diff --git a/.gitattributes b/.gitattributes index 4d2b05a547adbc1c08e62fc38f450c623645cc37..235c576ca18f05ebb43f97ee3525ddfef6a7eccf 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/gradio_pdf_demo/contract.pdf 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..dd43dae01dd7ec4f4a2dbac3523926bb7ebb1d68 --- /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/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\"),\n", " 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..69d51fd61c6edf2b5659136fb397573f342e3e75 --- /dev/null +++ b/demos/file_explorer_component_events/run.py @@ -0,0 +1,37 @@ +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/gradio_pdf_demo/contract.pdf b/demos/gradio_pdf_demo/contract.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e7a129731973bf719bc081a1f28ac924b136e45b --- /dev/null +++ b/demos/gradio_pdf_demo/contract.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6231f1a3e68391bc9c9da02a38b15792782fb8af959e618f60d02dda27297ab8 +size 127928 diff --git a/demos/gradio_pdf_demo/requirements.txt b/demos/gradio_pdf_demo/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..01b311df431781baa1927d2efdf022121124e50b --- /dev/null +++ b/demos/gradio_pdf_demo/requirements.txt @@ -0,0 +1 @@ +gradio_pdf==0.0.7 diff --git a/demos/gradio_pdf_demo/run.ipynb b/demos/gradio_pdf_demo/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3fe450f74243cae0f8a8d9f904f509599a6e659f --- /dev/null +++ b/demos/gradio_pdf_demo/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: gradio_pdf_demo"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio gradio_pdf==0.0.7 "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/gradio_pdf_demo/contract.pdf"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio_pdf import PDF\n", "from pathlib import Path\n", "\n", "current_dir = Path(__file__).parent\n", "\n", "demo = gr.Interface(lambda x: x,\n", " PDF(),\n", " gr.File(),\n", " examples=[[str(current_dir / \"contract.pdf\")]],\n", " api_name=\"predict\")\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/gradio_pdf_demo/run.py b/demos/gradio_pdf_demo/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d366a0a131ee32ab72726b4f1ee560fcffa962c9 --- /dev/null +++ b/demos/gradio_pdf_demo/run.py @@ -0,0 +1,14 @@ +import gradio as gr +from gradio_pdf import PDF +from pathlib import Path + +current_dir = Path(__file__).parent + +demo = gr.Interface(lambda x: x, + PDF(), + gr.File(), + examples=[[str(current_dir / "contract.pdf")]], + api_name="predict") + +if __name__ == "__main__": + demo.launch() diff --git a/demos/iframe_resizer/run.ipynb b/demos/iframe_resizer/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7655427985d65328bcc21d919207ca55fc009f48 --- /dev/null +++ b/demos/iframe_resizer/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: iframe_resizer"]}, {"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", "import time\n", "import os\n", "from gradio import get_image\n", "\n", "\n", "def greet():\n", " gr.Info(\"Warning in 1 second\")\n", " time.sleep(1)\n", " gr.Warning(\"Error in 1 second\")\n", " time.sleep(1)\n", " raise Exception(\"test\")\n", "\n", "\n", "im = get_image(\"cheetah.jpg\")\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Tab(\"Accordions\"):\n", " with gr.Row(height=1500):\n", " gr.Markdown(\"Scroll down to see UI.