b-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SJ-Donald\/SJ-SOLAR-10.7b-DPO<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_SJ-Donald__SJ-SOLAR-10.7b-DPO\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","226":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/senseable\/garten2-7b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">senseable\/garten2-7b<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_senseable__garten2-7b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","227":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Azure99\/blossom-v5-34b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Azure99\/blossom-v5-34b<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Azure99__blossom-v5-34b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","228":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Sao10K\/Fimbulvetr-11B-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Sao10K\/Fimbulvetr-11B-v2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Sao10K__Fimbulvetr-11B-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","229":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/mistralai\/Mixtral-8x7B-Instruct-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">mistralai\/Mixtral-8x7B-Instruct-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_mistralai__Mixtral-8x7B-Instruct-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","230":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/FelixChao\/Severus-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">FelixChao\/Severus-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_FelixChao__Severus-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","231":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Himitsui\/KuroMitsu-11B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Himitsui\/KuroMitsu-11B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Himitsui__KuroMitsu-11B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","232":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/maywell\/PiVoT-SUS-RP\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">maywell\/PiVoT-SUS-RP<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_maywell__PiVoT-SUS-RP\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","233":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/jondurbin\/bagel-dpo-8x7b-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">jondurbin\/bagel-dpo-8x7b-v0.2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_jondurbin__bagel-dpo-8x7b-v0.2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","234":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/ignos\/Mistral-T5-7B-v1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">ignos\/Mistral-T5-7B-v1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_ignos__Mistral-T5-7B-v1\" style=\"color: var(--link-text-color); text-decoration: 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text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","237":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/Sao10K\/Fimbulvetr-11B-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Sao10K\/Fimbulvetr-11B-v2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_Sao10K__Fimbulvetr-11B-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","238":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/SanjiWatsuki\/Kunoichi-DPO-v2-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SanjiWatsuki\/Kunoichi-DPO-v2-7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_SanjiWatsuki__Kunoichi-DPO-v2-7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","239":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cognitivecomputations\/mixtral-instruct-0.1-laser\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cognitivecomputations\/mixtral-instruct-0.1-laser<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cognitivecomputations__mixtral-instruct-0.1-laser\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","240":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/cognitivecomputations\/laserxtral\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">cognitivecomputations\/laserxtral<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_cognitivecomputations__laserxtral\" style=\"color: var(--link-text-color); text-decoration: 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underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","243":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/JaeyeonKang\/CCK_Gony_v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">JaeyeonKang\/CCK_Gony_v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_JaeyeonKang__CCK_Gony_v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","244":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/R136a1\/InfinityKuno-2x7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">R136a1\/InfinityKuno-2x7B<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_R136a1__InfinityKuno-2x7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","245":"<a 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href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_adonlee__Mistral_7B_SFT_DPO_v0\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","254":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/mncai\/yi-34B-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">mncai\/yi-34B-v2<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_mncai__yi-34B-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","255":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/NousResearch\/Nous-Hermes-2-Mixtral-8x7B-SFT\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">NousResearch\/Nous-Hermes-2-Mixtral-8x7B-SFT<\/a>  <a target=\"_blank\" 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href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_jan-hq__supermario-slerp-v2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","280":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/dillfrescott\/amadeus-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">dillfrescott\/amadeus-v0.1<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_dillfrescott__amadeus-v0.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","281":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/OpenBuddy\/openbuddy-deepseek-67b-v15.3-4k\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">OpenBuddy\/openbuddy-deepseek-67b-v15.3-4k<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_OpenBuddy__openbuddy-deepseek-67b-v15.3-4k\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","282":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/KnutJaegersberg\/Deita-20b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">KnutJaegersberg\/Deita-20b<\/a>  <a target=\"_blank\" href=\"https:\/\/huggingface.co\/datasets\/open-llm-leaderboard\/details_KnutJaegersberg__Deita-20b\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">\ud83d\udcd1<\/a>","283":"<a target=\"_blank\" href=\"https:\/\/huggingface.co\/LDCC\/LDCC-SOLAR-10.7B\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">LDCC\/LDCC-SOLAR-10.7B<\/a>  <a target=\"_blank\" 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O-Jawade","157":"abacusai\/Liberated-Qwen1.5-72B","158":"dddsaty\/SOLAR-Instruct-ko-Adapter-Attach","159":"macadeliccc\/SOLAR-10.7b-Instruct-truthy-dpo","160":"abacusai\/Liberated-Qwen1.5-72B","161":"cloudyu\/19B_MATH_DPO","162":"dhanushreddy29\/BrokenKeyboard","163":"fblgit\/UNA-SOLAR-10.7B-Instruct-v1.0","164":"fblgit\/UNA-POLAR-10.7B-InstructMath-v2","165":"fblgit\/UNAversal-2x7B-v1","166":"dddsaty\/Merge_Sakura_Solar","167":"rishiraj\/meow","168":"vicgalle\/ConfigurableSOLAR-10.7B","169":"fblgit\/UNA-TheBeagle-7b-v1","170":"vicgalle\/OpenBeagle-11B","171":"InferenceIllusionist\/Excalibur-7b-DPO","172":"JaeyeonKang\/CCK_Gony_v3","173":"Sao10K\/Typhon-Mixtral-v1","174":"fblgit\/UNAversal-8x7B-v1beta","175":"sophosympatheia\/Aurora-Nights-70B-v1.0","176":"allenai\/tulu-2-dpo-70b","177":"Steelskull\/Lumosia-v2-MoE-4x10.7","178":"NousResearch\/Nous-Hermes-2-Yi-34B","179":"decruz07\/kellemar-DPO-Orca-Distilled-7B-SLERP","180":"abacusai\/MM-Orc-Vic-bagel-34b-c1000","181":"JaeyeonKang\/CCK_Asura_v2","182":"Qwen\/Qwen-72B","183":"yam-peleg\/Experiment7-7B","184":"macadeliccc\/SOLAR-10.7b-Instruct-dpo","185":"yam-peleg\/Experiment15-7B","186":"yam-peleg\/Experiment10-7B","187":"yam-peleg\/Experiment8-7B","188":"cloudyu\/Mixtral-8x7B-Instruct-v0.1-DPO","189":"Neuronovo\/neuronovo-9B-v0.4","190":"cloudyu\/Mixtral_7Bx5_MoE_30B","191":"yam-peleg\/Experiment9-7B","192":"yam-peleg\/Experiment1-7B","193":"yam-peleg\/Experiment2-7B","194":"yam-peleg\/Experiment4-7B","195":"Sao10K\/Franziska-Mixtral-v1","196":"NousResearch\/Nous-Hermes-2-Mixtral-8x7B-DPO","197":"ResplendentAI\/DaturaCookie_7B","198":"ibndias\/Nous-Hermes-2-MoE-2x34B","199":"gradientai\/v-alpha-tross","200":"carsenk\/flippa-exp26-v3-7b","201":"SUSTech\/SUS-Chat-34B","202":"Sao10K\/SOLAR-10.7B-NahIdWin","203":"yunconglong\/7Bx4_DPO","204":"gradientai\/v-alpha-tross","205":"ResplendentAI\/Luna-2x7B-MoE","206":"NousResearch\/Nous-Hermes-2-Mixtral-8x7B-DPO","207":"ibivibiv\/multimaster-7b-v3","208":"yunconglong\/7Bx4_DPO_2e","209":"argilla\/notux-8x7b-v1","210":"The-Face-Of-Goonery\/HuginnV5.5-12.6B","211":"Qwen\/Qwen1.5-72B","212":"logicker\/SkkuDS-DPO-72B-v1","213":"VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct","214":"Himitsui\/Kaiju-11B","215":"PetroGPT\/Severus-7B-DPO","216":"S-miguel\/The-Trinity-Coder-7B","217":"logicker\/SkkuDS-DPO-72B-v3","218":"PSanni\/MPOMixtral-8x7B-Instruct-v0.1","219":"cloudyu\/19B_TRUTH_DPO","220":"JaeyeonKang\/CCK_Gony_v3.3","221":"VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct","222":"tenyx\/TenyxChat-8x7B-v1","223":"mistralai\/Mixtral-8x7B-Instruct-v0.1","224":"ChaoticNeutrals\/RPMix-4x7B-MoE","225":"SJ-Donald\/SJ-SOLAR-10.7b-DPO","226":"senseable\/garten2-7b","227":"Azure99\/blossom-v5-34b","228":"Sao10K\/Fimbulvetr-11B-v2","229":"mistralai\/Mixtral-8x7B-Instruct-v0.1","230":"FelixChao\/Severus-7B","231":"Himitsui\/KuroMitsu-11B","232":"maywell\/PiVoT-SUS-RP","233":"jondurbin\/bagel-dpo-8x7b-v0.2","234":"ignos\/Mistral-T5-7B-v1","235":"SanjiWatsuki\/Kunoichi-DPO-v2-7B","236":"Brillibits\/Instruct_Mixtral-8x7B-v0.1_Dolly15K","237":"Sao10K\/Fimbulvetr-11B-v2","238":"SanjiWatsuki\/Kunoichi-DPO-v2-7B","239":"cognitivecomputations\/mixtral-instruct-0.1-laser","240":"cognitivecomputations\/laserxtral","241":"OpenBuddy\/openbuddy-deepseek-67b-v15.2","242":"macadeliccc\/piccolo-math-2x7b","243":"JaeyeonKang\/CCK_Gony_v0.1","244":"R136a1\/InfinityKuno-2x7B","245":"jan-ai\/Solar-10.7B-SLERP","246":"mncai\/yi-34B-v3","247":"NeverSleep\/CausalLM-RP-34B","248":"Sao10K\/Fimbulvetr-10.7B-v1","249":"SanjiWatsuki\/Kunoichi-DPO-7B","250":"jan-hq\/supermario-slerp-v3","251":"viethq188\/LeoScorpius-7B","252":"JaeyeonKang\/CCK_Gony_v3.1","253":"adonlee\/Mistral_7B_SFT_DPO_v0","254":"mncai\/yi-34B-v2","255":"NousResearch\/Nous-Hermes-2-Mixtral-8x7B-SFT","256":"CausalLM\/72B-preview-llamafied-qwen-llamafy","257":"OpenPipe\/mistral-ft-optimized-1218","258":"bn22\/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED","259":"arlineka\/Brunhilde-2x7b-MOE-DPO-v.01.5","260":"OpenBuddy\/openbuddy-deepseek-67b-v18.1-4k","261":"deepseek-ai\/deepseek-llm-67b-chat","262":"mlabonne\/NeuralDarewin-7B","263":"OpenBuddy\/openbuddy-deepseek-67b-v15.1","264":"migtissera\/Tess-M-Creative-v1.0","265":"VitalContribution\/Evangelion-7B","266":"bhenrym14\/platypus-yi-34b","267":"RatanRohith\/NeuralPizza-7B-V0.3","268":"PracticeLLM\/SOLAR-tail-10.7B-Merge-v1.0","269":"Azure99\/blossom-v4-yi-34b","270":"llmixer\/BigWeave-v15-103b","271":"MaziyarPanahi\/Calme-7B-Instruct-v0.1","272":"Samee-ur\/NeuralPipe-7B-slerp-DPO","273":"VAGOsolutions\/SauerkrautLM-14b-MoE-LaserChat","274":"RatanRohith\/NeuralPizza-7B-V0.2","275":"RatanRohith\/NeuralPizza-7B-V0.1","276":"R136a1\/InfinityKumon-2x7B","277":"deepseek-ai\/deepseek-llm-67b-chat","278":"Sao10K\/14B-Glacier-Stack","279":"jan-hq\/supermario-slerp-v2","280":"dillfrescott\/amadeus-v0.1","281":"OpenBuddy\/openbuddy-deepseek-67b-v15.3-4k","282":"KnutJaegersberg\/Deita-20b","283":"LDCC\/LDCC-SOLAR-10.7B","284":"LDCC\/LDCC-SOLAR-10.7B"}}