\")\n", " greet_btn = gr.Button(\"Trigger toast\")\n", " greet_btn.click(fn=greet)\n", "\n", " with gr.Accordion(\"Accordion\"):\n", " gr.Markdown(\n", " \"\"\"\n", " ## Accordion content\n", " ### Accordion content\n", " #### Accordion content\n", " ##### Accordion content\n", " ###### Accordion content\n", " \"\"\"\n", " )\n", " with gr.Tab(\"Images\"):\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=im)\n", " gr.Image(value=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/iframe_resizer/run.py b/demos/iframe_resizer/run.py new file mode 100644 index 0000000000000000000000000000000000000000..42bf1505b4859bf53b559adec8d59681bfa34d51 --- /dev/null +++ b/demos/iframe_resizer/run.py @@ -0,0 +1,49 @@ +import gradio as gr +import time +import os +from gradio import get_image + + +def greet(): + gr.Info("Warning in 1 second") + time.sleep(1) + gr.Warning("Error in 1 second") + time.sleep(1) + raise Exception("test") + + +im = get_image("cheetah.jpg") + +with gr.Blocks() as demo: + with gr.Tab("Accordions"): + with gr.Row(height=1500): + gr.Markdown("Scroll down to see UI.") + greet_btn = gr.Button("Trigger toast") + greet_btn.click(fn=greet) + + with gr.Accordion("Accordion"): + gr.Markdown( + """ + ## Accordion content + ### Accordion content + #### Accordion content + ##### Accordion content + ###### Accordion content + """ + ) + with gr.Tab("Images"): + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + gr.Image(value=im) + + +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..a60f7abb29c259c71af04a939c2757bf8f263b84 --- /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", "import numpy as np\n", "\n", "\n", "def predict(im):\n", " return im[\"composite\"]\n", "\n", "\n", "def verify_clear(im):\n", " print(im)\n", " return int(not np.any(im[\"composite\"])), im[\"composite\"]\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Group():\n", " with gr.Row():\n", " im = gr.ImageEditor(\n", " type=\"numpy\",\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", " cleared_properly = gr.Number(label=\"cleared properly\")\n", " clear_btn = gr.Button(\"Clear Button\", 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", " ).then(verify_clear, inputs=im, outputs=[cleared_properly, im])\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..a0407f47b136a398ad801386aea88240ef55d086 --- /dev/null +++ b/demos/image_editor_events/run.py @@ -0,0 +1,68 @@ +import gradio as gr +import numpy as np + + +def predict(im): + return im["composite"] + + +def verify_clear(im): + print(im) + return int(not np.any(im["composite"])), im["composite"] + + +with gr.Blocks() as demo: + with gr.Group(): + with gr.Row(): + im = gr.ImageEditor( + type="numpy", + 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", + ) + cleared_properly = gr.Number(label="cleared properly") + clear_btn = gr.Button("Clear Button", 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, + ).then(verify_clear, inputs=im, outputs=[cleared_properly, im]) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/image_mod_default_image/run.ipynb b/demos/image_mod_default_image/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a202611e1d5eb960720a6287d60a655b2193dc62 --- /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": ["import gradio as gr\n", "from gradio.media import get_image\n", "\n", "def image_mod(image):\n", " return image.rotate(45)\n", "\n", "# get_image() returns file paths to sample media included with Gradio\n", "cheetah = get_image(\"cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", " api_name=\"predict\",\n", " flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", " get_image(\"lion.jpg\"),\n", " get_image(\"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..86ed6665f3b4bca1e4fafd590cf489bc7cd4ecc4 --- /dev/null +++ b/demos/image_mod_default_image/run.py @@ -0,0 +1,18 @@ +import gradio as gr +from gradio.media import get_image + +def image_mod(image): + return image.rotate(45) + +# get_image() returns file paths to sample media included with Gradio +cheetah = get_image("cheetah1.jpg") + +demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image", + api_name="predict", + flagging_options=["blurry", "incorrect", "other"], examples=[ + get_image("lion.jpg"), + get_image("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..dc19959722a8d769e6e203eb95266f7b3b0d624b --- /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", " api_name=\"predict\"\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..0a9ba702c15f05d9143effebc16fb9f76c5bd9ab --- /dev/null +++ b/demos/interface_random_slider/run.py @@ -0,0 +1,21 @@ +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", + api_name="predict" +) + +if __name__ == "__main__": + demo.launch() 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..e4376abac9bdab64bf731b2ee4b0574aa07145bb --- /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": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "from gradio.media import get_image, get_video, get_audio, get_file\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", " time,\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 get_audio(\"cantina.wav\"), # Audio\n", " np.flipud(im1)\n", " if im1 is not None\n", " else get_image(\"cheetah1.jpg\"), # Image\n", " video\n", " if video is not None\n", " else get_video(\"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", " \"\", # HTML\n", " get_file(\"titanic.csv\"), # File\n", " df1, # Dataframe\n", " np.random.randint(0, 10, (4, 4)), # Dataframe\n", " time, # DateTime\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", " gr.DateTime(label=\"DateTime\"),\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\", show_indices=True),\n", " gr.HTML(label=\"HTML\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\"),\n", " gr.Dataframe(label=\"Numpy\"),\n", " gr.DateTime(label=\"DateTime\"),\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", " get_image(\"cheetah1.jpg\"),\n", " # get_image(\"cheetah1.jpg\"),\n", " # get_image(\"cheetah1.jpg\"),\n", " get_image(\"cheetah1.jpg\"),\n", " get_video(\"world.mp4\"),\n", " get_audio(\"cantina.wav\"),\n", " get_audio(\"cantina.wav\"),\n", " get_file(\"titanic.csv\"),\n", " [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", " \"2025-06-10 12:00:00\",\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", " api_name=\"predict\"\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..ca060c6b3a181539054d3be69796a3b55107f8bb --- /dev/null +++ b/demos/kitchen_sink/run.py @@ -0,0 +1,166 @@ +import os +import json + +import numpy as np + +import gradio as gr +from gradio.media import get_image, get_video, get_audio, get_file + +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, + time, +): + 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 get_audio("cantina.wav"), # Audio + np.flipud(im1) + if im1 is not None + else get_image("cheetah1.jpg"), # Image + video + if video is not None + else get_video("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 + "", # HTML + get_file("titanic.csv"), # File + df1, # Dataframe + np.random.randint(0, 10, (4, 4)), # Dataframe + time, # DateTime + ) + +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"]), + gr.DateTime(label="DateTime"), + ], + 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", show_indices=True), + gr.HTML(label="HTML"), + gr.File(label="File"), + gr.Dataframe(label="Dataframe"), + gr.Dataframe(label="Numpy"), + gr.DateTime(label="DateTime"), + ], + examples=[ + [ + "the quick brown fox", + "jumps over the lazy dog", + 10, + 12, + 4, + True, + ["foo", "baz"], + "baz", + "bar", + ["foo", "bar"], + get_image("cheetah1.jpg"), + # get_image("cheetah1.jpg"), + # get_image("cheetah1.jpg"), + get_image("cheetah1.jpg"), + get_video("world.mp4"), + get_audio("cantina.wav"), + get_audio("cantina.wav"), + get_file("titanic.csv"), + [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]], + "2025-06-10 12:00:00", + ] + ] + * 3, + title="Kitchen Sink", + description="Try out all the components!", + article="Learn more about [Gradio](http://gradio.app)", + cache_examples=True, + api_name="predict" +) + +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..239bb2f058fddb4afd5ce94096ec66f0a2bfffca --- /dev/null +++ b/demos/kitchen_sink_random/constants.py @@ -0,0 +1,59 @@ +import numpy as np +import matplotlib.pyplot as plt +import random +from gradio.media import get_model3d + +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 + +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(): + return get_model3d() 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..298991bbe21fff84cefeda7e0ac65224553d36c0 --- /dev/null +++ b/demos/kitchen_sink_random/run.py @@ -0,0 +1,93 @@ +import gradio as gr +from datetime import datetime +import random +import string +import pandas as pd + +# get_audio(), get_video(), get_image(), get_model3d(), get_file() return file paths to sample media included with Gradio +from gradio.media import get_audio, get_video, get_image, get_model3d, get_file + +from constants import ( # type: ignore + highlighted_text, + highlighted_text_output_2, + highlighted_text_output_1, + random_plot, +) + +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: get_image() + ), + gr.Video(value=lambda: get_video("world.mp4")), + gr.Audio(value=lambda: get_audio("cantina.wav")), + gr.File( + value=lambda: get_file("titanic.csv") + ), + 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( + [ + '

I am red

', + '

I am blue

', + ] + ) + ), + gr.Gallery( + value=lambda: [get_image() for _ in range(3)] + ), + gr.Chatbot( + value=lambda: random.choice([[("hello", "hi!")], [("bye", "goodbye!")]]) + ), + gr.Model3D(value=lambda: get_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)) + ], + api_name="predict", +) + +if __name__ == "__main__": + demo.launch() diff --git a/demos/login_with_huggingface/requirements.txt b/demos/login_with_huggingface/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6b964ccca3c1b6766042b3fe3b2707ba25372924 --- /dev/null +++ b/demos/login_with_huggingface/requirements.txt @@ -0,0 +1 @@ +huggingface_hub diff --git a/demos/login_with_huggingface/run.ipynb b/demos/login_with_huggingface/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3b08a319a43da7e54eb4e7f73df59c67a8a5dccb --- /dev/null +++ b/demos/login_with_huggingface/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: login_with_huggingface"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio huggingface_hub "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from __future__ import annotations\n", "\n", "import gradio as gr\n", "from huggingface_hub import whoami\n", "\n", "def hello(profile: gr.OAuthProfile | None) -> str:\n", " if profile is None:\n", " return \"I don't know you.\"\n", " return f\"Hello {profile.name}\"\n", "\n", "def list_organizations(oauth_token: gr.OAuthToken | None) -> str:\n", " if oauth_token is None:\n", " return \"Please deploy this on Spaces and log in to list organizations.\"\n", " org_names = [org[\"name\"] for org in whoami(oauth_token.token)[\"orgs\"]]\n", " return f\"You belong to {', '.join(org_names)}.\"\n", "\n", "with gr.Blocks() as demo:\n", " gr.LoginButton()\n", " m1 = gr.Markdown()\n", " m2 = gr.Markdown()\n", " demo.load(hello, inputs=None, outputs=m1)\n", " demo.load(list_organizations, inputs=None, outputs=m2)\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/login_with_huggingface/run.py b/demos/login_with_huggingface/run.py new file mode 100644 index 0000000000000000000000000000000000000000..980dd2babda6b453011e5d1994540356dbe15202 --- /dev/null +++ b/demos/login_with_huggingface/run.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +import gradio as gr +from huggingface_hub import whoami + +def hello(profile: gr.OAuthProfile | None) -> str: + if profile is None: + return "I don't know you." + return f"Hello {profile.name}" + +def list_organizations(oauth_token: gr.OAuthToken | None) -> str: + if oauth_token is None: + return "Please deploy this on Spaces and log in to list organizations." + org_names = [org["name"] for org in whoami(oauth_token.token)["orgs"]] + return f"You belong to {', '.join(org_names)}." + +with gr.Blocks() as demo: + gr.LoginButton() + m1 = gr.Markdown() + m2 = gr.Markdown() + demo.load(hello, inputs=None, outputs=m1) + demo.load(list_organizations, inputs=None, outputs=m2) + +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..d7631cdbdd273cef915c4dd8d2e735dc1e31feed --- /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, column_count=3, buttons=[\"fullscreen\"]),\n", " \"numpy\",\n", " examples=[\n", " [np.zeros((30, 30)).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", " api_name=\"predict\"\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..2127775a5b65a602551b2062811a69ab5de84f2b --- /dev/null +++ b/demos/matrix_transpose/run.py @@ -0,0 +1,24 @@ +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, column_count=3, buttons=["fullscreen"]), + "numpy", + examples=[ + [np.zeros((30, 30)).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, + api_name="predict" +) + +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":"davidkim205\/Rhea-72b-v0.5<\/a> \ud83d\udcd1<\/a>","1":"MTSAIR\/MultiVerse_70B<\/a> \ud83d\udcd1<\/a>","2":"MTSAIR\/MultiVerse_70B<\/a> \ud83d\udcd1<\/a>","3":"SF-Foundation\/Ein-72B-v0.11<\/a> \ud83d\udcd1<\/a>","4":"SF-Foundation\/Ein-72B-v0.13<\/a> \ud83d\udcd1<\/a>","5":"SF-Foundation\/Ein-72B-v0.12<\/a> \ud83d\udcd1<\/a>","6":"abacusai\/Smaug-72B-v0.1<\/a> \ud83d\udcd1<\/a>","7":"ibivibiv\/alpaca-dragon-72b-v1<\/a> \ud83d\udcd1<\/a>","8":"moreh\/MoMo-72B-lora-1.8.7-DPO<\/a> \ud83d\udcd1<\/a>","9":"cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16<\/a> \ud83d\udcd1<\/a>","10":"saltlux\/luxia-21.4b-alignment-v1.0<\/a> \ud83d\udcd1<\/a>","11":"cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO<\/a> \ud83d\udcd1<\/a>","12":"zhengr\/MixTAO-7Bx2-MoE-v8.1<\/a> \ud83d\udcd1<\/a>","13":"yunconglong\/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B<\/a> \ud83d\udcd1<\/a>","14":"JaeyeonKang\/CCK_Asura_v1<\/a> \ud83d\udcd1<\/a>","15":"fblgit\/UNA-SimpleSmaug-34b-v1beta<\/a> \ud83d\udcd1<\/a>","16":"TomGrc\/FusionNet_34Bx2_MoE_v0.1<\/a> \ud83d\udcd1<\/a>","17":"migtissera\/Tess-72B-v1.5b<\/a> \ud83d\udcd1<\/a>","18":"moreh\/MoMo-72B-lora-1.8.6-DPO<\/a> \ud83d\udcd1<\/a>","19":"abacusai\/Smaug-34B-v0.1<\/a> \ud83d\udcd1<\/a>","20":"cloudyu\/Truthful_DPO_TomGrc_FusionNet_34Bx2_MoE<\/a> \ud83d\udcd1<\/a>","21":"ibivibiv\/orthorus-125b-v2<\/a> \ud83d\udcd1<\/a>","22":"ConvexAI\/Luminex-34B-v0.2<\/a> \ud83