\ No newline at end of file
diff --git a/demos/mini_leaderboard/requirements.txt b/demos/mini_leaderboard/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb6c7ed7ec60dafcf523d2e12daa17abc92ae384
--- /dev/null
+++ b/demos/mini_leaderboard/requirements.txt
@@ -0,0 +1 @@
+pandas
diff --git a/demos/mini_leaderboard/run.ipynb b/demos/mini_leaderboard/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..db8e589ee0769efec56b53d05d16f9087238de01
--- /dev/null
+++ b/demos/mini_leaderboard/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: mini_leaderboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pandas "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('assets')\n", "!wget -q -O assets/__init__.py https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/__init__.py\n", "!wget -q -O assets/custom_css.css https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/custom_css.css\n", "!wget -q -O assets/leaderboard_data.json https://github.com/gradio-app/gradio/raw/main/demo/mini_leaderboard/assets/leaderboard_data.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", "abs_path = Path(__file__).parent.absolute()\n", "\n", "df = pd.read_json(str(abs_path / \"assets/leaderboard_data.json\"))\n", "invisible_df = df.copy()\n", "\n", "COLS = [\n", "    \"T\",\n", "    \"Model\",\n", "    \"Average \u2b06\ufe0f\",\n", "    \"ARC\",\n", "    \"HellaSwag\",\n", "    \"MMLU\",\n", "    \"TruthfulQA\",\n", "    \"Winogrande\",\n", "    \"GSM8K\",\n", "    \"Type\",\n", "    \"Architecture\",\n", "    \"Precision\",\n", "    \"Merged\",\n", "    \"Hub License\",\n", "    \"#Params (B)\",\n", "    \"Hub \u2764\ufe0f\",\n", "    \"Model sha\",\n", "    \"model_name_for_query\",\n", "]\n", "ON_LOAD_COLS = [\n", "    \"T\",\n", "    \"Model\",\n", "    \"Average \u2b06\ufe0f\",\n", "    \"ARC\",\n", "    \"HellaSwag\",\n", "    \"MMLU\",\n", "    \"TruthfulQA\",\n", "    \"Winogrande\",\n", "    \"GSM8K\",\n", "    \"model_name_for_query\",\n", "]\n", "TYPES = [\n", "    \"str\",\n", "    \"markdown\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"number\",\n", "    \"str\",\n", "    \"str\",\n", "    \"str\",\n", "    \"str\",\n", "    \"bool\",\n", "    \"str\",\n", "    \"number\",\n", "    \"number\",\n", "    \"bool\",\n", "    \"str\",\n", "    \"bool\",\n", "    \"bool\",\n", "    \"str\",\n", "]\n", "NUMERIC_INTERVALS = {\n", "    \"?\": pd.Interval(-1, 0, closed=\"right\"),\n", "    \"~1.5\": pd.Interval(0, 2, closed=\"right\"),\n", "    \"~3\": pd.Interval(2, 4, closed=\"right\"),\n", "    \"~7\": pd.Interval(4, 9, closed=\"right\"),\n", "    \"~13\": pd.Interval(9, 20, closed=\"right\"),\n", "    \"~35\": pd.Interval(20, 45, closed=\"right\"),\n", "    \"~60\": pd.Interval(45, 70, closed=\"right\"),\n", "    \"70+\": pd.Interval(70, 10000, closed=\"right\"),\n", "}\n", "MODEL_TYPE = [str(s) for s in df[\"T\"].unique()]\n", "Precision = [str(s) for s in df[\"Precision\"].unique()]\n", "\n", "# Searching and filtering\n", "def update_table(\n", "    hidden_df: pd.DataFrame,\n", "    columns: list,\n", "    type_query: list,\n", "    precision_query: str,\n", "    size_query: list,\n", "    query: str,\n", "):\n", "    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)  # type: ignore\n", "    filtered_df = filter_queries(query, filtered_df)\n", "    df = select_columns(filtered_df, columns)\n", "    return df\n", "\n", "def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:\n", "    return df[(df[\"model_name_for_query\"].str.contains(query, case=False))]  # type: ignore\n", "\n", "def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:\n", "    # We use COLS to maintain sorting\n", "    filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]\n", "    return filtered_df  # type: ignore\n", "\n", "def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:\n", "    final_df = []\n", "    if query != \"\":\n", "        queries = [q.strip() for q in query.split(\";\")]\n", "        for _q in queries:\n", "            _q = _q.strip()\n", "            if _q != \"\":\n", "                temp_filtered_df = search_table(filtered_df, _q)\n", "                if len(temp_filtered_df) > 0:\n", "                    final_df.append(temp_filtered_df)\n", "        if len(final_df) > 0:\n", "            filtered_df = pd.concat(final_df)\n", "            filtered_df = filtered_df.drop_duplicates(  # type: ignore\n", "                subset=[\"Model\", \"Precision\", \"Model sha\"]\n", "            )\n", "\n", "    return filtered_df\n", "\n", "def filter_models(\n", "    df: pd.DataFrame,\n", "    type_query: list,\n", "    size_query: list,\n", "    precision_query: list,\n", ") -> pd.DataFrame:\n", "    # Show all models\n", "    filtered_df = df\n", "\n", "    type_emoji = [t[0] for t in type_query]\n", "    filtered_df = filtered_df.loc[df[\"T\"].isin(type_emoji)]\n", "    filtered_df = filtered_df.loc[df[\"Precision\"].isin(precision_query + [\"None\"])]\n", "\n", "    numeric_interval = pd.IntervalIndex(\n", "        sorted([NUMERIC_INTERVALS[s] for s in size_query])  # type: ignore\n", "    )\n", "    params_column = pd.to_numeric(df[\"#Params (B)\"], errors=\"coerce\")\n", "    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))  # type: ignore\n", "    filtered_df = filtered_df.loc[mask]\n", "\n", "    return filtered_df\n", "\n", "demo = gr.Blocks(css=str(abs_path / \"assets/leaderboard_data.json\"))\n", "with demo:\n", "    gr.Markdown(\"\"\"Test Space of the LLM Leaderboard\"\"\", elem_classes=\"markdown-text\")\n", "\n", "    with gr.Tabs(elem_classes=\"tab-buttons\") as tabs:\n", "        with gr.TabItem(\"\ud83c\udfc5 LLM Benchmark\", elem_id=\"llm-benchmark-tab-table\", id=0):\n", "            with gr.Row():\n", "                with gr.Column():\n", "                    with gr.Row():\n", "                        search_bar = gr.Textbox(\n", "                            placeholder=\" \ud83d\udd0d Search for your model (separate multiple queries with `;`) and press ENTER...\",\n", "                            show_label=False,\n", "                            elem_id=\"search-bar\",\n", "                        )\n", "                    with gr.Row():\n", "                        shown_columns = gr.CheckboxGroup(\n", "                            choices=COLS,\n", "                            value=ON_LOAD_COLS,\n", "                            label=\"Select columns to show\",\n", "                            elem_id=\"column-select\",\n", "                            interactive=True,\n", "                        )\n", "                with gr.Column(min_width=320):\n", "                    filter_columns_type = gr.CheckboxGroup(\n", "                        label=\"Model types\",\n", "                        choices=MODEL_TYPE,\n", "                        value=MODEL_TYPE,\n", "                        interactive=True,\n", "                        elem_id=\"filter-columns-type\",\n", "                    )\n", "                    filter_columns_precision = gr.CheckboxGroup(\n", "                        label=\"Precision\",\n", "                        choices=Precision,\n", "                        value=Precision,\n", "                        interactive=True,\n", "                        elem_id=\"filter-columns-precision\",\n", "                    )\n", "                    filter_columns_size = gr.CheckboxGroup(\n", "                        label=\"Model sizes (in billions of parameters)\",\n", "                        choices=list(NUMERIC_INTERVALS.keys()),\n", "                        value=list(NUMERIC_INTERVALS.keys()),\n", "                        interactive=True,\n", "                        elem_id=\"filter-columns-size\",\n", "                    )\n", "\n", "            leaderboard_table = gr.components.Dataframe(\n", "                value=df[ON_LOAD_COLS],  # type: ignore\n", "                headers=ON_LOAD_COLS,\n", "                datatype=TYPES,\n", "                elem_id=\"leaderboard-table\",\n", "                interactive=False,\n", "                visible=True,\n", "                column_widths=[\"2%\", \"33%\"],\n", "            )\n", "\n", "            # Dummy leaderboard for handling the case when the user uses backspace key\n", "            hidden_leaderboard_table_for_search = gr.components.Dataframe(\n", "                value=invisible_df[COLS],  # type: ignore\n", "                headers=COLS,\n", "                datatype=TYPES,\n", "                visible=False,\n", "            )\n", "            search_bar.submit(\n", "                update_table,\n", "                [\n", "                    hidden_leaderboard_table_for_search,\n", "                    shown_columns,\n", "                    filter_columns_type,\n", "                    filter_columns_precision,\n", "                    filter_columns_size,\n", "                    search_bar,\n", "                ],\n", "                leaderboard_table,\n", "            )\n", "            for selector in [\n", "                shown_columns,\n", "                filter_columns_type,\n", "                filter_columns_precision,\n", "                filter_columns_size,\n", "            ]:\n", "                selector.change(\n", "                    update_table,\n", "                    [\n", "                        hidden_leaderboard_table_for_search,\n", "                        shown_columns,\n", "                        filter_columns_type,\n", "                        filter_columns_precision,\n", "                        filter_columns_size,\n", "                        search_bar,\n", "                    ],\n", "                    leaderboard_table,\n", "                    queue=True,\n", "                )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.queue(default_concurrency_limit=40).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demos/mini_leaderboard/run.py b/demos/mini_leaderboard/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eef4c3bc6a911152efb650b3539e9531164444a
--- /dev/null
+++ b/demos/mini_leaderboard/run.py
@@ -0,0 +1,236 @@
+import gradio as gr
+import pandas as pd
+from pathlib import Path
+
+abs_path = Path(__file__).parent.absolute()
+
+df = pd.read_json(str(abs_path / "assets/leaderboard_data.json"))
+invisible_df = df.copy()
+
+COLS = [
+    "T",
+    "Model",
+    "Average ⬆️",
+    "ARC",
+    "HellaSwag",
+    "MMLU",
+    "TruthfulQA",
+    "Winogrande",
+    "GSM8K",
+    "Type",
+    "Architecture",
+    "Precision",
+    "Merged",
+    "Hub License",
+    "#Params (B)",
+    "Hub ❤️",
+    "Model sha",
+    "model_name_for_query",
+]
+ON_LOAD_COLS = [
+    "T",
+    "Model",
+    "Average ⬆️",
+    "ARC",
+    "HellaSwag",
+    "MMLU",
+    "TruthfulQA",
+    "Winogrande",
+    "GSM8K",
+    "model_name_for_query",
+]
+TYPES = [
+    "str",
+    "markdown",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "str",
+    "str",
+    "str",
+    "str",
+    "bool",
+    "str",
+    "number",
+    "number",
+    "bool",
+    "str",
+    "bool",
+    "bool",
+    "str",
+]
+NUMERIC_INTERVALS = {
+    "?": pd.Interval(-1, 0, closed="right"),
+    "~1.5": pd.Interval(0, 2, closed="right"),
+    "~3": pd.Interval(2, 4, closed="right"),
+    "~7": pd.Interval(4, 9, closed="right"),
+    "~13": pd.Interval(9, 20, closed="right"),
+    "~35": pd.Interval(20, 45, closed="right"),
+    "~60": pd.Interval(45, 70, closed="right"),
+    "70+": pd.Interval(70, 10000, closed="right"),
+}
+MODEL_TYPE = [str(s) for s in df["T"].unique()]
+Precision = [str(s) for s in df["Precision"].unique()]
+
+# Searching and filtering
+def update_table(
+    hidden_df: pd.DataFrame,
+    columns: list,
+    type_query: list,
+    precision_query: str,
+    size_query: list,
+    query: str,
+):
+    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)  # type: ignore
+    filtered_df = filter_queries(query, filtered_df)
+    df = select_columns(filtered_df, columns)
+    return df
+
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df["model_name_for_query"].str.contains(query, case=False))]  # type: ignore
+
+def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
+    # We use COLS to maintain sorting
+    filtered_df = df[[c for c in COLS if c in df.columns and c in columns]]
+    return filtered_df  # type: ignore
+
+def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
+    final_df = []
+    if query != "":
+        queries = [q.strip() for q in query.split(";")]
+        for _q in queries:
+            _q = _q.strip()
+            if _q != "":
+                temp_filtered_df = search_table(filtered_df, _q)
+                if len(temp_filtered_df) > 0:
+                    final_df.append(temp_filtered_df)
+        if len(final_df) > 0:
+            filtered_df = pd.concat(final_df)
+            filtered_df = filtered_df.drop_duplicates(  # type: ignore
+                subset=["Model", "Precision", "Model sha"]
+            )
+
+    return filtered_df
+
+def filter_models(
+    df: pd.DataFrame,
+    type_query: list,
+    size_query: list,
+    precision_query: list,
+) -> pd.DataFrame:
+    # Show all models
+    filtered_df = df
+
+    type_emoji = [t[0] for t in type_query]
+    filtered_df = filtered_df.loc[df["T"].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df["Precision"].isin(precision_query + ["None"])]
+
+    numeric_interval = pd.IntervalIndex(
+        sorted([NUMERIC_INTERVALS[s] for s in size_query])  # type: ignore
+    )
+    params_column = pd.to_numeric(df["#Params (B)"], errors="coerce")
+    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))  # type: ignore
+    filtered_df = filtered_df.loc[mask]
+
+    return filtered_df
+
+demo = gr.Blocks(css=str(abs_path / "assets/leaderboard_data.json"))
+with demo:
+    gr.Markdown("""Test Space of the LLM Leaderboard""", elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        search_bar = gr.Textbox(
+                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
+                            show_label=False,
+                            elem_id="search-bar",
+                        )
+                    with gr.Row():
+                        shown_columns = gr.CheckboxGroup(
+                            choices=COLS,
+                            value=ON_LOAD_COLS,
+                            label="Select columns to show",
+                            elem_id="column-select",
+                            interactive=True,
+                        )
+                with gr.Column(min_width=320):
+                    filter_columns_type = gr.CheckboxGroup(
+                        label="Model types",
+                        choices=MODEL_TYPE,
+                        value=MODEL_TYPE,
+                        interactive=True,
+                        elem_id="filter-columns-type",
+                    )
+                    filter_columns_precision = gr.CheckboxGroup(
+                        label="Precision",
+                        choices=Precision,
+                        value=Precision,
+                        interactive=True,
+                        elem_id="filter-columns-precision",
+                    )
+                    filter_columns_size = gr.CheckboxGroup(
+                        label="Model sizes (in billions of parameters)",
+                        choices=list(NUMERIC_INTERVALS.keys()),
+                        value=list(NUMERIC_INTERVALS.keys()),
+                        interactive=True,
+                        elem_id="filter-columns-size",
+                    )
+
+            leaderboard_table = gr.components.Dataframe(
+                value=df[ON_LOAD_COLS],  # type: ignore
+                headers=ON_LOAD_COLS,
+                datatype=TYPES,
+                elem_id="leaderboard-table",
+                interactive=False,
+                visible=True,
+                column_widths=["2%", "33%"],
+            )
+
+            # Dummy leaderboard for handling the case when the user uses backspace key
+            hidden_leaderboard_table_for_search = gr.components.Dataframe(
+                value=invisible_df[COLS],  # type: ignore
+                headers=COLS,
+                datatype=TYPES,
+                visible=False,
+            )
+            search_bar.submit(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    search_bar,
+                ],
+                leaderboard_table,
+            )
+            for selector in [
+                shown_columns,
+                filter_columns_type,
+                filter_columns_precision,
+                filter_columns_size,
+            ]:
+                selector.change(
+                    update_table,
+                    [
+                        hidden_leaderboard_table_for_search,
+                        shown_columns,
+                        filter_columns_type,
+                        filter_columns_precision,
+                        filter_columns_size,
+                        search_bar,
+                    ],
+                    leaderboard_table,
+                    queue=True,
+                )
+
+if __name__ == "__main__":
+    demo.queue(default_concurrency_limit=40).launch()
diff --git a/demos/model3D/files/Bunny.obj b/demos/model3D/files/Bunny.obj
new file mode 100644
index 0000000000000000000000000000000000000000..9baeb363cce8feb5dd62ecaf8d64a14b6c50ce37
--- /dev/null
+++ b/demos/model3D/files/Bunny.obj
@@ -0,0 +1,7474 @@
+# OBJ file format with ext .obj
+# vertex count = 2503
+# face count = 4968
+v -3.4101800e-003 1.3031957e-001 2.1754370e-002
+v -8.1719160e-002 1.5250145e-001 2.9656090e-002
+v -3.0543480e-002 1.2477885e-001 1.0983400e-003
+v -2.4901590e-002 1.1211138e-001 3.7560240e-002
+v -1.8405680e-002 1.7843055e-001 -2.4219580e-002
+v 1.9067940e-002 1.2144925e-001 3.1968440e-002
+v 6.0412000e-003 1.2494359e-001 3.2652890e-002
+v -1.3469030e-002 1.6299355e-001 -1.2000020e-002
+v -3.4393240e-002 1.7236688e-001 -9.8213000e-004
+v -8.4314160e-002 1.0957263e-001 3.7097300e-003
+v -4.2233540e-002 1.7211574e-001 -4.1799800e-003
+v -6.3308390e-002 1.5660615e-001 -1.3838790e-002
+v -7.6903950e-002 1.6708033e-001 -2.6931360e-002
+v -7.2253920e-002 1.1539550e-001 5.1670300e-002
+v 1.2981330e-002 1.1366375e-001 3.8302950e-002
+v -3.7857280e-002 1.7010102e-001 1.4236000e-003
+v 4.8689400e-003 3.7962370e-002 4.5867630e-002
+v -5.7180550e-002 4.0918830e-002 4.6301340e-002
+v -4.5209070e-002 3.8839100e-002 4.4503770e-002
+v -3.3761490e-002 1.2617876e-001 1.7132300e-003
+v -5.0242270e-002 1.5773747e-001 9.3944500e-003
+v -2.1216950e-002 1.5887938e-001 -4.6923700e-003
+v -5.6472950e-002 1.5778406e-001 8.1786500e-003
+v -5.2802060e-002 4.1319860e-002 4.6169800e-002
+v -4.9960340e-002 4.3101950e-002 4.4462650e-002
+v -2.9748750e-002 3.6539860e-002 5.2493310e-002
+v -3.5438900e-003 4.2659770e-002 4.7541530e-002
+v 4.9304900e-003 4.1982660e-002 4.5723390e-002
+v -3.9088180e-002 1.6872020e-001 -1.1924680e-002
+v -5.6901000e-002 4.5437000e-002 4.3236960e-002
+v -4.1244880e-002 4.3098890e-002 4.2129560e-002
+v -2.6471980e-002 4.5034530e-002 5.1219460e-002
+v -2.1866970e-002 4.4022930e-002 5.3243800e-002
+v -3.6996250e-002 1.6899301e-001 1.3256300e-003
+v -6.7216590e-002 1.6171340e-001 -1.3733710e-002
+v 4.9760060e-002 7.0235220e-002 2.3732020e-002
+v -4.9186640e-002 4.6411230e-002 4.1170040e-002
+v -4.4590380e-002 4.3797990e-002 4.2685460e-002
+v -4.3686470e-002 4.7154500e-002 4.0286310e-002
+v -2.2491950e-002 4.6513620e-002 5.1885310e-002
+v -6.5174200e-003 4.5036200e-002 4.7502780e-002
+v 3.7699000e-004 4.4935790e-002 4.6519930e-002
+v 3.4023920e-002 1.1353879e-001 2.4595280e-002
+v -2.6467900e-002 1.8104250e-001 -8.0811700e-003
+v -1.7533470e-002 4.7964250e-002 4.8829630e-002
+v -7.0012600e-003 4.6416520e-002 4.7485540e-002
+v 5.9862300e-003 4.6689140e-002 4.9073620e-002
+v 9.1007200e-003 4.8474490e-002 4.9353190e-002
+v -3.5453700e-002 1.1244769e-001 3.5055410e-002
+v -7.5983200e-002 1.3820800e-001 4.9216580e-002
+v 3.4838440e-002 4.3153410e-002 2.8954310e-002
+v -5.2655550e-002 4.8494220e-002 3.8731190e-002
+v -4.7378940e-002 4.8456670e-002 3.9126790e-002
+v -3.8933750e-002 4.6364270e-002 4.0364780e-002
+v -2.6468940e-002 4.7816430e-002 4.9322590e-002
+v -2.2365790e-002 4.8073650e-002 5.0126500e-002
+v -1.3373430e-002 4.7892410e-002 4.7883850e-002
+v -1.2193490e-002 4.9470300e-002 4.9484490e-002
+v -6.3364000e-004 4.7193060e-002 4.9136900e-002
+v 2.0656800e-003 5.0104680e-002 5.2290220e-002
+v -2.2749270e-002 4.9883880e-002 4.6605520e-002
+v -1.8002080e-002 4.9917850e-002 4.6947970e-002
+v -7.8036800e-003 5.0169310e-002 5.0988650e-002
+v -2.6843800e-003 5.1247420e-002 5.3186790e-002
+v -6.3875650e-002 1.6140094e-001 -2.0064210e-002
+v 3.2434000e-002 4.5333970e-002 3.0316760e-002
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diff --git a/demos/model3D/files/Duck.glb b/demos/model3D/files/Duck.glb
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diff --git a/demos/model3D/files/Fox.gltf b/demos/model3D/files/Fox.gltf
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index 0000000000000000000000000000000000000000..ff3115c05d97ac673c511affe1b3fecc64e1c3d3
--- /dev/null
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VnwNaEzEF5NINB3YhFwEPu6kHynZtBBmQNwVpSEkL+CiHBLeUfwVzZFkKOdITBdo7awEQ8C0LDS4TBdo7awEQ8C0LDS4TB3/HMwHJjCUI5ax/BBmQNwVpSEkL+CiHBwKVEGco2AEJmSHpBaOGtwIH7CEJIWB/BAY9SHOtkA0IsVB7BaOGtwIH7CEJIWB/BwKVEGco2AEJmSHpBpN+gwKRoA0LqFzVBpN+gwKRoA0LqFzVB3/HMwHJjCUI5ax/BaOGtwIH7CEJIWB/BBmQNQVpSEkL+CiHB3/HMQHJjCUI5ax/Bdo7aQEQ8C0LDS4TBdo7aQEQ8C0LDS4TBLeUfQVzZFkKOdITBBmQNQVpSEkL+CiHBwKVEGco2AEJmSHpBAY9SHOtkA0IsVB7BpOGtQIH7CEJIWB/BpOGtQIH7CEJIWB/B3/HMQHJjCUI5ax/BpN+gQKRoA0LqFzVBpN+gQKRoA0LqFzVBwKVEGco2AEJmSHpBpOGtQIH7CEJIWB/BV5JdHB+QA0JwBDTBn32GHPbRB0JIKYXBF8JwP1TQB0K/KIXBV5JdHB+QA0JwBDTBF8Jwv1TQB0LPKIXBn32GHPbRB0JIKYXBV5JdHB+QA0JwBDTBG/lswPhuCUKmp4XBF8Jwv1TQB0LPKIXBnZsDQJHbDEJnXLjBG/lsQPhuCUKmp4XBF8JwP1TQB0K/KIXBIkcKwZhhREKe5g5CQobgwIykOEJ/HRBCZ7AEwUOMUUIm4CxCZ7AEwUOMUUIm4CxC6M4fwbrSY0KLzihCIkcKwZhhREKe5g5CQobgwIykOEJ/HRBCfq/nmxl9LkJsKhFC3zoXnFezQUIbdyxC3zoXnFezQUIbdyxCZ7AEwUOMUUIm4CxCQobgwIykOEJ/HRBCIkcKQZhhREKe5g5C6M4fQbrSY0KLzihCZ7AEQUOMUUIm4CxCZ7AEQUOMUUIm4CxCQobgQIykOEJ/HRBCIkcKQZhhREKe5g5CQobgQIykOEJ/HRBCZ7AEQUOMUUIm4CxC3zoXnFezQUIbdyxC3zoXnFezQUIbdyxCfq/nmxl9LkJsKhFCQobgQIykOEJ/HRBCA8qGQL5mL0LypSPCgpDNvb3RK0IkPSPCbtvwHG7GA0I3+C7CQSO/QBggCULpxTPCA8qGQL5mL0LypSPCbtvwHG7GA0I3+C7CpN+gQKRoA0LqFzVBBmQNQVpSEkL+CiHBCVcgQawGFULhiCHBCVcgQawGFULhiCHBgcohQWYTD0KU4C9BpN+gQKRoA0LqFzVB2W29HGkRgkLAcW3Bji2vHFbFgULhoDXBQIO/QFGEc0L2tjLBQIO/QFGEc0L2tjLBwLgfQWAGa0INTMDB2W29HGkRgkLAcW3BlD0VQSPEYkID4RbCnCwrQcgXRUIqLBfCOQrhQIpsQkKVyyPCwLgfQWAGa0INTMDBpYM4QfiVRkLsO8XBnCwrQcgXRUIqLBfCnCwrQcgXRUIqLBfC91XhQJiaK0JFqxbCA8qGQL5mL0LypSPCpN+gQKRoA0LqFzVBgcohQWYTD0KU4C9BY0EVQSna30H1n1ZBABUhQdaickJPj7VB+ietG33PhUL9CrVBwDjnGtMJj0JHlgBCylEzQa9KV0L5V69BABUhQdaickJPj7VBWzb5QE8DhULzzAJCkUImQQKQgEJ7/SJCWzb5QE8DhULzzAJCremVQLI9kELXCyVC6M4fQbrSY0KLzihCkUImQQKQgEJ7/SJCfmYYQTBxhEIHr0NCremVQLI9kELXCyVC0H4Lm8JAk0KcsCRCzBv5m992jkLSvUxCWzb5QE8DhULzzAJCwDjnGtMJj0JHlgBC0H4Lm8JAk0KcsCRCyDggQUlwakLr5ExCfmYYQTBxhEIHr0NC9ierQC7yZkIYwWlCkUImQQKQgEJ7/SJCremVQLI9kELXCyVCxntJQXvQnUIGeEpCfmYYQTBxhEIHr0NCp0yYQJr7i0LecUxCRJYcQL71eEIts2ZCkKFAQHQ8bEIGx3VCT5JenO08dkKeZ2pCOXWanDIUYEKpP4VC9ierQC7yZkIYwWlCRJYcQL71eEIts2ZCkKFAQHQ8bEIGx3VCIkcKQZhhREKe5g5CvPofQQY7TUJK/A1C6M4fQbrSY0KLzihCfmYYQTBxhEIHr0NCkUImQQKQgEJ7/SJCxntJQXvQnUIGeEpCremVQLI9kELXCyVCp0yYQJr7i0LecUxCxntJQXvQnUIGeEpCOQrhQIpsQkKVyyPCA8qGQL5mL0LypSPCQSO/QBggCULpxTPCpYM4QfiVRkLsO8XBLeUfQVzZFkKOdITBFAsVQQbpAULh9pDBQSO/QBggCULpxTPCbtvwHG7GA0I3+C7CeETQQKjGqUEruGPCp0yYQJr7i0LecUxCzBv5m992jkLSvUxCRJYcQL71eEIts2ZCyDggQV4LZ0KGfAFBZXw/HFQdgEKGfAFBPqUIHAt1hELYRIBBuM7nHMhjgULh1czBwLgfQWAGa0INTMDBlD0VQSPEYkID4RbC3KYIHeZEdUJFqxbClD0VQSPEYkID4RbCgpDNvSfxYEIIfyPCnCwrQcgXRUIqLBfCpYM4QfiVRkLsO8XB8405QVxe9EEN9dXBLeUfQVzZFkKOdITBdo7aQEQ8C0LDS4TB+kziQMlVAUJX7ofB91XhQJiaK0JFqxbCnCwrQcgXRUIqLBfC48ERQehC30FdDwnCNFZhQGGEKUL3DRTC91XhQJiaK0JFqxbCuy/fQFOV4EFgHxLCKYACQR5umkHEoBjCuw0MQRZ/l0H7hBbCzQ4KQXzHfEFwOhvCNxWZQN/EfkGfOxnCakGfQCj2jUGs/QfCK8qUQLmvkUGqgg7C+kziQMlVAUJX7ofBG/lsQPhuCUKmp4XBakGfQCj2jUGs/QfCaC3JQMGcnEFPMhrCEQzgQCfon0F9kBzCWHDgQH6DgkFbTyHCKtWNQPtGk0EJXRHCTuiWQGLDlUEQRhXCNxWZQN/EfkGfOxnCFAsVQQbpAULh9pDB+kziQMlVAUJX7ofBCez4QKs8pUGVtPPB8405QVxe9EEN9dXBFAsVQQbpAULh9pDBCez4QKs8pUGVtPPBK8qUQLmvkUGqgg7CKtWNQPtGk0EJXRHCNxWZQN/EfkGfOxnCY0EVQSna30H1n1ZBN/AmQQVU50FnL4BBpJ8QQUrqh0F9FnhBlMVnQNaEzEF5NINBPcePQAVU50GoSFZBBna9QG+sjUHJzGhBnoPYwByeFUKvZttByDggwbI2KkJivtVB+BQXwVbl7EE7Ja5B+BQXwVbl7EE7Ja5BWXXgwBzc7kHmqcBBnoPYwByeFUKvZttBwKVEGco2AEJmSHpBpN+gQKRoA0LqFzVBPcePQAVU50GoSFZBPcePQAVU50GoSFZBY0EVQSna30H1n1ZBBna9QG+sjUHJzGhBlngUHfkDTkIw70HCmmSxQHBoUEI88iXCpeTKQDCkN0LOvUbCQSO/QBggCULpxTPCb/UJQWsRyUFizGzC0gvzQPAxIULKoT3CvHbnwIpsQkKVyyPCHtG3wHBoUEI88iXCPgvPwDCkN0LOvUbCPgvPwDCkN0LOvUbCLjL3wPAxIULKoT3CvHbnwIpsQkKVyyPCJHL5QLltCkIqToHCb/UJQWsRyUFizGzCMubMQL+tjkHNrZHCeETQQKjGqUEruGPC1CgFHZgyoUEDBWLCDyAZHSC+ZEEt1IrCb/UJQWsRyUFizGzCeETQQKjGqUEruGPCo595QC/wckFy54vCZ4i2QHygFUL7dYTCJHL5QLltCkIqToHCKqKYQNb2v0F3KpzCnEUuHSy2GEIRGIjCZ4i2QHygFUL7dYTCKqKYQNb2v0F3KpzC+kziQMlVAUJX7ofBdo7aQEQ8C0LDS4TBG/lsQPhuCUKmp4XB2W29HGkRgkLAcW3BwLgfQWAGa0INTMDBuM7nHMhjgULh1czBmmSxQHBoUEI88iXClD0VQSPEYkID4RbCOQrhQIpsQkKVyyPCgpDNvSfxYEIIfyPClD0VQSPEYkID4RbCmmSxQHBoUEI88iXClD0VQSPEYkID4RbCwLgfQWAGa0INTMDBnCwrQcgXRUIqLBfCABUhQdaickJPj7VBPqUIHAt1hELYRIBB+ietG33PhUL9CrVBOQrhQIpsQkKVyyPCnCwrQcgXRUIqLBfCA8qGQL5mL0LypSPCWzb5QE8DhULzzAJCABUhQdaickJPj7VBwDjnGtMJj0JHlgBCRBorQbqccUJjDwdCylEzQa9KV0L5V69BWzb5QE8DhULzzAJCPcePQAVU50GoSFZBpN+gQKRoA0LqFzVBY0EVQSna30H1n1ZByDggQUlwakLr5ExC6M4fQbrSY0KLzihCfmYYQTBxhEIHr0NCremVQLI9kELXCyVCWzb5QE8DhULzzAJC0H4Lm8JAk0KcsCRCRJYcQL71eEIts2ZCzBv5m992jkLSvUxCT5JenO08dkKeZ2pC9ierQC7yZkIYwWlCfmYYQTBxhEIHr0NCRJYcQL71eEIts2ZCkKFAQHQ8bEIGx3VCRJYcQL71eEIts2ZCT5JenO08dkKeZ2pCY0EVQSna30H1n1ZBgcohQWYTD0KU4C9BN/AmQQVU50FnL4BBWXXgQBzc7kHmqcBBYYPYQByeFUKvZttBIM2hQDmF7UGbCLRBCVcgQawGFULhiCHBuG4vQURmMEL/gibBgcohQWYTD0KU4C9BlngUHfkDTkIw70HCgpDNvSfxYEIIfyPCmmSxQHBoUEI88iXC8405QVxe9EEN9dXBpYM4QfiVRkLsO8XBFAsVQQbpAULh9pDBb/UJQWsRyUFizGzCQSO/QBggCULpxTPCeETQQKjGqUEruGPCp0yYQJr7i0LecUxCremVQLI9kELXCyVCzBv5m992jkLSvUxCHaYgQQ7MbEJxbnVByDggQV4LZ0KGfAFBPqUIHAt1hELYRIBB3KYIHeZEdUJFqxbCuM7nHMhjgULh1czBlD0VQSPEYkID4RbC48ERQehC30FdDwnCnCwrQcgXRUIqLBfC8405QVxe9EEN9dXBQIO/QFGEc0L2tjLBcewfQahqaULh4DDBwLgfQWAGa0INTMDBFAsVQQbpAULh9pDBLeUfQVzZFkKOdITB+kziQMlVAUJX7ofBuy/fQFOV4EFgHxLC91XhQJiaK0JFqxbC48ERQehC30FdDwnCHwGQQCfC3EGMtgjCNFZhQGGEKUL3DRTCuy/fQFOV4EFgHxLCakGfQCj2jUGs/QfCG/lsQPhuCUKmp4XBK8qUQLmvkUGqgg7CICHaP0wA6EFi/X9BwKVEGco2AEJmSHpBPcePQAVU50GoSFZBpJ8QQUrqh0F9FnhBN/AmQQVU50FnL4BB77sTQTD6xkGBpJ9BZ4i2QHygFUL7dYTClngUHfkDTkIw70HCJHL5QLltCkIqToHCeETQQKjGqUEruGPCbtvwHG7GA0I3+C7C1CgFHZgyoUEDBWLCnEUuHSy2GEIRGIjClngUHfkDTkIw70HCZ4i2QHygFUL7dYTCJHL5QLltCkIqToHClngUHfkDTkIw70HCpeTKQDCkN0LOvUbCKqKYQNb2v0F3KpzCJHL5QLltCkIqToHCMubMQL+tjkHNrZHCo595QC/wckFy54vCeETQQKjGqUEruGPCDyAZHSC+ZEEt1IrCMubMQL+tjkHNrZHCb/UJQWsRyUFizGzCo595QC/wckFy54vChzaNwL5mL0LypSPCbtvwHG7GA0I3+C7CgpDNvb3RK0IkPSPCBCO/wBggCULpxTPCbtvwHG7GA0I3+C7ChzaNwL5mL0LypSPCgcohwWYTD0KU4C9BCVcgwawGFULhiCHBBmQNwVpSEkL+CiHBBmQNwVpSEkL+CiHBpN+gwKRoA0LqFzVBgcohwWYTD0KU4C9BwLgfwWAGa0INTMDBQIO/wFGEc0L2tjLBji2vHFbFgULhoDXBji2vHFbFgULhoDXB2W29HGkRgkLAcW3BwLgfwWAGa0INTMDBHBA0wcHmVELiEHRBXWMhwf4KFkJweXBByDggwbI2KkJivtVByDggwbI2KkJivtVBylEzwa9KV0L5V69BHBA0wcHmVELiEHRBlD0VwSPEYkID4RbCvHbnwIpsQkKVyyPCfSwrwcgXRUIqLBfCwLgfwWAGa0INTMDBfSwrwcgXRUIqLBfCpYM4wfiVRkLsO8XBfSwrwcgXRUIqLBfChzaNwL5mL0LypSPC91XhwJiaK0JFqxbCpN+gwKRoA0LqFzVBY0EVwSna30H1n1ZBgcohwWYTD0KU4C9BHhUhwdaickJPj7VBwDjnGtMJj0JHlgBC+ietG33PhUL9CrVBylEzwa9KV0L5V69BWzb5wE8DhULzzAJCHhUhwdaickJPj7VBkUImwQKQgEJ7/SJC6umVwLI9kELXCyVCWzb5wE8DhULzzAJCylEzQa9KV0L5V69BRBorQbqccUJjDwdCvPofQQY7TUJK/A1CvPofQQY7TUJK/A1CyDggQbI2KkJivtVBylEzQa9KV0L5V69B6M4fwbrSY0KLzihCfmYYwTBxhEIHr0NCkUImwQKQgEJ7/SJC6umVwLI9kELXCyVCzBv5m992jkLSvUxC0H4Lm8JAk0KcsCRCWzb5wE8DhULzzAJC0H4Lm8JAk0KcsCRCwDjnGtMJj0JHlgBCyDggwUlwakLr5ExC9ierwC7yZkIYwWlCfmYYwTBxhEIHr0NCkUImwQKQgEJ7/SJCxntJwXvQnUIGeEpC6umVwLI9kELXCyVCfmYYwTBxhEIHr0NCRJYcwL71eEIts2ZCp0yYwJr7i0LecUxCkKFAwHQ8bEIGx3VCOXWanDIUYEKpP4VCT5JenO08dkKeZ2pC9ierwC7yZkIYwWlCkKFAwHQ8bEIGx3VCRJYcwL71eEIts2ZCIkcKwZhhREKe5g5C6M4fwbrSY0KLzihCvPofwQY7TUJK/A1CfmYYwTBxhEIHr0NCxntJwXvQnUIGeEpCkUImwQKQgEJ7/SJC6umVwLI9kELXCyVCxntJwXvQnUIGeEpCp0yYwJr7i0LecUxCvHbnwIpsQkKVyyPCBCO/wBggCULpxTPChzaNwL5mL0LypSPCpYM4wfiVRkLsO8XBFAsVwQbpAULh9pDBLeUfwVzZFkKOdITBBCO/wBggCULpxTPCeETQwKjGqUEruGPCbtvwHG7GA0I3+C7Cp0yYwJr7i0LecUxCRJYcwL71eEIts2ZCzBv5m992jkLSvUxCyDggwV4LZ0KGfAFBPqUIHAt1hELYRIBBZXw/HFQdgEKGfAFBuM7nHMhjgULh1czBlD0VwSPEYkID4RbCwLgfwWAGa0INTMDB3KYIHeZEdUJFqxbCgpDNvSfxYEIIfyPClD0VwSPEYkID4RbCfSwrwcgXRUIqLBfC8405wVxe9EEN9dXBpYM4wfiVRkLsO8XBLeUfwVzZFkKOdITB+kziwMlVAUJX7ofBdo7awEQ8C0LDS4TB91XhwJiaK0JFqxbC48ERwehC30FdDwnCfSwrwcgXRUIqLBfCulVhwGGEKUL3DRTCfi/fwFOV4EFgHxLC91XhwJiaK0JFqxbCCoACwR5umkHEoBjCzQ4KwXzHfEFwOhvCnQ0MwSV/l0H7hBbC+kziwMlVAUJX7ofBLkGfwCj2jUGs/QfCG/lswPhuCUKmp4XBKy3JwMGcnEFPMhrCG3DgwH6DgkFbTyHCEQzgwCfon0F9kBzCKtWNwPtGk0EJXRHCNxWZwN/EfkGfOxnCTuiWwGLDlUEQRhXCFAsVwQbpAULh9pDBCez4wKs8pUGVtPPB+kziwMlVAUJX7ofB8405wVxe9EEN9dXBCez4wKs8pUGVtPPBFAsVwQbpAULh9pDBK8qUwLmvkUGqgg7CNxWZwN/EfkGfOxnCKtWNwPtGk0EJXRHCY0EVwSna30H1n1ZBpJ8QwUrqh0F9FnhBN/AmwQVU50FnL4BBlMVnwNaEzEF5NINBBna9wG+sjUHJzGhBPcePwAVU50GoSFZBwKVEGco2AEJmSHpBPcePwAVU50GoSFZBpN+gwKRoA0LqFzVBPcePwAVU50GoSFZBBna9wG+sjUHJzGhBY0EVwSna30H1n1ZBlngUHfkDTkIw70HCJHL5wLltCkIqToHCPgvPwDCkN0LOvUbCLjL3wPAxIULKoT3Cb/UJwWsRyUFizGzCBCO/wBggCULpxTPC0gvzQPAxIULKoT3Cb/UJQWsRyUFizGzCJHL5QLltCkIqToHCJHL5QLltCkIqToHCpeTKQDCkN0LOvUbC0gvzQPAxIULKoT3CJHL5wLltCkIqToHC9eXMwL+tjkHNrZHCb/UJwWsRyUFizGzCeETQwKjGqUEruGPCDyAZHSC+ZEEt1IrC1CgFHZgyoUEDBWLCb/UJwWsRyUFizGzCo595wC/wckFy54vCeETQwKjGqUEruGPCZ4i2wHygFUL7dYTCKqKYwNb2v0F3KpzCJHL5wLltCkIqToHCnEUuHSy2GEIRGIjCKqKYwNb2v0F3KpzCZ4i2wHygFUL7dYTC+kziwMlVAUJX7ofBG/lswPhuCUKmp4XBdo7awEQ8C0LDS4TB2W29HGkRgkLAcW3BuM7nHMhjgULh1czBwLgfwWAGa0INTMDBHtG3wHBoUEI88iXCvHbnwIpsQkKVyyPClD0VwSPEYkID4RbCgpDNvSfxYEIIfyPCHtG3wHBoUEI88iXClD0VwSPEYkID4RbClD0VwSPEYkID4RbCfSwrwcgXRUIqLBfCwLgfwWAGa0INTMDBHhUhwdaickJPj7VB+ietG33PhUL9CrVBPqUIHAt1hELYRIBBvHbnwIpsQkKVyyPChzaNwL5mL0LypSPCfSwrwcgXRUIqLBfCWzb5wE8DhULzzAJCwDjnGtMJj0JHlgBCHhUhwdaickJPj7VBWzb5wE8DhULzzAJCylEzwa9KV0L5V69BYhorwbqccUJjDwdCPcePwAVU50GoSFZBY0EVwSna30H1n1ZBpN+gwKRoA0LqFzVByDggwUlwakLr5ExCfmYYwTBxhEIHr0NC6M4fwbrSY0KLzihC6umVwLI9kELXCyVC0H4Lm8JAk0KcsCRCWzb5wE8DhULzzAJCRJYcwL71eEIts2ZCT5JenO08dkKeZ2pCzBv5m992jkLSvUxC9ierwC7yZkIYwWlCRJYcwL71eEIts2ZCfmYYwTBxhEIHr0NCkKFAwHQ8bEIGx3VCT5JenO08dkKeZ2pCRJYcwL71eEIts2ZCY0EVwSna30H1n1ZBN/AmwQVU50FnL4BBgcohwWYTD0KU4C9BWXXgwBzc7kHmqcBBIM2hwDmF7UGbCLRBnoPYwByeFUKvZttBCVcgwawGFULhiCHBgcohwWYTD0KU4C9BuG4vwURmMEL/gibBlngUHfkDTkIw70HCHtG3wHBoUEI88iXCgpDNvSfxYEIIfyPC8405wVxe9EEN9dXBFAsVwQbpAULh9pDBpYM4wfiVRkLsO8XBb/UJwWsRyUFizGzCeETQwKjGqUEruGPCBCO/wBggCULpxTPCp0yYwJr7i0LecUxCzBv5m992jkLSvUxC6umVwLI9kELXCyVCPqUIHAt1hELYRIBByDggwV4LZ0KGfAFBHaYgwQ7MbEJxbnVB3KYIHeZEdUJFqxbClD0VwSPEYkID4RbCuM7nHMhjgULh1czB48ERwehC30FdDwnC8405wVxe9EEN9dXBfSwrwcgXRUIqLBfCQIO/wFGEc0L2tjLBwLgfwWAGa0INTMDBcewfwahqaULh4DDBFAsVwQbpAULh9pDB+kziwMlVAUJX7ofBLeUfwVzZFkKOdITBfi/fwFOV4EFgHxLC48ERwehC30FdDwnC91XhwJiaK0JFqxbCHwGQwCfC3EGMtgjCfi/fwFOV4EFgHxLCulVhwGGEKUL3DRTCLkGfwCj2jUGs/QfCNxWZwN/EfkGfOxnCK8qUwLmvkUGqgg7CPcePwAVU50GoSFZBwKVEGco2AEJmSHpBICHav0wA6EFi/X9BpJ8QwUrqh0F9FnhB77sTwTD6xkGBpJ9BN/AmwQVU50FnL4BBZ4i2wHygFUL7dYTCJHL5wLltCkIqToHClngUHfkDTkIw70HCeETQwKjGqUEruGPC1CgFHZgyoUEDBWLCbtvwHG7GA0I3+C7CnEUuHSy2GEIRGIjCZ4i2wHygFUL7dYTClngUHfkDTkIw70HCLjL3wPAxIULKoT3CPgvPwDCkN0LOvUbCJHL5wLltCkIqToHCJHL5wLltCkIqToHCb/UJwWsRyUFizGzCLjL3wPAxIULKoT3CKqKYwNb2v0F3KpzC9eXMwL+tjkHNrZHCJHL5wLltCkIqToHCo595wC/wckFy54vCDyAZHSC+ZEEt1IrCeETQwKjGqUEruGPC9eXMwL+tjkHNrZHCo595wC/wckFy54vCb/UJwWsRyUFizGzCKtWNQPtGk0EJXRHCK8qUQLmvkUGqgg7CG/lsQPhuCUKmp4XBG/lsQPhuCUKmp4XBnZsDQJHbDEJnXLjBKtWNQPtGk0EJXRHCyDggwV4LZ0KGfAFBcewfwahqaULh4DDBnvU2weuVS0JsdCvBnvU2weuVS0JsdCvBhsw0waGIUkI6dQpByDggwV4LZ0KGfAFBgcohQWYTD0KU4C9BuG4vQURmMEL/gibBnvU2QeuVS0JsdCvBnvU2QeuVS0JsdCvBhsw0QaGIUkI6dQpBgcohQWYTD0KU4C9BgcohwWYTD0KU4C9BN/AmwQVU50FnL4BBXWMhwf4KFkJweXBBhsw0waGIUkI6dQpBnvU2weuVS0JsdCvBuG4vwURmMEL/gibBuG4vwURmMEL/gibBgcohwWYTD0KU4C9Bhsw0waGIUkI6dQpByDggQV4LZ0KGfAFBhsw0QaGIUkI6dQpBnvU2QeuVS0JsdCvBnvU2QeuVS0JsdCvBcewfQahqaULh4DDByDggQV4LZ0KGfAFBYYPYQByeFUKvZttBWXXgQBzc7kHmqcBB2hQXQVbl7EE7Ja5B2hQXQVbl7EE7Ja5ByDggQbI2KkJivtVBYYPYQByeFUKvZttBN/AmwQVU50FnL4BB77sTwTD6xkGBpJ9B+BQXwVbl7EE7Ja5BIM2hwDmF7UGbCLRBWXXgwBzc7kHmqcBBJc7jwPts1UFmZrVBJc7jwPts1UFmZrVBBgOQwBR02UGqO6dBIM2hwDmF7UGbCLRBIM2hQDmF7UGbCLRBBgOQQBR02UGqO6dBJc7jQPts1UFmZrVBJc7jQPts1UFmZrVBWXXgQBzc7kHmqcBBIM2hQDmF7UGbCLRBWXXgQBzc7kHmqcBBJc7jQPts1UFmZrVB77sTQTD6xkGBpJ9B77sTQTD6xkGBpJ9B2hQXQVbl7EE7Ja5BWXXgQBzc7kHmqcBBWXXgwBzc7kHmqcBB+BQXwVbl7EE7Ja5B77sTwTD6xkGBpJ9B77sTwTD6xkGBpJ9BJc7jwPts1UFmZrVBWXXgwBzc7kHmqcBBlMVnQNaEzEF5NINBICHaP0wA6EFi/X9BPcePQAVU50GoSFZBlMVnwNaEzEF5NINBPcePwAVU50GoSFZBICHav0wA6EFi/X9BN/AmQQVU50FnL4BB2hQXQVbl7EE7Ja5B77sTQTD6xkGBpJ9B2hQXQVbl7EE7Ja5BN/AmQQVU50FnL4BBXWMhQf4KFkJTeXBBXWMhQf4KFkJTeXBByDggQbI2KkJivtVB2hQXQVbl7EE7Ja5BHBA0QcHmVELiEHRBylEzQa9KV0L5V69ByDggQbI2KkJivtVByDggQbI2KkJivtVBXWMhQf4KFkJTeXBBHBA0QcHmVELiEHRBXWMhQf4KFkJTeXBBgcohQWYTD0KU4C9Bhsw0QaGIUkI6dQpBhsw0QaGIUkI6dQpBHBA0QcHmVELiEHRBXWMhQf4KFkJTeXBBHaYgQQ7MbEJxbnVBHBA0QcHmVELiEHRBhsw0QaGIUkI6dQpBhsw0QaGIUkI6dQpByDggQV4LZ0KGfAFBHaYgQQ7MbEJxbnVBHaYgwQ7MbEJxbnVBHBA0wcHmVELiEHRBylEzwa9KV0L5V69BylEzwa9KV0L5V69BHhUhwdaickJPj7VBHaYgwQ7MbEJxbnVByDggwbI2KkJivtVBXWMhwf4KFkJweXBBN/AmwQVU50FnL4BBN/AmwQVU50FnL4BB+BQXwVbl7EE7Ja5ByDggwbI2KkJivtVBgcohQWYTD0KU4C9BXWMhQf4KFkJTeXBBN/AmQQVU50FnL4BBHaYgwQ7MbEJxbnVByDggwV4LZ0KGfAFBhsw0waGIUkI6dQpBhsw0waGIUkI6dQpBHBA0wcHmVELiEHRBHaYgwQ7MbEJxbnVBHhUhwdaickJPj7VBPqUIHAt1hELYRIBBHaYgwQ7MbEJxbnVBABUhQdaickJPj7VBHaYgQQ7MbEJxbnVBPqUIHAt1hELYRIBBHaYgQQ7MbEJxbnVBABUhQdaickJPj7VBylEzQa9KV0L5V69BylEzQa9KV0L5V69BHBA0QcHmVELiEHRBHaYgQQ7MbEJxbnVBHBA0wcHmVELiEHRBhsw0waGIUkI6dQpBgcohwWYTD0KU4C9BgcohwWYTD0KU4C9BXWMhwf4KFkJweXBBHBA0wcHmVELiEHRBEQzgQCfon0F9kBzCaC3JQMGcnEFPMhrCHwGQQCfC3EGMtgjCHwGQQCfC3EGMtgjCuy/fQFOV4EFgHxLCEQzgQCfon0F9kBzCTuiWQGLDlUEQRhXCKtWNQPtGk0EJXRHCnZsDQJHbDEJnXLjBnZsDQJHbDEJnXLjBHwGQQCfC3EGMtgjCTuiWQGLDlUEQRhXCuw0MQRZ/l0H7hBbCKYACQR5umkHEoBjCuy/fQFOV4EFgHxLCuy/fQFOV4EFgHxLC48ERQehC30FdDwnCuw0MQRZ/l0H7hBbCnQ0MwSV/l0H7hBbC48ERwehC30FdDwnCfi/fwFOV4EFgHxLCfi/fwFOV4EFgHxLCCoACwR5umkHEoBjCnQ0MwSV/l0H7hBbCEQzgwCfon0F9kBzCfi/fwFOV4EFgHxLCHwGQwCfC3EGMtgjCHwGQwCfC3EGMtgjCKy3JwMGcnEFPMhrCEQzgwCfon0F9kBzC48ERwehC30FdDwnCQO4SwafzjEHX+hDC8405wVxe9EEN9dXB48ERQehC30FdDwnC8405QVxe9EEN9dXBQO4SQafzjEHX+hDCKtWNwPtGk0EJXRHCnZsDwJHbDEJnXLjBG/lswPhuCUKmp4XBG/lswPhuCUKmp4XBK8qUwLmvkUGqgg7CKtWNwPtGk0EJXRHCTuiWwGLDlUEQRhXCHwGQwCfC3EGMtgjCnZsDwJHbDEJnXLjBnZsDwJHbDEJnXLjBKtWNwPtGk0EJXRHCTuiWwGLDlUEQRhXCG/lswPhuCUKmp4XBLkGfwCj2jUGs/QfCK8qUwLmvkUGqgg7COQrhQIpsQkKVyyPC0gvzQPAxIULKoT3CpeTKQDCkN0LOvUbCpeTKQDCkN0LOvUbCmmSxQHBoUEI88iXCOQrhQIpsQkKVyyPCHtG3wHBoUEI88iXClngUHfkDTkIw70HCPgvPwDCkN0LOvUbCvHbnwIpsQkKVyyPCLjL3wPAxIULKoT3CBCO/wBggCULpxTPCOQrhQIpsQkKVyyPCQSO/QBggCULpxTPC0gvzQPAxIULKoT3CZXw/HFQdgEKGfAFBji2vHFbFgULhoDXBQIO/wFGEc0L2tjLBZXw/HFQdgEKGfAFBQIO/QFGEc0L2tjLBji2vHFbFgULhoDXBcewfQahqaULh4DDBQIO/QFGEc0L2tjLBZXw/HFQdgEKGfAFBZXw/HFQdgEKGfAFByDggQV4LZ0KGfAFBcewfQahqaULh4DDBcewfQahqaULh4DDBnvU2QeuVS0JsdCvBpYM4QfiVRkLsO8XBpYM4QfiVRkLsO8XBwLgfQWAGa0INTMDBcewfQahqaULh4DDBuG4vQURmMEL/gibBCVcgQawGFULhiCHBLeUfQVzZFkKOdITBLeUfQVzZFkKOdITBpYM4QfiVRkLsO8XBuG4vQURmMEL/gibBpYM4QfiVRkLsO8XBnvU2QeuVS0JsdCvBuG4vQURmMEL/gibBLeUfQVzZFkKOdITBCVcgQawGFULhiCHBBmQNQVpSEkL+CiHBpYM4wfiVRkLsO8XBuG4vwURmMEL/gibBnvU2weuVS0JsdCvBcewfwahqaULh4DDByDggwV4LZ0KGfAFBZXw/HFQdgEKGfAFBZXw/HFQdgEKGfAFBQIO/wFGEc0L2tjLBcewfwahqaULh4DDBcewfwahqaULh4DDBwLgfwWAGa0INTMDBpYM4wfiVRkLsO8XBpYM4wfiVRkLsO8XBnvU2weuVS0JsdCvBcewfwahqaULh4DDBLeUfwVzZFkKOdITBBmQNwVpSEkL+CiHBCVcgwawGFULhiCHBuG4vwURmMEL/gibBpYM4wfiVRkLsO8XBLeUfwVzZFkKOdITBLeUfwVzZFkKOdITBCVcgwawGFULhiCHBuG4vwURmMEL/gibBylEzwa9KV0L5V69ByDggwbI2KkJivtVBvPofwQY7TUJK/A1CvPofwQY7TUJK/A1CYhorwbqccUJjDwdCylEzwa9KV0L5V69BRBorQbqccUJjDwdCkUImQQKQgEJ7/SJC6M4fQbrSY0KLzihC6M4fQbrSY0KLzihCvPofQQY7TUJK/A1CRBorQbqccUJjDwdCvPofQQY7TUJK/A1CIkcKQZhhREKe5g5CYYPYQByeFUKvZttBYYPYQByeFUKvZttByDggQbI2KkJivtVBvPofQQY7TUJK/A1CvPofwQY7TUJK/A1CyDggwbI2KkJivtVBnoPYwByeFUKvZttBnoPYwByeFUKvZttBIkcKwZhhREKe5g5CvPofwQY7TUJK/A1CYhorwbqccUJjDwdCvPofwQY7TUJK/A1C6M4fwbrSY0KLzihC6M4fwbrSY0KLzihCkUImwQKQgEJ7/SJCYhorwbqccUJjDwdCkUImQQKQgEJ7/SJCRBorQbqccUJjDwdCWzb5QE8DhULzzAJCkUImwQKQgEJ7/SJCWzb5wE8DhULzzAJCYhorwbqccUJjDwdCQO4SQafzjEHX+hDCCez4QKs8pUGVtPPB38v0QG42gEGfPQXCakGfQCj2jUGs/QfCNxWZQN/EfkGfOxnC6zORQBO2i0CurgfCQO4SQafzjEHX+hDC38v0QG42gEGfPQXCj08VQaH06UBTygvCA9PdQH0U1j2WjgnCj08VQaH06UBTygvCaN0VQeSSTUBu9APCNxWZQN/EfkGfOxnCWHDgQH6DgkFbTyHCA9PdQH0U1j2WjgnC38v0QG42gEGfPQXCakGfQCj2jUGs/QfCCNikQJKZ0UBZ7QXCzQ4KQXzHfEFwOhvCQO4SQafzjEHX+hDCj08VQaH06UBTygvCWHDgQH6DgkFbTyHCzQ4KQXzHfEFwOhvCj08VQaH06UBTygvCRr2yQFbRcD5EAN7B6zORQBO2i0CurgfCnEGPQFJV+b11p//BRr2yQFbRcD5EAN7BnEGPQFJV+b11p//BvgIVQaK70b3cZ//BA9PdQH0U1j2WjgnCaN0VQeSSTUBu9APCvgIVQaK70b3cZ//BaN0VQeSSTUBu9APCj08VQaH06UBTygvCnSP+QI+KkD5XHt7Bj08VQaH06UBTygvCOHDeQIvDzkATvQHCnSP+QI+KkD5XHt7BOHDeQIvDzkATvQHCCNikQJKZ0UBZ7QXCRr2yQFbRcD5EAN7BBna9QG+sjUHJzGhBpJ8QQUrqh0F9FnhBCQwAQUfp8kCN+nJBBna9QG+sjUHJzGhBC2a1QFXPokA/AIJBJJmNQMw5jEGHu4JB7UUEQVz1hUHskKJBXGzhQI41hkEcWq5Bc8beQFaCuEDkg6ZBby2+QKDVh0GjkaJBVmSSQOLgiEHm/5pBF++UQEooy0DS7Y1BVmSSQOLgiEHm/5pBFtuBQP+Ii0H5vIdBF++UQEooy0DS7Y1BpJ8QQUrqh0F9FnhB77sTQTD6xkGBpJ9BMe8OQePvhkEWTptBxe2lQMULTD8Y8LtBF++UQEooy0DS7Y1BagydQHE5db08dolBagydQHE5db08dolBNzoLQYzvNr3ypolBLL8GQV7zED/PqLpBF++UQEooy0DS7Y1BC2a1QFXPokA/AIJBagydQHE5db08dolBC2a1QFXPokA/AIJBOGoNQYk7pEAgaYdBNzoLQYzvNr3ypolBql8IQZCivUB5YJVBc8beQFaCuEDkg6ZBiQvjQDzQZj65rMlBc8beQFaCuEDkg6ZBF++UQEooy0DS7Y1Bxe2lQMULTD8Y8LtBOGoNQYk7pEAgaYdBql8IQZCivUB5YJVBLL8GQV7zED/PqLpBEQzgQCfon0F9kBzCuy/fQFOV4EFgHxLCKYACQR5umkHEoBjCCez4QKs8pUGVtPPB+kziQMlVAUJX7ofBakGfQCj2jUGs/QfCTuiWQGLDlUEQRhXCHwGQQCfC3EGMtgjCaC3JQMGcnEFPMhrC38v0QG42gEGfPQXCCez4QKs8pUGVtPPBakGfQCj2jUGs/QfCQO4SQafzjEHX+hDC8405QVxe9EEN9dXBCez4QKs8pUGVtPPBCNikQJKZ0UBZ7QXCakGfQCj2jUGs/QfC6zORQBO2i0CurgfCj08VQaH06UBTygvC38v0QG42gEGfPQXCOHDeQIvDzkATvQHC6zORQBO2i0CurgfCNxWZQN/EfkGfOxnCA9PdQH0U1j2WjgnCOHDeQIvDzkATvQHC38v0QG42gEGfPQXCCNikQJKZ0UBZ7QXCA9PdQH0U1j2WjgnCWHDgQH6DgkFbTyHCj08VQaH06UBTygvCnEGPQFJV+b11p//B6zORQBO2i0CurgfCA9PdQH0U1j2WjgnCnSP+QI+KkD5XHt7BRr2yQFbRcD5EAN7BvgIVQaK70b3cZ//BA9PdQH0U1j2WjgnCvgIVQaK70b3cZ//BnEGPQFJV+b11p//BvgIVQaK70b3cZ//BaN0VQeSSTUBu9APCnSP+QI+KkD5XHt7BRr2yQFbRcD5EAN7BCNikQJKZ0UBZ7QXC6zORQBO2i0CurgfCnSP+QI+KkD5XHt7BOHDeQIvDzkATvQHCRr2yQFbRcD5EAN7BBna9QG+sjUHJzGhBY0EVQSna30H1n1ZBpJ8QQUrqh0F9FnhBCQwAQUfp8kCN+nJBpJ8QQUrqh0F9FnhBOGoNQYk7pEAgaYdBFtuBQP+Ii0H5vIdBlMVnQNaEzEF5NINBJJmNQMw5jEGHu4JBMe8OQePvhkEWTptB77sTQTD6xkGBpJ9B7UUEQVz1hUHskKJBXGzhQI41hkEcWq5BJc7jQPts1UFmZrVBby2+QKDVh0GjkaJBC2a1QFXPokA/AIJBBna9QG+sjUHJzGhBCQwAQUfp8kCN+nJBOGoNQYk7pEAgaYdBpJ8QQUrqh0F9FnhBql8IQZCivUB5YJVBNzoLQYzvNr3ypolBOGoNQYk7pEAgaYdBLL8GQV7zED/PqLpBC2a1QFXPokA/AIJBCQwAQUfp8kCN+nJBOGoNQYk7pEAgaYdBxe2lQMULTD8Y8LtBagydQHE5db08dolBiQvjQDzQZj65rMlBiQvjQDzQZj65rMlBagydQHE5db08dolBLL8GQV7zED/PqLpBagydQHE5db08dolBC2a1QFXPokA/AIJBNzoLQYzvNr3ypolBLL8GQV7zED/PqLpBql8IQZCivUB5YJVBiQvjQDzQZj65rMlBiQvjQDzQZj65rMlBc8beQFaCuEDkg6ZBxe2lQMULTD8Y8LtBQO4SwafzjEHX+hDC38v0wG42gEGfPQXCCez4wKs8pUGVtPPBLkGfwCj2jUGs/QfC6zORwBO2i0CurgfCNxWZwN/EfkGfOxnCQO4SwafzjEHX+hDCcU8VwaH06UBTygvC38v0wG42gEGfPQXCA9PdwH0U1j2WjgnCSt0VweSSTUBu9APCcU8VwaH06UBTygvCNxWZwN/EfkGfOxnCA9PdwH0U1j2WjgnCG3DgwH6DgkFbTyHC38v0wG42gEGfPQXCCNikwJKZ0UBZ7QXCLkGfwCj2jUGs/QfCzQ4KwXzHfEFwOhvCcU8VwaH06UBTygvCQO4SwafzjEHX+hDCG3DgwH6DgkFbTyHCcU8VwaH06UBTygvCzQ4KwXzHfEFwOhvCRr2ywFbRcD5EAN7BnEGPwFJV+b11p//B6zORwBO2i0CurgfCRr2ywFbRcD5EAN7BnwIVwaK70b3cZ//BnEGPwFJV+b11p//BA9PdwH0U1j2WjgnCnwIVwaK70b3cZ//BSt0VweSSTUBu9APCSt0VweSSTUBu9APCnSP+wI+KkD5XHt7BcU8VwaH06UBTygvCcU8VwaH06UBTygvCnSP+wI+KkD5XHt7BOHDewIvDzkATvQHCOHDewIvDzkATvQHCRr2ywFbRcD5EAN7BCNikwJKZ0UBZ7QXCBna9wG+sjUHJzGhBJwwAwUfp8kCN+nJBpJ8QwUrqh0F9FnhBJJmNwMw5jEGHu4JBSGa1wFXPokA/AIJBBna9wG+sjUHJzGhB7UUEwVz1hUHskKJBc8bewFaCuEDkg6ZBXGzhwI41hkEcWq5Bby2+wKDVh0GjkaJBVO+UwEooy0DS7Y1BVmSSwOLgiEHm/5pBVmSSwOLgiEHm/5pBVO+UwEooy0DS7Y1BFtuBwA+Ji0H5vIdBpJ8QwUrqh0F9FnhBql8IwZCivUB5YJVBMe8OwePvhkEWTptBxe2lwMULTD8Y8LtBagydwHE5db08dolBVO+UwEooy0DS7Y1BagydwHE5db08dolBLL8GwV7zED/PqLpBNzoLwYzvNr3ypolBVO+UwEooy0DS7Y1BagydwHE5db08dolBSGa1wFXPokA/AIJBSGa1wFXPokA/AIJBNzoLwYzvNr3ypolBOGoNwYk7pEAgaYdBql8IwZCivUB5YJVBiQvjwDzQZj65rMlBc8bewFaCuEDkg6ZBc8bewFaCuEDkg6ZBxe2lwMULTD8Y8LtBVO+UwEooy0DS7Y1BOGoNwYk7pEAgaYdBLL8GwV7zED/PqLpBql8IwZCivUB5YJVBEQzgwCfon0F9kBzCCoACwR5umkHEoBjCfi/fwFOV4EFgHxLCCez4wKs8pUGVtPPBLkGfwCj2jUGs/QfC+kziwMlVAUJX7ofBTuiWwGLDlUEQRhXCKy3JwMGcnEFPMhrCHwGQwCfC3EGMtgjC38v0wG42gEGfPQXCLkGfwCj2jUGs/QfCCez4wKs8pUGVtPPBQO4SwafzjEHX+hDCCez4wKs8pUGVtPPB8405wVxe9EEN9dXBCNikwJKZ0UBZ7QXC6zORwBO2i0CurgfCLkGfwCj2jUGs/QfCcU8VwaH06UBTygvCOHDewIvDzkATvQHC38v0wG42gEGfPQXC6zORwBO2i0CurgfCA9PdwH0U1j2WjgnCNxWZwN/EfkGfOxnCOHDewIvDzkATvQHCCNikwJKZ0UBZ7QXC38v0wG42gEGfPQXCA9PdwH0U1j2WjgnCcU8VwaH06UBTygvCG3DgwH6DgkFbTyHCnEGPwFJV+b11p//BA9PdwH0U1j2WjgnC6zORwBO2i0CurgfCnSP+wI+KkD5XHt7BnwIVwaK70b3cZ//BRr2ywFbRcD5EAN7BA9PdwH0U1j2WjgnCnEGPwFJV+b11p//BnwIVwaK70b3cZ//BnwIVwaK70b3cZ//BnSP+wI+KkD5XHt7BSt0VweSSTUBu9APCRr2ywFbRcD5EAN7B6zORwBO2i0CurgfCCNikwJKZ0UBZ7QXCnSP+wI+KkD5XHt7BRr2ywFbRcD5EAN7BOHDewIvDzkATvQHCBna9wG+sjUHJzGhBpJ8QwUrqh0F9FnhBY0EVwSna30H1n1ZBJwwAwUfp8kCN+nJBOGoNwYk7pEAgaYdBpJ8QwUrqh0F9FnhBFtuBwA+Ji0H5vIdBJJmNwMw5jEGHu4JBlMVnwNaEzEF5NINBMe8OwePvhkEWTptB7UUEwVz1hUHskKJB77sTwTD6xkGBpJ9BXGzhwI41hkEcWq5Bby2+wKDVh0GjkaJBJc7jwPts1UFmZrVBSGa1wFXPokA/AIJBJwwAwUfp8kCN+nJBBna9wG+sjUHJzGhBOGoNwYk7pEAgaYdBql8IwZCivUB5YJVBpJ8QwUrqh0F9FnhBNzoLwYzvNr3ypolBLL8GwV7zED/PqLpBOGoNwYk7pEAgaYdBSGa1wFXPokA/AIJBOGoNwYk7pEAgaYdBJwwAwUfp8kCN+nJBxe2lwMULTD8Y8LtBiQvjwDzQZj65rMlBagydwHE5db08dolBiQvjwDzQZj65rMlBLL8GwV7zED/PqLpBagydwHE5db08dolBagydwHE5db08dolBNzoLwYzvNr3ypolBSGa1wFXPokA/AIJBLL8GwV7zED/PqLpBiQvjwDzQZj65rMlBql8IwZCivUB5YJVBiQvjwDzQZj65rMlBxe2lwMULTD8Y8LtBc8bewFaCuEDkg6ZB7UUEQVz1hUHskKJBc8beQFaCuEDkg6ZBql8IQZCivUB5YJVBql8IQZCivUB5YJVBMe8OQePvhkEWTptB7UUEQVz1hUHskKJBJJmNwMw5jEGHu4JBFtuBwA+Ji0H5vIdBVO+UwEooy0DS7Y1BVO+UwEooy0DS7Y1BSGa1wFXPokA/AIJBJJmNwMw5jEGHu4JBby2+wKDVh0GjkaJBXGzhwI41hkEcWq5Bc8bewFaCuEDkg6ZBc8bewFaCuEDkg6ZBVO+UwEooy0DS7Y1Bby2+wKDVh0GjkaJBXGzhQI41hkEcWq5B7UUEQVz1hUHskKJB77sTQTD6xkGBpJ9B77sTQTD6xkGBpJ9BJc7jQPts1UFmZrVBXGzhQI41hkEcWq5Bby2+QKDVh0GjkaJBF++UQEooy0DS7Y1Bc8beQFaCuEDkg6ZBc8beQFaCuEDkg6ZBXGzhQI41hkEcWq5Bby2+QKDVh0GjkaJBJJmNQMw5jEGHu4JBC2a1QFXPokA/AIJBF++UQEooy0DS7Y1BF++UQEooy0DS7Y1BFtuBQP+Ii0H5vIdBJJmNQMw5jEGHu4JBVmSSwOLgiEHm/5pBBgOQwBR02UGqO6dBJc7jwPts1UFmZrVBJc7jwPts1UFmZrVBby2+wKDVh0GjkaJBVmSSwOLgiEHm/5pBFtuBwA+Ji0H5vIdBlMVnwNaEzEF5NINBBgOQwBR02UGqO6dBBgOQwBR02UGqO6dBVmSSwOLgiEHm/5pBFtuBwA+Ji0H5vIdBVmSSQOLgiEHm/5pBby2+QKDVh0GjkaJBJc7jQPts1UFmZrVBJc7jQPts1UFmZrVBBgOQQBR02UGqO6dBVmSSQOLgiEHm/5pBFtuBQP+Ii0H5vIdBVmSSQOLgiEHm/5pBBgOQQBR02UGqO6dBBgOQQBR02UGqO6dBlMVnQNaEzEF5NINBFtuBQP+Ii0H5vIdBXGzhwI41hkEcWq5BJc7jwPts1UFmZrVB77sTwTD6xkGBpJ9B77sTwTD6xkGBpJ9B7UUEwVz1hUHskKJBXGzhwI41hkEcWq5B7UUEwVz1hUHskKJBMe8OwePvhkEWTptBql8IwZCivUB5YJVBql8IwZCivUB5YJVBc8bewFaCuEDkg6ZB7UUEwVz1hUHskKJBql8IQZCivUB5YJVBpJ8QQUrqh0F9FnhBMe8OQePvhkEWTptBlMVnQNaEzEF5NINBBna9QG+sjUHJzGhBJJmNQMw5jEGHu4JBlMVnwNaEzEF5NINBJJmNwMw5jEGHu4JBBna9wG+sjUHJzGhB77sTwTD6xkGBpJ9BpJ8QwUrqh0F9FnhBMe8OwePvhkEWTptBKy3JwMGcnEFPMhrCTuiWwGLDlUEQRhXCNxWZwN/EfkGfOxnCNxWZwN/EfkGfOxnCG3DgwH6DgkFbTyHCKy3JwMGcnEFPMhrCaC3JQMGcnEFPMhrCWHDgQH6DgkFbTyHCNxWZQN/EfkGfOxnCNxWZQN/EfkGfOxnCTuiWQGLDlUEQRhXCaC3JQMGcnEFPMhrCKYACQR5umkHEoBjCzQ4KQXzHfEFwOhvCWHDgQH6DgkFbTyHCWHDgQH6DgkFbTyHCEQzgQCfon0F9kBzCKYACQR5umkHEoBjCCoACwR5umkHEoBjCEQzgwCfon0F9kBzCG3DgwH6DgkFbTyHCG3DgwH6DgkFbTyHCzQ4KwXzHfEFwOhvCCoACwR5umkHEoBjCnQ0MwSV/l0H7hBbCQO4SwafzjEHX+hDC48ERwehC30FdDwnCzQ4KwXzHfEFwOhvCQO4SwafzjEHX+hDCnQ0MwSV/l0H7hBbCuw0MQRZ/l0H7hBbC48ERQehC30FdDwnCQO4SQafzjEHX+hDCzQ4KQXzHfEFwOhvCuw0MQRZ/l0H7hBbCQO4SQafzjEHX+hDC1rvFP6UrXEKYO4VCOXWanDIUYEKpP4VC9w6fnMrhVkLuP4VCyrzFv6UrXEKYO4VC9w6fnMrhVkLuP4VCOXWanDIUYEKpP4VCq1kHP5W1LT9zoAs/bxAtP9ArHj8g1Tg/A33CPUwYNT8QA909lSk+P1DHoz3g9T0/xAihPTdPRT8K1ng9jzlHPzIg+zyKA0Q/b39uPZASPz/ECKE9N09FP6/rFz3ZQEI/EAPdPZUpPj+NX/g9qDlFP8QIoT03T0U/Q3IiPqbtOz/CiSg+bapCP41f+D2oOUU/9UppPsrAMT7dfGM+yZM0PrOXXT5n1Sc+X3kQPhq/cD9Zayg+x4FrP7ixOT6BIXs/YodxPeONOD9Qx6M94PU9P29/bj2QEj8/YodxPeONOD9vf249kBI/P8uf7zyLb0A/bZEUPvJcLz9DciI+pu07PwN9wj1MGDU/+S4VP2H7PT8d4xY/Z+45P9ArHj8g1Tg/HqX6PqFILz9KJew+Gw4rPz+n8D5CziM/P6fwPkLOIz8jMvQ+0GMkPx6l+j6hSC8/q1kHP5W1LT9aZwA/A3coP5XwAD9YVSc/lfAAP1hVJz9zoAs/bxAtP6tZBz+VtS0/EcPOPl4sbD7tDqk+J756Pus5uT7SwjU+tJJWPhdHNT76YUQ+oWY4PrCpQz66TiM+XRZjP7STLT+asWQ/c4QoP3XodD8cQDc/7MJjP28pMz9dFmM/tJMtP3XodD8cQDc/KXtTP0s+Sj9l42U/19k8P/1NdD8Xgjw/ZeNlP9fZPD/swmM/bykzP3XodD8cQDc/53LrPlpILD/Nlek+AHQwP1GHtT7TUCM/HeMWP2fuOT+rWQc/lbUtP9ArHj8g1Tg/WtluPnx+ID/mBoM+tYggP5BNgj65cCg/A33CPUwYNT9DciI+pu07PxAD3T2VKT4/YodxPeONOD8DfcI9TBg1P1DHoz3g9T0/r+sXPdlAQj/ECKE9N09FPzIg+zyKA0Q/b39uPZASPz9Qx6M94PU9P8QIoT03T0U/y5/vPItvQD9vf249kBI/P6/rFz3ZQEI/UMejPeD1PT8QA909lSk+P8QIoT03T0U/8ztNPs8yMz+QTYI+uXAoP8aGTj7T+jc/EAPdPZUpPj9DciI+pu07P41f+D2oOUU/HqX6PqFILz8jMvQ+0GMkP74U/j7u0Ss/WtluPnx+ID/nxJY+cJoWP+YGgz61iCA/a/AaPysTPj/5LhU/Yfs9P9ArHj8g1Tg/53LrPlpILD9KJew+Gw4rPx6l+j6hSC8/vhT+Pu7RKz8jMvQ+0GMkP1pnAD8Ddyg/3XxjPsmTND6P/HE+rkpSPrSSVj4XRzU+UaN0P86KQD8pe1M/Sz5KP/1NdD8Xgjw/deh0PxxANz+asWQ/c4QoPzOodj/7rzM//U10PxeCPD9l42U/19k8P3XodD8cQDc/CvV4PxTtNj/8N3c/DYk/P/1NdD8Xgjw/deh0PxxANz8K9Xg/FO02P/1NdD8Xgjw/uHMBPuEJtT47jwo90jnHPn9O4T3y7LI+kE2CPrlwKD/zO00+zzIzP5vIPD70Tik/m8g8PvROKT9a2W4+fH4gP5BNgj65cCg/cjEOP+z41z7KNRE/Ck3qPpATCj/5aOk+N+MQPyL7+D4mOB0/Yp/4PkZEFT9Bnv0+QKUWP7KE7T57vRs/1NL0PjfjED8i+/g+kBMKP/lo6T434xA/Ivv4PngJBj9i1/Y+OLr6PpCe4j54CQY/Ytf2Phx79j4fv+8+pfNhPzav+j6Px2Q/BoH1PsptYz/f+/s+9FDbPWuCbD9tWZ49VP94P9kFoz1HzGg/0AsXP8eE4D5ApRY/soTtPso1ET8KTeo+0AsXP8eE4D5o5h0//5HxPkClFj+yhO0+H/MBP7hAyj5yMQ4/7PjXPji6+j6QnuI+aXSHPeLo0j47jwo90jnHPj1Jej06dMo+csE5Pp32pD6fBUE+GCGkPmZORz6p+bI+Zk5HPqn5sj5V3Cg+Hw66PnLBOT6d9qQ+f07hPfLssj74bB0+coupPsAiHz775as+wCIfPvvlqz64cwE+4Qm1Pn9O4T3y7LI+HcxiP5HUcj41RWw/V7E4PnXndT97aXo+calmPzcW/D7ONms/Er/yPnE8az93TP0+p61BP/rtWz+Txy8/DWxlP6weQD883FY/3PNAPwaBYT+Txy8/DWxlP6etQT/67Vs/hv9QP5C/eD9pVDA/CK5qP1a7Pj/xKms/Vrs+P/Eqaz+Txy8/DWxlP9zzQD8GgWE/vD1IPup7tT7jb5s+uJOoPgFqSj4S9r0+PUl6PTp0yj47jwo90jnHPrhzAT7hCbU+ELHZPtSYqD6Xb80+f2u3PiAlzj6UoKc+cjEOP+z41z6QEwo/+WjpPji6+j6QnuI+0AsXP8eE4D7KNRE/Ck3qPnIxDj/s+Nc+e70bP9TS9D4mOB0/Yp/4PjfjED8i+/g+QKUWP7KE7T434xA/Ivv4Pso1ET8KTeo+aOYdP/+R8T57vRs/1NL0PkClFj+yhO0+yjURPwpN6j434xA/Ivv4PpATCj/5aOk+9wbnPlRSzz44heU+DcbYPpdvzT5/a7c+kBMKP/lo6T54CQY/Ytf2Pji6+j6QnuI+VdwoPh8Ouj79TiM+v9WyPnLBOT6d9qQ+ELHZPtSYqD4gJc4+lKCnPg5OvD4JF5I+XVI1PR5U0j47jwo90jnHPml0hz3i6NI+vD1IPup7tT5V3Cg+Hw66PmZORz6p+bI+/U4jPr/Vsj64cwE+4Qm1PsAiHz775as+ym1jP9/7+z5xqWY/Nxb8PmUXYD/OjQU/ZvQvP4C1bj9pVDA/CK5qP4b/UD+Qv3g/k8cvPw1sZT8SES4/c9hhP6weQD883FY/aVQwPwiuaj+Txy8/DWxlP1a7Pj/xKms/9bwrP7sOZT9pVDA/CK5qP3hjLT+9rG0/k8cvPw1sZT9pVDA/CK5qP/W8Kz+7DmU/l2/NPn9rtz4Qsdk+1JioPs7+8D5WZLw+zv7wPlZkvD73Buc+VFLPPpdvzT5/a7c+3XxjPsmTND71Smk+ysAxPhHjhT4Pf00+EeOFPg9/TT6P/HE+rkpSPt18Yz7JkzQ+tJJWPhdHNT6wqUM+uk4jPrOXXT5n1Sc+s5ddPmfVJz7dfGM+yZM0PrSSVj4XRzU+ym1jP9/7+z5lF2A/zo0FP3SaWT9WmgQ/dJpZP1aaBD+l82E/Nq/6PsptYz/f+/s++mFEPqFmOD60klY+F0c1Po/8cT6uSlI+j/xxPq5KUj5gdUQ+RgZJPvphRD6hZjg+cTxrP3dM/T5bYGs/js0CP2UXYD/OjQU/ZRdgP86NBT9xqWY/Nxb8PnE8az93TP0+calmPzcW/D7KbWM/3/v7Po/HZD8GgfU+j8dkPwaB9T7ONms/Er/yPnGpZj83Fvw+AWpKPhL2vT68Ayw+wCHEPlXcKD4fDro+VdwoPh8Ouj68PUg+6nu1PgFqSj4S9r0+td2kPvMcmT5mTkc+qfmyPp8FQT4YIaQ+Zk5HPqn5sj613aQ+8xyZPuNvmz64k6g+42+bPriTqD68PUg+6nu1PmZORz6p+bI+zZXpPgB0MD/ncus+WkgsPx6l+j6hSC8/HqX6PqFILz+zJPg+BDk0P82V6T4AdDA/m42tPrItGz8/p/A+Qs4jP0ol7D4bDis/SiXsPhsOKz/ncus+WkgsP1GHtT7TUCM/UYe1PtNQIz+bja0+si0bP0ol7D4bDis/IzL0PtBjJD+V8AA/WFUnP1pnAD8Ddyg/csE5Pp32pD7AIh8+++WrPvhsHT5yi6k+csE5Pp32pD79TiM+v9WyPsAiHz775as+q1kHP5W1LT++FP4+7tErP1pnAD8Ddyg/OIXlPg3G2D73Buc+VFLPPji6+j6QnuI+OLr6PpCe4j4ce/Y+H7/vPjiF5T4Nxtg+9wbnPlRSzz7O/vA+VmS8Ph/zAT+4QMo+H/MBP7hAyj44uvo+kJ7iPvcG5z5UUs8+xoZOPtP6Nz/CiSg+bapCP0NyIj6m7Ts/Q3IiPqbtOz/zO00+zzIzP8aGTj7T+jc/8ztNPs8yMz9DciI+pu07P22RFD7yXC8/bZEUPvJcLz+byDw+9E4pP/M7TT7PMjM/W+kxP6VJNT9r8y8/cM8vPyLhOz9X6yg/bD4+P6qeMD9b6TE/pUk1PyLhOz9X6yg/UYe1PtNQIz/Nlek+AHQwP/3c6D7kLjI//dzoPuQuMj+jyLI+6DArP1GHtT7TUCM/aAXuPsZrXj+QMOQ+NgVeP4bJ5D4TC1Q/hsnkPhMLVD+3QwM/18BOP2gF7j7Ga14/QdMSP1A6TT/dJxM/LqpFP2XgGD+TAEU/t0MDP9fATj/wxAQ/iJ9DP90nEz8uqkU/3ScTPy6qRT/5LhU/Yfs9P2vwGj8rEz4/bFsEPh07aD44Ed09zyx5PiTV1z00ElE+btuHPnJTSz96w4U+JnBbPz1EUz6Dpl0/FciMPt/5QT9u24c+clNLP+ymVD6jj1E/UpotPvgYTD/splQ+o49RP2pQJD4+W1c/wokoPm2qQj9Smi0++BhMP2GkBz50lk0/alAkPj5bVz8tYCI+ZaldP2tF2z1ma1k/7KZUPqOPUT89RFM+g6ZdPy1gIj5lqV0/jV/4Pag5RT9hpAc+dJZNP8QIoT03T0U/7s5KPjunZT9cymk+ysNuP7ixOT6BIXs/YaQHPnSWTT+CdPE9j6VTP2KFmz2dSEw/CtZ4PY85Rz/rp3892nNNPzIg+zyKA0Q/xAihPTdPRT9ihZs9nUhMPwrWeD2POUc/xoZOPtP6Nz9B9U8+/aM7P8KJKD5tqkI/WWsoPseBaz/uzko+O6dlP7ixOT6BIXs/XMppPsrDbj+gNHQ+deZ6P7ixOT6BIXs/5SkvP3QMPD9b6TE/pUk1P2w+Pj+qnjA/YLALP5eqjD7VtPs+rRVtPoj0+z5lHFM+bD4+P6qeMD8i4Ts/V+soPyofUj9xWiw/gnTxPY+lUz9rRds9ZmtZP2KFmz2dSEw/n1axPhuDSj+yf64+wvtaP4nwlz47qVs/P28GP3IaXj+3QwM/18BOP0HTEj9QOk0/HOsWP2yVWD9B0xI/UDpNP4HOHD/8x1I/ZycbP9qpgT5gsAs/l6qMPkZCBz8glj0+1bT7Pq0VbT7ZsvQ+ibRdPmjq9T7mslE+ZycbP0p/Xz5nJxs/2qmBPtnqDj9GsyI+7Q6pPie+ej6+a6A+Z5l1PrOYsD719zI+NC8PPzgx5D0wEg4/TFDjPcHlDT8JMsI9Y0a4PvJ37z2+2sE+R3YFPjNUvT60qgU+GyzkPvtzYT6ME98+3lhwPr7awT5HdgU+K4azPjMyCD6sGbE+qikJPoMTsT6Qae09eGG7PlneBT44vLg+2PMFPmNGuD7yd+89iPT7PmUcUz5o6vU+5rJRPm2rBT99kwY+RkIHPyCWPT6I9Ps+ZRxTPm2rBT99kwY+M1S9PrSqBT54Ybs+Wd4FPmNGuD7yd+89JNXXPTQSUT474Lo9541TPjSc0j11eBg+sKlDPrpOIz4S9TI+SMEzPm2sND6eX/Q9JJdPP9EeDz8zpkw/at4VP3bhRz/+0wU/duFHP/7TBT/L9Es/8N4FPySXTz/RHg8/YHVEPkYGST7Q0iU+HO5DPhL1Mj5IwTM+bFsEPhL1Uj4k1dc9NBJRPsSV8z1DcBw+S+o0Pw/UST+E9Cw/o69AP4KOOj+hLEA/bD4+P6qeMD890VE/GRsyPwFoPD9OnDg/3Xp1P5Tbaj9uoXc/OINvPwoPaj964W4/Cg9qP3rhbj9mSWg/bkxnP916dT+U22o/mS1RPz9zPj890VE/GRsyP+zCYz9vKTM/Kh9SP3FaLD9CmVI/DHkkP5qxZD9zhCg/PdFRPxkbMj8qH1I/cVosP10WYz+0ky0/TDdRP2UBQz+ZLVE/P3M+P2XjZT/X2Tw/KXtTP0s+Sj9MN1E/ZQFDP2XjZT/X2Tw/GyzkPvtzYT5bDOY+nWdsPowT3z7eWHA+aAXuPsZrXj+3QwM/18BOPz9vBj9yGl4/i94ZP4/hST9B0xI/UDpNP2XgGD+TAEU/gc4cP/zHUj9B0xI/UDpNP4veGT+P4Uk/QdMSP1A6TT+3QwM/18BOP90nEz8uqkU/btuHPnJTSz+J8Jc+O6lbP3rDhT4mcFs/ZeAYP5MART/dJxM/LqpFP2vwGj8rEz4/7KZUPqOPUT9u24c+clNLPz1EUz6Dpl0/yatTPo1fSD8VyIw+3/lBP+ymVD6jj1E/bFsEPhL1Uj5sWwQ+HTtoPiTV1z00ElE+jV/4Pag5RT/CiSg+bapCP2GkBz50lk0/alAkPj5bVz/splQ+o49RPy1gIj5lqV0/YoWbPZ1ITD9rRds9ZmtZP+unfz3ac00/xAihPTdPRT9hpAc+dJZNP2KFmz2dSEw/CtZ4PY85Rz9ihZs9nUhMP+unfz3ac00/JNXXPTQSUT44Ed09zyx5Pjvguj3njVM+5NZ0PsUaLj4R44U+D39NPvVKaT7KwDE+/dzoPuQuMj/D1OY+Vwg7P6PIsj7oMCs/S+o0Pw/UST+4ySg/Uz1JP4T0LD+jr0A/RkIHPyCWPT5gsAs/l6qMPoj0+z5lHFM+PdFRPxkbMj9sPj4/qp4wPyofUj9xWiw/gnTxPY+lUz9qUCQ+PltXP2tF2z1ma1k/eJqcPt7lSj+fVrE+G4NKP4nwlz47qVs/HOsWP2yVWD8/bwY/chpeP0HTEj9QOk0/2eoOP0azIj5nJxs/2qmBPkZCBz8glj0+hsnkPhMLVD9FY+U+ZK1NP7dDAz/XwE4/iPT7PmUcUz7VtPs+rRVtPmjq9T7mslE+KqoSPzl9HT5nJxs/Sn9fPtnqDj9GsyI+6zm5PtLCNT7tDqk+J756PrOYsD719zI+vtrBPkd2BT6ME98+3lhwPjNUvT60qgU++mFEPqFmOD5gdUQ+RgZJPhL1Mj5IwTM+NJzSPXV4GD474Lo9541TPjUomj3z5zs+TDdRP2UBQz9L6jQ/D9RJP5ktUT8/cz4/Kh9SP3FaLD8i4Ts/V+soP0KZUj8MeSQ/KXtTP0s+Sj9L6jQ/D9RJP0w3UT9lAUM/mS1RPz9zPj9L6jQ/D9RJP4KOOj+hLEA/ZeNlP9fZPD+ZLVE/P3M+P+zCYz9vKTM/XRZjP7STLT8qH1I/cVosP5qxZD9zhCg/7MJjP28pMz890VE/GRsyP10WYz+0ky0/pbxyP4IcZD8M6Wg/VIxXP2ywdD92pF4/7IVmPxQ+Xz8M6Wg/VIxXP6W8cj+CHGQ/qrmcPlKAuD6GN0s+OnnBPgFqSj4S9r0+AWpKPhL2vT7jb5s+uJOoPqq5nD5SgLg+VrgFPhXJ9z6wHkc+WacCP2HGRD6Vmww/YcZEPpWbDD8KFDE+NpIMP1a4BT4Vyfc+BMmrPgO16D5xWqg+eJq8PhYYyj7Thcg+FhjKPtOFyD44Zb4+qkTpPgTJqz4Dteg+y9WPPdcS8j6hR0w9O4ngPpc4kj2gxeI+VrgFPhXJ9z6XOJI9oMXiPtOhAz7YReE+lziSPaDF4j5dUjU9HlTSPml0hz3i6NI+uB02P4cYBz95dj0/rfwCP7NcOj8ziww/yJrBPkhQ/D6OW9w+ar8RP1zHwD4TRw4/OGW+PqpE6T4G2t0+MbEFP8iawT5IUPw+B0LyPsQjAT/x1/Q+LowMPwba3T4xsQU/FciMPt/5QT/Jq1M+jV9IP0H1Tz79ozs/QfVPPv2jOz/0FYQ+lx4xPxXIjD7f+UE/HHv2Ph+/7z7rbgI/onoDPwdC8j7EIwE/8df0Pi6MDD+/1Ac/iNUPPx2r9D7C3BI/BtrdPjGxBT8dq/Q+wtwSP45b3D5qvxE/eAkGP2LX9j434xA/Ivv4PutuAj+iegM/g4oqPfgXZT9tWZ49VP94PxR4pzw8wG8/624CP6J6Az9e8RA/QX0DPzmYBT+I1wk/RkQVP0Ge/T4mOB0/Yp/4PsNHFD/g9gQ/N+MQPyL7+D5GRBU/QZ79Pl7xED9BfQM/OIXlPg3G2D4ce/Y+H7/vPo0l5D7G/d8+2QWjPUfMaD9tWZ49VP94P4OKKj34F2U/FHinPDzAbz9tWZ49VP94PwFRsDzDKHw/3Xp1P5Tbaj/shWY/FD5fP6W8cj+CHGQ/9DE/P/d0jT7/50w/JbFUPtkHTT+wqm4+7IVmPxQ+Xz/XpVI/VdpaPwzpaD9UjFc/OZgFP4jXCT9e8RA/QX0DP7/UBz+I1Q8//nuYPk3z9j4eqa4+660NPxVYmD4//Qs/OrHnPRzrCj/L1Y891xLyPla4BT4Vyfc+D/FPPQETBD9/ifg8PV/7PsvVjz3XEvI+7bovP1x0gj4OoEM/4Co/PvQxPz/3dI0+2QdNP7Cqbj4g7U8/pkdTPteIUD9JSV8+7bovP00UYT5q9zs/SUgkPu26Lz9cdII+ded1P3tpej62gHA/TFM0Pi8Wej8ttHM+ELM7Pz5b5z2D/Dw/ilvFPRTQPD9TeuY9JuRXP98WbD6L3WY/fy4KPjvFWj/n+3k+FxBuP9RFCj5I4W4/4c/wPctKbz82ygo+ERlqP7JlCT7OUWs/jIH1PbZqaz/v/gg+/+dMPyWxVD7WNkU/PSgIPiDtTz+mR1M+DqBDP+AqPz7WNkU/PSgIPv/nTD8lsVQ+gh9pPxGOCT7OUWs/jIH1PREZaj+yZQk+eXY9P638Aj+jk0E/Wd3qPoXRQD+vegQ/zjZrPxK/8j6KkG4/4undPmmLbz+6pPo+W2BrP47NAj9pi28/uqT6Pgn7cj+bPAE/rWw3P9TwAT9tVj0/WcLqPnl2PT+t/AI/845vP5C/eD8icVM/AftsPwoPaj964W4/ZkloP25MZz8U6VI/pptgP+yFZj8UPl8/AWg8P06cOD890VE/GRsyP5ktUT8/cz4/mS1RPz9zPj+Cjjo/oSxAPwFoPD9OnDg/InFTPwH7bD/c80A/BoFhPxTpUj+mm2A/16VSP1XaWj+sHkA/PNxWP206Uj/P+FI/FOlSP6abYD+nrUE/+u1bP9elUj9V2lo/aFlTP/CJcT9Wuz4/8SprPyJxUz8B+2w/hv9QP5C/eD9Wuz4/8SprP2hZUz/wiXE/JuRXP98WbD47xVo/5/t5Pm41Vz8MV3c+ChQxPjaSDD86sec9HOsKP1a4BT4Vyfc+TRM2PQ0a6j6hR0w9O4ngPsvVjz3XEvI+f4n4PD1f+z5NEzY9DRrqPsvVjz3XEvI+y9WPPdcS8j6XOJI9oMXiPla4BT4Vyfc+yJrBPkhQ/D5cx8A+E0cOPx6prj7rrQ0/oUdMPTuJ4D5dUjU9HlTSPpc4kj2gxeI+BtrdPjGxBT+OW9w+ar8RP8iawT5IUPw+BtrdPjGxBT84Zb4+qkTpPr3/3z7IJvk+rWw3P9TwAT95dj0/rfwCP7gdNj+HGAc/eAkGP2LX9j7rbgI/onoDPxx79j4fv+8+8df0Pi6MDD8dq/Q+wtwSPwba3T4xsQU/XvEQP0F9Az/DRxQ/4PYEP7/UBz+I1Q8/N+MQPyL7+D5e8RA/QX0DP+tuAj+iegM/RkQVP0Ge/T7DRxQ/4PYEP17xED9BfQM/eXY9P638Aj+F0UA/r3oEP7NcOj8ziww/RghfPxYV+T6l82E/Nq/6PnSaWT9WmgQ/hjdLPjp5wT6quZw+UoC4PtUFTD7gSdM+845vP5C/eD9uoXc/OINvPz+oez9hHHg/DqBDP+AqPz7/50w/JbFUPvQxPz/3dI0+FOlSP6abYD/XpVI/VdpaP+yFZj8UPl8/OZgFP4jXCT+/1Ac/iNUPP/HX9D4ujAw/HqmuPuutDT/+e5g+TfP2PlILrT7Zlvk+D/FPPQETBD/L1Y891xLyPjqx5z0c6wo/avc7P0lIJD4OoEM/4Co/Pu26Lz9cdII+sB5HPlmnAj9WuAU+Fcn3PicxSD4yk/g+/+dMPyWxVD4g7U8/pkdTPtkHTT+wqm4+GTg4P/kRHz5q9zs/SUgkPu26Lz9NFGE+NUVsP1exOD62gHA/TFM0PnXndT97aXo+i91mP38uCj7OUWs/jIH1PYIfaT8Rjgk+aYtvP7qk+j5bYGs/js0CP3E8az93TP0+o5NBP1nd6j64PEY/kIL/PoXRQD+vegQ/aFlTP/CJcT8icVM/AftsP/OObz+Qv3g/16VSP1XaWj9tOlI/z/hSPwzpaD9UjFc/hv9QP5C/eD9oWVM/8IlxP/OObz+Qv3g/ZkloP25MZz8KD2o/euFuPyJxUz8B+2w/InFTPwH7bD8U6VI/pptgP2ZJaD9uTGc/Vrs+P/Eqaz/c80A/BoFhPyJxUz8B+2w/p61BP/rtWz+sHkA/PNxWP9elUj9V2lo/3PNAPwaBYT+nrUE/+u1bPxTpUj+mm2A/eGG7PlneBT4zVL0+tKoFPowT3z7eWHA+jBPfPt5YcD4Rw84+XixsPnhhuz5Z3gU+/nuYPk3z9j4nMUg+MpP4PnakSj4FxOQ+dqRKPgXE5D4TY5k+xyvoPv57mD5N8/Y+o8iyPugwKz/D1OY+Vwg7P07w5T4Nw0M/TvDlPg3DQz+PxrE+NxtDP6PIsj7oMCs/s1w6PzOLDD+F0UA/r3oEP1gfPz8O2w4/E2OZPscr6D52pEo+BcTkPtUFTD7gSdM+1QVMPuBJ0z6quZw+UoC4PhNjmT7HK+g+n1axPhuDSj+PxrE+NxtDP07w5T4Nw0M/TvDlPg3DQz9FY+U+ZK1NP59WsT4bg0o/vY3NPEpGbj4RjEM9af1NPkt1gT2A1VE+S3WBPYDVUT6Yp/M8k6qFPr2NzTxKRm4+hdFAP696BD+4PEY/kIL/PnbhRz/+0wU/pfNhPzav+j5GCF8/FhX5PsAgYT/oFvI+wCBhP+gW8j6Px2Q/BoH1PqXzYT82r/o+9UppPsrAMT6zl10+Z9UnPnTtaz4VcyA+dO1rPhVzID7k1nQ+xRouPvVKaT7KwDE+EYxDPWn9TT6mtWk9SUxAPjUomj3z5zs+NSiaPfPnOz5LdYE9gNVRPhGMQz1p/U0+y/RLP/DeBT924Uc//tMFP7g8Rj+Qgv8+uDxGP5CC/z58fEo/YvcBP8v0Sz/w3gU/sKlDPrpOIz76YUQ+oWY4PhL1Mj5IwTM+zjZrPxK/8j5pi28/uqT6PnE8az93TP0+O+C6PeeNUz5LdYE9gNVRPjUomj3z5zs+S3WBPYDVUT474Lo9541TPgKbsz0MdH0+ApuzPQx0fT6Yp/M8k6qFPkt1gT2A1VE+G2OfPh6KQj8VyIw+3/lBP/QVhD6XHjE/9BWEPpceMT/V0KY+JLUsPxtjnz4eikI/1dCmPiS1LD+jyLI+6DArP4/GsT43G0M/j8axPjcbQz8bY58+HopCP9XQpj4ktSw/eJqcPt7lSj8bY58+HopCP4/GsT43G0M/j8axPjcbQz+fVrE+G4NKP3ianD7e5Uo/UgutPtmW+T4Eyas+A7XoPjhlvj6qROk+OGW+PqpE6T7ImsE+SFD8PlILrT7Zlvk+M6ZMP2reFT9YHz8/DtsOP4XRQD+vegQ/hdFAP696BD924Uc//tMFPzOmTD9q3hU/OBHdPc8seT4Cm7M9DHR9Pjvguj3njVM+UgutPtmW+T7+e5g+TfP2PhNjmT7HK+g+E2OZPscr6D4Eyas+A7XoPlILrT7Zlvk+yJrBPkhQ/D4eqa4+660NP1ILrT7Zlvk+btuHPnJTSz94mpw+3uVKP4nwlz47qVs/eJqcPt7lSj9u24c+clNLPxXIjD7f+UE/FciMPt/5QT8bY58+HopCP3ianD7e5Uo/BMmrPgO16D4TY5k+xyvoPqq5nD5SgLg+qrmcPlKAuD5xWqg+eJq8PgTJqz4Dteg+rBmxPqopCT4rhrM+MzIIPus5uT7SwjU+6zm5PtLCNT6zmLA+9fcyPqwZsT6qKQk+OLy4PtjzBT54Ybs+Wd4FPhHDzj5eLGw+EcPOPl4sbD7rObk+0sI1Pji8uD7Y8wU+MBIOP0xQ4z00Lw8/ODHkPSqqEj85fR0+KqoSPzl9HT7Z6g4/RrMiPjASDj9MUOM9FNA8P1N65j1q9zs/SUgkPhk4OD/5ER8+GTg4P/kRHz4Qszs/PlvnPRTQPD9TeuY9y0pvPzbKCj62gHA/TFM0PjVFbD9XsTg+NUVsP1exOD4XEG4/1EUKPstKbz82ygo+avc7P0lIJD7XMD8/Jc3fPQ6gQz/gKj8+2eoOP0azIj5GQgc/IJY9PmyxCz+lo9w9ERlqP7JlCT4dzGI/kdRyPjvFWj/n+3k+O8VaP+f7eT6CH2k/EY4JPhEZaj+yZQk+tmprP+/+CD41RWw/V7E4Ph3MYj+R1HI+HcxiP5HUcj4RGWo/smUJPrZqaz/v/gg+O8VaP+f7eT6L3WY/fy4KPoIfaT8Rjgk+5SkvP3QMPD8BaDw/Tpw4P4KOOj+hLEA/go46P6EsQD+E9Cw/o69AP+UpLz90DDw/bqF3PziDbz/zjm8/kL94PwoPaj964W4/3Xp1P5Tbaj9mSWg/bkxnP+yFZj8UPl8/5SkvP3QMPD9sPj4/qp4wPwFoPD9OnDg/FViYPj/9Cz9hxkQ+lZsMP7AeRz5ZpwI/sn+uPsL7Wj+GyeQ+EwtUP5Aw5D42BV4/RWPlPmStTT+GyeQ+EwtUP7J/rj7C+1o/sn+uPsL7Wj+fVrE+G4NKP0Vj5T5krU0/RWPlPmStTT9O8OU+DcNDP/DEBD+In0M/8MQEP4ifQz+3QwM/18BOP0Vj5T5krU0/w9TmPlcIOz/93Og+5C4yP7Mk+D4EOTQ/syT4PgQ5ND/wxAQ/iJ9DP8PU5j5XCDs/8MQEP4ifQz9O8OU+DcNDP8PU5j5XCDs/syT4PgQ5ND/93Og+5C4yP82V6T4AdDA/06EDPthF4T7VBUw+4EnTPnakSj4FxOQ+JzFIPjKT+D7+e5g+TfP2PhVYmD4//Qs/FViYPj/9Cz+wHkc+WacCPycxSD4yk/g+JzFIPjKT+D5WuAU+Fcn3PtOhAz7YReE+06EDPthF4T52pEo+BcTkPicxSD4yk/g+vAMsPsAhxD4Bako+Eva9PoY3Sz46ecE+1QVMPuBJ0z7ToQM+2EXhPrwDLD7AIcQ+vAMsPsAhxD6GN0s+OnnBPtUFTD7gSdM+OGW+PqpE6T4WGMo+04XIPo0l5D7G/d8+jSXkPsb93z69/98+yCb5Pjhlvj6qROk+yatTPo1fSD9Smi0++BhMP8KJKD5tqkI/wokoPm2qQj9B9U8+/aM7P8mrUz6NX0g/QfVPPv2jOz/Ghk4+0/o3P5BNgj65cCg/kE2CPrlwKD/0FYQ+lx4xP0H1Tz79ozs/jSXkPsb93z4WGMo+04XIPpdvzT5/a7c+l2/NPn9rtz44heU+DcbYPo0l5D7G/d8+vf/fPsgm+T6NJeQ+xv3fPhx79j4fv+8+HHv2Ph+/7z4HQvI+xCMBP73/3z7IJvk+UpotPvgYTD/Jq1M+jV9IP+ymVD6jj1E/B0LyPsQjAT8G2t0+MbEFP73/3z7IJvk+bLELP6Wj3D1tqwU/fZMGPigPBz/Hgdc9vtrBPkd2BT5jRrg+8nfvPQu3xD4Ul4M9bLELP6Wj3D0oDwc/x4HXPQACBj/vkng9oSwEP0rtxTwAAgY/75J4PXZuAj+ndDA9Y0a4PvJ37z2DE7E+kGntPfhwwT4EIC49xAjJPnIZ9z2+2sE+R3YFPpHwxT7oE5k9weUNPwkywj1ssQs/paPcPQACBj/vkng90bAQPzz3vj3B5Q0/CTLCPQACBj/vkng9NBDTPszQOD0Lt8Q+FJeDPfFHyT40nDI909jOPrAf4jzxR8k+NJwyPW0fwj7KG+A8oSwEP0rtxTx2bgI/p3QwPfEtAD+kUuw8dm4CP6d0MD0AAgY/75J4PYE99j4YIxI9AAIGP++SeD2wjAE/lueBPYE99j4YIxI9G/PKPuOonD2R8MU+6BOZPTQQ0z7M0Dg9xJXzPUNwHD40nNI9dXgYPjUp5T0o7tg9baw0Pp5f9D2zszg+HLZtPQwBQD5tH/I9zc6iPa1oEz4dlJA9NbQRPuF8qj18Kbw93/xWPgBW5z0wLE8+ZHXrPUAWQj5Qi4E9MCxPPmR16z1FgEM+xvjwPUAWQj5Qi4E9NJzSPXV4GD41KJo98+c7PjFBrT3YCxU+5jtYPtXKBD1AFkI+UIuBPYjxOj5ETwo9ZRwDPmzrhz2tM949S1mGPVZG4z1I/Ao9QBZCPlCLgT2zszg+HLZtPYjxOj5ETwo9j9/7PTzBvj1fmdc9omK8Pa0z3j1LWYY9kNrEPSelwD3hfKo9fCm8PX2wjD1Hcnk9nMBUPnI1cj1AFkI+UIuBPeY7WD7VygQ9X5nXPaJivD2Q2sQ9J6XAPR6Koj3obIE9qkgRP+au5T0qqhI/OX0dPjQvDz84MeQ9B3zOPisVFD4bLOQ++3NhPr7awT5HdgU+OLy4PtjzBT7rObk+0sI1PiuGsz4zMgg+xAjJPnIZ9z0HfM4+KxUUPr7awT5HdgU+bLELP6Wj3D1GQgc/IJY9Pm2rBT99kwY+kfDFPugTmT2+2sE+R3YFPgu3xD4Ul4M9AAIGP++SeD0oDwc/x4HXPbCMAT+W54E9C7fEPhSXgz1jRrg+8nfvPfhwwT4EIC49G/PKPuOonD3ECMk+chn3PZHwxT7oE5k9oSwEP0rtxTzRsBA/PPe+PQACBj/vkng98UfJPjScMj0Lt8Q+FJeDPfhwwT4EIC49TFDLPuvjoTzT2M4+sB/iPG0fwj7KG+A8+HDBPgQgLj1tH8I+yhvgPPFHyT40nDI98S0AP6RS7Dx2bgI/p3QwPYE99j4YIxI9NBDTPszQOD2R8MU+6BOZPQu3xD4Ul4M9x0vXPgslUz0b88o+46icPTQQ0z7M0Dg9xJXzPUNwHD4k1dc9NBJRPjSc0j11eBg+NSnlPSju2D00nNI9dXgYPl+Z1z2iYrw9RYBDPsb48D2wqUM+uk4jPgwBQD5tH/I9MUGtPdgLFT41KJo98+c7Ps3Ooj2taBM+i8VfPl+a4j107Ws+FXMgPt/8Vj4AVuc9j9/7PTzBvj3ElfM9Q3AcPjUp5T0o7tg9X5nXPaJivD00nNI9dXgYPpDaxD0npcA9rTPePUtZhj1fmdc9omK8PR6Koj3obIE9j9/7PTzBvj01KeU9KO7YPV+Z1z2iYrw9Ja4DPlFNCT1lHAM+bOuHPS+G8j1gI8k8L4byPWAjyTxlHAM+bOuHPVZG4z1I/Ao9ZRwDPmzrhz2P3/s9PMG+Pa0z3j1LWYY9HoqiPehsgT2Q2sQ9J6XAPX2wjD1Hcnk9t2FkPppB/DycwFQ+cjVyPeY7WD7VygQ91zA/PyXN3z0c00M/R6vaPdY2RT89KAg+i91mP38uCj4Z42M/zqeOPc5Raz+MgfU91zA/PyXN3z0z4EQ/++Z+PRzTQz9Hq9o9orVGP2SV0jy9c0g/qMc2PTPgRD/75n49zlFrP4yB9T1+AWU/bTpCPUjhbj/hz/A9ExBjP1WhAT4rhmM/Y4CkPYvdZj9/Lgo+g/w8P4pbxT0z4EQ/++Z+PdcwPz8lzd89cjE6P0Ihwj0z4EQ/++Z+PYP8PD+KW8U9G0tcPznSWT3iIGE/rHNMPRnjYz/Op449HvtdPyRGDz1uT2Q/V+sEPeIgYT+sc0w9orVGP2SV0jxCtEo/pfj4PL1zSD+oxzY9vXNIP6jHNj2Dw08/GXYYPTPgRD/75n49M+BEP/vmfj2Dw08/GXYYPYJVST8XEYU9NBJhPwjpqT0bS1w/OdJZPSuGYz9jgKQ9bVY9P1nC6j4kCUI/pWXUPqOTQT9Z3eo+OblrP4qR3T4b9Ww/bqK+PoqQbj/i6d0+861HP/9Z6z5wB0o/5gjRPkP+ST9/pes+E+5lP0xQ2z74qWo/+nvBPubmZz+cMdw+5uZnP5wx3D74qWo/+nvBPhvYaj8MWd0+o5NBP1nd6j5bskY/zH/QPkBQRj+qgOs+NdRkPwwisj7UKGw/8kKyPvipaj/6e8E+f4VAP7q7vj5gzEY/dcmwPiNrRT9o0MA++KlqP/p7wT7UKGw/8kKyPhv1bD9uor4+vhZAP12ozD4ja0U/aNDAPnqORD9hUM4+W7JGP8x/0D4kl08/1H7DPnAHSj/mCNE+8fVlP7a7vz411GQ/DCKyPvipaj/6e8E+eo5EP2FQzj5ozEw/ZkrDPluyRj/Mf9A+mpk5P2bY6D0Qszs/PlvnPRk4OD/5ER8+o+lgP64PGz6L3WY/fy4KPibkVz/fFmw+tmprP+/+CD4XEG4/1EUKPjVFbD9XsTg+ExBjP1WhAT6L3WY/fy4KPqPpYD+uDxs+1zA/PyXN3z3WNkU/PSgIPg6gQz/gKj8+K4ZjP2OApD0Z42M/zqeOPYvdZj9/Lgo+M+BEP/vmfj2CVUk/FxGFPRzTQz9Hq9o9GeNjP86njj1+AWU/bTpCPc5Raz+MgfU9NBJhPwjpqT0rhmM/Y4CkPRMQYz9VoQE+orVGP2SV0jwz4EQ/++Z+PXIxOj9CIcI94iBhP6xzTD1+AWU/bTpCPRnjYz/Op449O45fPx9k2TxuT2Q/V+sEPR77XT8kRg89fgFlP206Qj3iIGE/rHNMPW5PZD9X6wQ9QrRKP6X4+DyDw08/GXYYPb1zSD+oxzY9G0tcPznSWT0Z42M/zqeOPSuGYz9jgKQ9WFZaPwcmdz0bS1w/OdJZPTQSYT8I6ak9bVY9P1nC6j6jk0E/Wd3qPnl2PT+t/AI/JAlCP6Vl1D56jkQ/YVDOPqOTQT9Z3eo+G9hqPwxZ3T45uWs/ipHdPs42az8Sv/I+QFBGP6qA6z7zrUc//1nrPrg8Rj+Qgv8+ibZjP8dM2j4T7mU/TFDbPsAgYT/oFvI+vhZAP12ozD4kCUI/pWXUPm1WPT9Zwuo+eo5EP2FQzj5bskY/zH/QPqOTQT9Z3eo+I2tFP2jQwD5ozEw/ZkrDPnqORD9hUM4+vhZAP12ozD56jkQ/YVDOPiQJQj+lZdQ+xXNCP/Zcrj6YikU/wjGrPn+FQD+6u74+mIpFP8Ixqz5gzEY/dcmwPn+FQD+6u74+f4VAP7q7vj4ja0U/aNDAPr4WQD9dqMw+aMxMP2ZKwz4kl08/1H7DPluyRj/Mf9A+R8dhP5CIsT411GQ/DCKyPvH1ZT+2u78+zc6iPa1oEz7hfKo9fCm8PZDaxD0npcA9kNrEPSelwD0xQa092AsVPs3Ooj2taBM+OblrP4qR3T4b2Go/DFndPvipaj/6e8E++KlqP/p7wT4b9Ww/bqK+Pjm5az+Kkd0+E+5lP0xQ2z6JtmM/x0zaPvH1ZT+2u78+8fVlP7a7vz74qWo/+nvBPhPuZT9MUNs+HZSQPTW0ET7NzqI9rWgTPjUomj3z5zs+NSiaPfPnOz6mtWk9SUxAPh2UkD01tBE+3/xWPgBW5z1AFkI+UIuBPZzAVD5yNXI9nMBUPnI1cj2LxV8+X5riPd/8Vj4AVuc9DAFAPm0f8j2zszg+HLZtPUAWQj5Qi4E9QBZCPlCLgT1FgEM+xvjwPQwBQD5tH/I95uZnP5wx3D6Px2Q/BoH1PsAgYT/oFvI+wCBhP+gW8j4T7mU/TFDbPubmZz+cMdw+G9hqPwxZ3T7ONms/Er/yPo/HZD8GgfU+j8dkPwaB9T7m5mc/nDHcPhvYaj8MWd0+MCxPPmR16z3f/FY+AFbnPXTtaz4VcyA+dO1rPhVzID6zl10+Z9UnPjAsTz5kdes9RYBDPsb48D0wLE8+ZHXrPbOXXT5n1Sc+s5ddPmfVJz6wqUM+uk4jPkWAQz7G+PA9Q/5JP3+l6z58fEo/YvcBP7g8Rj+Qgv8+uDxGP5CC/z7zrUc//1nrPkP+ST9/pes+861HP/9Z6z5AUEY/qoDrPluyRj/Mf9A+W7JGP8x/0D5wB0o/5gjRPvOtRz//Wes+kNrEPSelwD00nNI9dXgYPjFBrT3YCxU+sKlDPrpOIz5trDQ+nl/0PQwBQD5tH/I9zjZrPxK/8j45uWs/ipHdPoqQbj/i6d0+uDxGP5CC/z6jk0E/Wd3qPkBQRj+qgOs+FxBuP9RFCj62ams/7/4IPs5Raz+MgfU9zlFrP4yB9T1I4W4/4c/wPRcQbj/URQo+K4azPjMyCD6DE7E+kGntPWNGuD7yd+89Y0a4PvJ37z04vLg+2PMFPiuGsz4zMgg+NC8PPzgx5D3B5Q0/CTLCPdGwED8897490bAQPzz3vj2qSBE/5q7lPTQvDz84MeQ9ELM7Pz5b5z2amTk/ZtjoPXIxOj9CIcI9cjE6P0Ihwj2D/Dw/ilvFPRCzOz8+W+c9FNA8P1N65j3XMD8/Jc3fPWr3Oz9JSCQ+g/w8P4pbxT3XMD8/Jc3fPRTQPD9TeuY9MBIOP0xQ4z3Z6g4/RrMiPmyxCz+lo9w9weUNPwkywj0wEg4/TFDjPWyxCz+lo9w9r+sXPdlAQj8yIPs8igNEP8uf7zyLb0A/e70bP9TS9D5o5h0//5HxPiY4HT9in/g+AgAQAAIAAgACABAAFAACAAIADQACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIACgACAAIAAgAKAAIAAgACAAoACwACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIAAgAQAAIAAgACABAAAgACAAIADQACAAIAAgAQAAIAAgADAAIAAgACAAMAAgACAAIAAwACAAIAAgADAAIAAgACAAIAEAACAAIAAgAQAAIAAgACAAIAAgACAAIAAgACAAIAAgACAAIAAgACABAAFAACAAIAEAACAAIAAgAQAAIAAgACABAAAgACABEAEAACAAIACgACAAIAAgAKAAsAAgACAAsACgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwAOAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIAAwACAAIAAgADABAAAgACAAoABAACAAIAAgAQAAIAAgACABAAAgACAAIADQACAAIABAAFAAcACgAKAAQAAgACAAoAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABQAEAAIAAgAKAAIAAgACAAUABAACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIAAgAQAAIAAgADAAIAAgACAAIAEAARAAIABAAFAAcACgAEAAoABwACAAoABAACAAIAAgANAAIAAgACABAAAgACAAIADQACAAIAAwACAAIAAgADAAIAAgACAAIAEAACAAIAAgAQABEAAgADAAIAAgACAAIAAgACAAIACgACAAIAAgAKAAQAAgACAAoAAgACAAIADwACAAIAAgAPAA4AAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIAAgAUAAIAAgACAA0AAgACAAIAEAAUAAIACgACAAIAAgAFAAQAAgACAAUABAACAAIABQAEAAIAAgAEAAUABwAKAAoAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABwACAAIAAgAHAAgAAgACAAcAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIAAgAUAAIAAgACAA0AAgACAAIAFAACAAIAAwACAAIAAgADAAIAAgACAAMAAgACAAIAAwACAAIAAgACABQAAgACAAMAAgACAAIAAgAQABQAAgACAAIAAgACAAIAAgACAAIAAgACAAIAAgACABQAAgACAAIAEAAUAAIAAgAUAAIAAgAVABQAAgACAAIAFAACAAIABwACAAIAAgAIAAcAAgACAAcACAACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwAOAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIAAwACAAIAAgAHAAQAAgACAAMAFAACAAIAAgAUAAIAAgACAA0AAgACAAIAFAACAAIABAAFAAcACgAHAAIAAgACAAcABAACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABQAEAAIAAgAFAAQAAgACAAcAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIAAgAUAAIAAgACABQAFQACAAMAAgACAAIABAAFAAcACgAHAAQAAgACAAQACgAHAAIAAgANAAIAAgACAA0AAgACAAIAFAACAAIAAwACAAIAAgACABQAAgACAAMAAgACAAIAAgAUABUAAgACABQAAgACAAIAAgACAAIABwACAAIAAgAHAAIAAgACAAcABAACAAIADwACAAIAAgAPAAIAAgACAA8ADgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIADwACAAIAAgAPAAIAAgACAA8AAgACAAIABwACAAIAAgAEAAUABwAKAAUABAACAAIABQAEAAIAAgAFAAQAAgACAAcAAgACAAIACgACAAIAAgAKAAIAAgACAAoAAgACAAIACgACAAIAAgAKAAQAAgACAAoAAgACAAIACgACAAIAAgALAAoAAgACAAoACwACAAIACgALAAIAAgAKAAIAAgACAAoAAgACAAIABwACAAIAAgAHAAQAAgACAAcAAgACAAIABwACAAIAAgAHAAIAAgACAAcAAgACAAIACgALAAIAAgAKAAIAAgACAAoABAACAAIACgAEAAIAAgAEAAoABwACAAoACwACAAIABwAIAAIAAgAEAAoABwACAAcABAACAAIABwAEAAIAAgAHAAIAAgACAAcACAACAAIABwACAAIAAgAHAAIAAgACAAcACAACAAIABwAIAAIAAgAIAAcAAgACAAcAAgACAAIAAwAUAAIAAgACABQAAgACAAIAFAACAAIAAgAUAAIAAgADAAIAAgACAAMAFAACAAIABAAKAAcAAgADAAIAAgACAAMAAgACAAIAAwACAAIAAgAEAAoABwACAAcABAACAAIABwAEAAIAAgADAAIAAgACAAMAAgACAAIAAwAQAAIAAgADAAIAAgACAAIAEAACAAIAAgAQAAIAAgACABAAAgACAAMAEAACAAIABAAKAAcAAgADAAIAAgACAAMAAgACAAIAAwACAAIAAgADAAIAAgACAAoABAACAAIACgAEAAIAAgAEAAoABwACAAMAAgACAAIAAwACAAIAAgACAAIAAgACAAIAAgACAAIAAwACAAIAAgACAAIAAgACAAIAAgACAAIAAwACAAIAAgACABQAFQACAAIAAgACAAIAAgAQAAIAAgACABAAEQACAAIAAgACAAIABQAEAAIAAgAFAAQAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAUABAACAAIABQAEAAIAAgAFAAQAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAUABAACAAIABQAEAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAFAAQAAgACAAUABAACAAIABQAEAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAFAAQAAgACAAUABAACAAIAAgANAAIAAgACAA0AAgACAA0ADgACAAIADQAOAAIAAgACAA0AAgACAA0ADgACAAIACgAEAAIAAgADABAAAgACAAMAEAACAAIAAwAQAAIAAgAKAAQAAgACAAoABAACAAIAAwACAAIAAgADAAIAAgACAAMAAgACAAIAAwACAAIAAgACAAIAAgACAAMAAgACAAIAAgANAAIAAgACABAAAgACAAIADQACAAIAAgACAAIAAgACABAAAgACAAIAEAACAAIAAgAQAAIAAgACABAAAgACAAIADQACAAIACgAEAAIAAgAKAAQAAgACAAoACwACAAIABAACAAIAAgAEAAIAAgACAAUABAACAAIABAACAAIAAgAEAAIAAgACAAUABAACAAIABgACAAIAAgAFAAQAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABQAEAAIAAgAFAAQAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABQAEAAIAAgAFAAQAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIAAgANAAIAAgACAA0AAgACAA0ADgACAAIAAgAQAAIAAgACABAAAgACABEAEAACAAIADQAOAAIAAgANAA4AAgACAA8ADgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABAACAAIAAgAEAAIAAgACAAQAAgACAAIAAgACAAIAAgACAAIAAgACAAIADQACAAIAAgANAAIAAgACAA0AAgACAAIADQACAAIAAgAQAAIAAgACABAAAgACABAAEQACAAIAAgAQAAIAAgACABAAAgACABEAEAACAAIAAgAQAAIAAgACABAAAgACABEAEAACAAIAAgAQAAIAAgACABAAAgACABEAEAACAAIAEQASAAIAAgARABIAAgACABEAEgACAAIAEQASAAIAAgARABIAAgACABEAEgACAAIAEQAQAAIAAgACABAAEQACABEAEgACAAIAEQASAAIAAgARABIAAgACABEAEgACAAIAEQASAAIAAgARABIAAgACABEAEgACAAIAEQAQAAIAAgARABAAAgACABEAAgACAAIAEAARAAIAAgARABAAAgACABEAAgACAAIAEQASAAIAAgARABIAAgACABEAEgACAAIACgALAAIAAgAKAAsAAgACAAsAAgACAAIACwAKAAIAAgAKAAsAAgACAAsAAgACAAIABwACAAIAAgAHAAIAAgACAAcAAgACAAIABwACAAIAAgAHAAIAAgACAAcAAgACAAIABAAKAAcAAgAKAAQAAgACAAoACwACAAIACgALAAIAAgAKAAsAAgACAAsAAgACAAIADQAOAAIAAgACAA0AAgACAA0ADgACAAIADQAOAAIAAgAPAA4AAgACAA0ADgACAAIAAgANAAIAAgACAA0AAgACAA0ADgACAAIADQAOAAIAAgANAA4AAgACAAIADQACAAIADwAOAAIAAgAPAA4AAgACAA8AAgACAAIADwAOAAIAAgAPAA4AAgACAA8AAgACAAIADwAOAAIAAgAPAA4AAgACAA8AAgACAAIADwAOAAIAAgAPAA4AAgACAA8AAgACAAIADwAOAAIAAgAPAA4AAgACAA8AAgACAAIAEQAQAAIAAgACABAAAgACAAIAEAARAAIAAwACAAIAAgACAAIAAgACAAIAAgACAAIAAgANAAIAAgACAA0AAgACAAIADQACAAIAAgANAAIAAgACAA0AAgACAAIADQACAAIAAgANAAIAAgACAAIAAgACAAIAEAACAAIABAACAAIAAgAEAAIAAgACAAQAAgACAAIAAgANAAIAAgACABAAAgACAAIADQACAAIABQAEAAIAAgAEAAIAAgACAAUABAACAAIABQAEAAIAAgAEAAIAAgACAAUABAACAAIACgALAAIAAgAKAAQAAgACAAoACwACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAFAAQAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIACgALAAIAAgAKAAQAAgACAAoACwACAAIACgACAAIAAgAKAAIAAgACAAoAAgACAAIAAwAQAAIAAgADAAIAAgACAAoABAACAAIADQAOAAIAAgACAA0AAgACAAIADQACAAIAEAARAAIAAgACABAAAgACABEAEAACAAIADwAOAAIAAgANAA4AAgACAA8ADgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABAACAAIAAgAEAAIAAgACAAQAAgACAAIAAgANAAIAAgACAAIAAgACAAIADQACAAIAEQAQAAIAAgACABAAAgACABAAEQACAAIAAwACAAIAAgADAAIAAgACAAIAAgACAAIAEQAQAAIAAgACABAAAgACABEAEAACAAIAEQAQAAIAAgACABAAAgACABEAEAACAAIAEQAQAAIAAgACABAAAgACABEAEAACAAIAEQASAAIAAgACABAAEQACABEAEgACAAIACgALAAIAAgAEAAoABwACAAoACwACAAIACwACAAIAAgAKAAsAAgACAAsACgACAAIADwAOAAIAAgANAA4AAgACAA8ADgACAAIADwAOAAIAAgANAA4AAgACAA8ADgACAAIADwAOAAIAAgANAA4AAgACAA8ADgACAAIADwAOAAIAAgANAA4AAgACAA0ADgACAAIADwACAAIAAgAPAA4AAgACAA8AAgACAAIADwACAAIAAgAPAA4AAgACAA8AAgACAAIADwACAAIAAgAPAA4AAgACAA8AAgACAAIAAgANAAIAAgANAA4AAgACAAIADQACAAIADQAOAAIAAgANAA4AAgACAAIADQACAAIABwAEAAIAAgADABQAAgACAAMAFAACAAIAAwAUAAIAAgAHAAQAAgACAAcABAACAAIAAgACAAIAAgADAAIAAgACAAMAAgACAAIAAwACAAIAAgADAAIAAgACAAIAAgACAAIABAACAAIAAgAHAAIAAgACAAcAAgACAAIABwACAAIAAgAEAAIAAgACAAQAAgACAAIAAgANAAIAAgACAA0AAgACAAIAFAACAAIAAgACAAIAAgACABQAAgACAAIAFAACAAIAAgAUAAIAAgACAA0AAgACAAIAFAACAAIABwAEAAIAAgAHAAgAAgACAAcABAACAAIABAACAAIAAgAFAAQAAgACAAQAAgACAAIABAACAAIAAgAFAAQAAgACAAQAAgACAAIABgACAAIAAgAGAAIAAgACAAUABAACAAIABAACAAIAAgAFAAQAAgACAAUABAACAAIABQAEAAIAAgAKAAIAAgACAAQAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABQAEAAIAAgAGAAIAAgACAAUABAACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABQAEAAIAAgAGAAIAAgACAAUABAACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIAAgANAAIAAgANAA4AAgACAAIADQACAAIAAgAUAAIAAgAVABQAAgACAAIAFAACAAIADQAOAAIAAgAPAA4AAgACAA0ADgACAAIABgACAAIAAgAGAAIAAgACAAYAAgACAAIABAACAAIAAgAEAAIAAgACAAQAAgACAAIAAgACAAIAAgACAA0AAgACAAIAAgACAAIAAgANAAIAAgACAA0AAgACAAIADQACAAIAAgAUAAIAAgAUABUAAgACAAIAFAACAAIAAgAUAAIAAgAVABQAAgACAAIAFAACAAIAAgAUAAIAAgAVABQAAgACAAIAFAACAAIAAgAUAAIAAgAVABQAAgACAAIAFAACAAIAFQAWAAIAAgAVABYAAgACABUAFgACAAIAFQAUAAIAAgAVABYAAgACAAIAFAAVAAIAFQAWAAIAAgAVABYAAgACABUAFgACAAIAFQAWAAIAAgAVABYAAgACABUAFgACAAIAFQAUAAIAAgAVAAIAAgACABUAFAACAAIAFAAVAAIAAgAVAAIAAgACABUAFAACAAIAFQAWAAIAAgAVABYAAgACABUAFgACAAIABwAIAAIAAgAIAAIAAgACAAcACAACAAIACAAHAAIAAgAIAAIAAgACAAcACAACAAIABAAKAAcAAgAHAAgAAgACAAcABAACAAIABwAIAAIAAgAIAAIAAgACAAcACAACAAIADQAOAAIAAgAPAA4AAgACAA0ADgACAAIADQAOAAIAAgAPAA4AAgACAA0ADgACAAIADQAOAAIAAgAPAA4AAgACAA8ADgACAAIADwAOAAIAAgANAA4AAgACAA0ADgACAAIADwAOAAIAAgAPAAIAAg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",
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",
+            "mimeType": "image/png"
+        }
+    ],
+    "materials": [
+        {
+            "name": "fox_material",
+            "pbrMetallicRoughness": {
+                "baseColorTexture": {
+                    "index": 0
+                },
+                "metallicFactor": 0,
+                "roughnessFactor": 0.58
+            }
+        }
+    ],
+    "meshes": [
+        {
+            "name": "fox1",
+            "primitives": [
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diff --git a/demos/model3D/files/face.obj b/demos/model3D/files/face.obj
new file mode 100644
index 0000000000000000000000000000000000000000..a2e82ee9cb6dd5a9fc6cb612dfea0bd5e7eab162
--- /dev/null
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+f 780 783 778
+# 1610 faces, 0 coords texture
+
+# End of File
diff --git a/demos/model3D/files/sofia.stl b/demos/model3D/files/sofia.stl
new file mode 100644
index 0000000000000000000000000000000000000000..8a811c851675350bc5f9ab0d1f0eff69651104ae
Binary files /dev/null and b/demos/model3D/files/sofia.stl differ
diff --git a/demos/model3D/files/source.txt b/demos/model3D/files/source.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95f4bcc925f0bf5cf6a00913e13532fd0616f77a
--- /dev/null
+++ b/demos/model3D/files/source.txt
@@ -0,0 +1,13 @@
+Stanford Bunny:
+https://graphics.stanford.edu/data/3Dscanrep/
+https://graphics.stanford.edu/~mdfisher/Data/Meshes/bunny.obj
+
+Duck & Fox:
+https://github.com/KhronosGroup/glTF-Sample-Models
+
+Face:
+https://github.com/mikedh/trimesh/tree/main/models
+
+NASA SOFIA:
+https://nasa3d.arc.nasa.gov/detail/sofia
+https://github.com/nasa/NASA-3D-Resources/blob/master/3D%20Models/SOFIA/Fuselage_top.stl
\ No newline at end of file
diff --git a/demos/model3D/run.ipynb b/demos/model3D/run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..17b9ec140751406d0d006d43ab2d2ff94a34ef68
--- /dev/null
+++ b/demos/model3D/run.ipynb
@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: model3D"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/Bunny.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Bunny.obj\n", "!wget -q -O files/Duck.glb https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Duck.glb\n", "!wget -q -O files/Fox.gltf https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/Fox.gltf\n", "!wget -q -O files/face.obj https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/face.obj\n", "!wget -q -O files/sofia.stl https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/sofia.stl\n", "!wget -q -O files/source.txt https://github.com/gradio-app/gradio/raw/main/demo/model3D/files/source.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def load_mesh(mesh_file_name):\n", "    return mesh_file_name\n", "\n", "demo = gr.Interface(\n", "    fn=load_mesh,\n", "    inputs=gr.Model3D(),\n", "    outputs=gr.Model3D(\n", "            clear_color=(0.0, 0.0, 0.0, 0.0),  label=\"3D Model\", display_mode=\"wireframe\"),\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"files/Bunny.obj\")],\n", "        [os.path.join(os.path.abspath(''), \"files/Duck.glb\")],\n", "        [os.path.join(os.path.abspath(''), \"files/Fox.gltf\")],\n", "        [os.path.join(os.path.abspath(''), \"files/face.obj\")],\n", "        [os.path.join(os.path.abspath(''), \"files/sofia.stl\")],\n", "        [\"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat\"],\n", "        [\"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply\"],\n", "    ],\n", "    cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file