d\udcd1<\/a>","23":"yunconglong\/DARE_TIES_13B<\/a> \ud83d\udcd1<\/a>","24":"yunconglong\/13B_MATH_DPO<\/a> \ud83d\udcd1<\/a>","25":"TomGrc\/FusionNet_34Bx2_MoE<\/a> \ud83d\udcd1<\/a>","26":"ConvexAI\/Luminex-34B-v0.1<\/a> \ud83d\udcd1<\/a>","27":"yunconglong\/MoE_13B_DPO<\/a> \ud83d\udcd1<\/a>","28":"JaeyeonKang\/CCK_Asura_v3.0<\/a> \ud83d\udcd1<\/a>","29":"cloudyu\/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO<\/a> \ud83d\udcd1<\/a>","30":"yam-peleg\/Experiment26-7B<\/a> \ud83d\udcd1<\/a>","31":"MTSAIR\/multi_verse_model<\/a> \ud83d\udcd1<\/a>","32":"chihoonlee10\/T3Q-Mistral-Orca-Math-DPO<\/a> \ud83d\udcd1<\/a>","33":"yam-peleg\/Experiment26-7B<\/a> \ud83d\udcd1<\/a>","34":"rwitz\/experiment26-truthy-iter-0<\/a> \ud83d\udcd1<\/a>","35":"yam-peleg\/Experiment30-7B<\/a> \ud83d\udcd1<\/a>","36":"yam-peleg\/Experiment28-7B<\/a> \ud83d\udcd1<\/a>","37":"MaziyarPanahi\/Calme-7B-Instruct-v0.2<\/a> \ud83d\udcd1<\/a>","38":"rwitz\/experiment26-truthy-iter-1<\/a> \ud83d\udcd1<\/a>","39":"rwitz\/experiment26-truthy-iter-2<\/a> \ud83d\udcd1<\/a>","40":"chlee10\/T3Q-Merge-Mistral7B<\/a> \ud83d\udcd1<\/a>","41":"LeroyDyer\/Mixtral_AI_Cyber_3.m1<\/a> \ud83d\udcd1<\/a>","42":"yam-peleg\/Experiment31-7B<\/a> \ud83d\udcd1<\/a>","43":"yam-peleg\/Experiment31-7B<\/a> \ud83d\udcd1<\/a>","44":"yam-peleg\/Experiment24-7B<\/a> \ud83d\udcd1<\/a>","45":"zhengr\/MixTAO-7Bx2-MoE-Instruct-v7.0<\/a> \ud83d\udcd1<\/a>","46":"bobofrut\/ladybird-base-7B-v8<\/a> \ud83d\udcd1<\/a>","47":"yam-peleg\/Experiment29-7B<\/a> \ud83d\udcd1<\/a>","48":"yam-peleg\/Experiment30-7B<\/a> \ud83d\udcd1<\/a>","49":"CorticalStack\/pastiche-crown-clown-7b-dare-dpo<\/a> \ud83d\udcd1<\/a>","50":"MaziyarPanahi\/Calme-7B-Instruct-v0.1.1<\/a> \ud83d\udcd1<\/a>","51":"mlabonne\/UltraMerge-7B<\/a> \ud83d\udcd1<\/a>","52":"cloudyu\/Truthful_DPO_cloudyu_Mixtral_34Bx2_MoE_60B<\/a> \ud83d\udcd1<\/a>","53":"yam-peleg\/Experiment27-7B<\/a> \ud83d\udcd1<\/a>","54":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO<\/a> \ud83d\udcd1<\/a>","55":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2<\/a> \ud83d\udcd1<\/a>","56":"cloudyu\/Yi-34Bx2-MoE-60B-DPO<\/a> \ud83d\udcd1<\/a>","57":"chihoonlee10\/T3Q-EN-DPO-Mistral-7B<\/a> \ud83d\udcd1<\/a>","58":"jefferylovely\/AiMaven-Merkaba-7b<\/a> \ud83d\udcd1<\/a>","59":"bardsai\/jaskier-7b-dpo-v5.6<\/a> \ud83d\udcd1<\/a>","60":"JaeyeonKang\/CCK_Asura_v2.1<\/a> \ud83d\udcd1<\/a>","61":"yam-peleg\/Experiment25-7B<\/a> \ud83d\udcd1<\/a>","62":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3<\/a> \ud83d\udcd1<\/a>","63":"CorticalStack\/neurotic-crown-clown-7b-tak-stack-dpo<\/a> \ud83d\udcd1<\/a>","64":"jan-hq\/stealth-v2<\/a> \ud83d\udcd1<\/a>","65":"bardsai\/jaskier-7b-dpo-v6.1<\/a> \ud83d\udcd1<\/a>","66":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v4-test<\/a> \ud83d\udcd1<\/a>","67":"chihoonlee10\/T3Q-DPO-Mistral-7B<\/a> \ud83d\udcd1<\/a>","68":"Kukedlc\/Jupiter-k-7B-slerp<\/a> \ud83d\udcd1<\/a>","69":"moreh\/MoMo-72B-lora-1.8.4-DPO<\/a> \ud83d\udcd1<\/a>","70":"TomGrc\/FusionNet_7Bx2_MoE_v0.1<\/a> \ud83d\udcd1<\/a>","71":"macadeliccc\/MBX-7B-v3-DPO<\/a> \ud83d\udcd1<\/a>","72":"vicgalle\/CarbonBeagle-11B-truthy<\/a> \ud83d\udcd1<\/a>","73":"dddsaty\/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach<\/a> \ud83d\udcd1<\/a>","74":"vicgalle\/RoleBeagle-11B<\/a> \ud83d\udcd1<\/a>","75":"MaziyarPanahi\/Calme-7B-Instruct-v0.5<\/a> \ud83d\udcd1<\/a>","76":"FelixChao\/Capricorn-7B-DPO<\/a> \ud83d\udcd1<\/a>","77":"rwitz\/experiment26-SPIN-iter-0<\/a> \ud83d\udcd1<\/a>","78":"Kukedlc\/NeuralKrishna-7B-V2-DPO<\/a> \ud83d\udcd1<\/a>","79":"abideen\/AlphaMonarch-laser<\/a> \ud83d\udcd1<\/a>","80":"touqir\/Cyrax-7B<\/a> \ud83d\udcd1<\/a>","81":"Eric111\/UltraCatunaMayo-DPO<\/a> \ud83d\udcd1<\/a>","82":"abideen\/AlphaMonarch-daser<\/a> \ud83d\udcd1<\/a>","83":"yam-peleg\/Experiment21-7B<\/a> \ud83d\udcd1<\/a>","84":"TomGrc\/FusionNet_7Bx2_MoE_14B<\/a> \ud83d\udcd1<\/a>","85":"yam-peleg\/Experiment22-7B<\/a> \ud83d\udcd1<\/a>","86":"cloudyu\/Yi-34Bx2-MOE-200K<\/a> \ud83d\udcd1<\/a>","87":"NeverSleep\/MiquMaid-v2-2x70B-DPO<\/a> \ud83d\udcd1<\/a>","88":"abideen\/AlphaMonarch-dora<\/a> \ud83d\udcd1<\/a>","89":"AiMavenAi\/Prometheus-1.3<\/a> \ud83d\udcd1<\/a>","90":"FelixChao\/Capricorn-7B<\/a> \ud83d\udcd1<\/a>","91":"yleo\/EmertonMonarch-7B<\/a> \ud83d\udcd1<\/a>","92":"yam-peleg\/Experiment20-7B<\/a> \ud83d\udcd1<\/a>","93":"ibivibiv\/multimaster-7b-v6<\/a> \ud83d\udcd1<\/a>","94":"abacusai\/Smaug-Mixtral-v0.1<\/a> \ud83d\udcd1<\/a>","95":"daxiongshu\/Pluto_24B_DPO_63<\/a> \ud83d\udcd1<\/a>","96":"abacusai\/Smaug-Mixtral-v0.1<\/a> \ud83d\udcd1<\/a>","97":"cloudyu\/Phoenix_DPO_60B<\/a> \ud83d\udcd1<\/a>","98":"Weyaxi\/Helion-4x34B<\/a> \ud83d\udcd1<\/a>","99":"ibivibiv\/multimaster-7b-v4<\/a> \ud83d\udcd1<\/a>","100":"fblgit\/UNA-34BeagleSimpleMath-32K-v1<\/a> \ud83d\udcd1<\/a>","101":"ShinojiResearch\/Senku-70B-Full<\/a> \ud83d\udcd1<\/a>","102":"fblgit\/UNA-34Beagles-32K-v1<\/a> \ud83d\udcd1<\/a>","103":"one-man-army\/UNA-34Beagles-32K-bf16-v1<\/a> \ud83d\udcd1<\/a>","104":"FelixChao\/Scorpio-7B<\/a> \ud83d\udcd1<\/a>","105":"vicgalle\/ConfigurableBeagle-11B<\/a> \ud83d\udcd1<\/a>","106":"Weyaxi\/Cosmosis-3x34B<\/a> \ud83d\udcd1<\/a>","107":"macadeliccc\/WestLake-7B-v2-laser-truthy-dpo<\/a> \ud83d\udcd1<\/a>","108":"yam-peleg\/Experiment19-7B<\/a> \ud83d\udcd1<\/a>","109":"ShinojiResearch\/Senku-70B-Full<\/a> \ud83d\udcd1<\/a>","110":"yam-peleg\/Experiment23-7B<\/a> \ud83d\udcd1<\/a>","111":"maywell\/kiqu-70b<\/a> \ud83d\udcd1<\/a>","112":"FelixChao\/WestSeverus-7B-DPO-v2<\/a> \ud83d\udcd1<\/a>","113":"migtissera\/Tess-70B-v1.6<\/a> \ud83d\udcd1<\/a>","114":"alnrg2arg\/test3_sft_16bit<\/a> \ud83d\udcd1<\/a>","115":"FelixChao\/Faraday-7B<\/a> \ud83d\udcd1<\/a>","116":"Weyaxi\/Astralis-4x34B<\/a> \ud83d\udcd1<\/a>","117":"FelixChao\/Faraday-7B<\/a> \ud83d\udcd1<\/a>","118":"PetroGPT\/WestSeverus-7B-DPO<\/a> \ud83d\udcd1<\/a>","119":"FelixChao\/Sectumsempra-7B-DPO<\/a> \ud83d\udcd1<\/a>","120":"NeverSleep\/MiquMaid-v1-70B<\/a> \ud83d\udcd1<\/a>","121":"Weyaxi\/Bagel-Hermes-2x34B<\/a> \ud83d\udcd1<\/a>","122":"BarryFutureman\/WestLakeX-7B-EvoMerge-Variant2<\/a> \ud83d\udcd1<\/a>","123":"ibivibiv\/multimaster-7b-v5<\/a> \ud83d\udcd1<\/a>","124":"alnrg2arg\/test3_sft_16bit_dpo2<\/a> \ud83d\udcd1<\/a>","125":"ChaoticNeutrals\/Eris_Floramix_DPO_7B<\/a> \ud83d\udcd1<\/a>","126":"abhishekchohan\/SOLAR-10.7B-Instruct-Forest-DPO-v1<\/a> \ud83d\udcd1<\/a>","127":"abacusai\/MetaMath-Bagel-DPO-34B<\/a> \ud83d\udcd1<\/a>","128":"cognitivecomputations\/WestLake-7B-v2-laser<\/a> \ud83d\udcd1<\/a>","129":"cloudyu\/60B_MoE_Coder_v3<\/a> \ud83d\udcd1<\/a>","130":"ChaoticNeutrals\/Eris_Remix_DPO_7B<\/a> \ud83d\udcd1<\/a>","131":"Eric111\/CatunaLaserPi-DPO<\/a> \ud83d\udcd1<\/a>","132":"jondurbin\/nontoxic-bagel-34b-v0.2<\/a> \ud83d\udcd1<\/a>","133":"jondurbin\/bagel-dpo-34b-v0.2<\/a> \ud83d\udcd1<\/a>","134":"senseable\/WestLake-7B-v2<\/a> \ud83d\udcd1<\/a>","135":"moreh\/MoMo-72B-LoRA-V1.4<\/a> \ud83d\udcd1<\/a>","136":"MaziyarPanahi\/Calme-7B-Instruct-v0.4<\/a> \ud83d\udcd1<\/a>","137":"kevin009\/llamaRAGdrama<\/a> \ud83d\udcd1<\/a>","138":"vicgalle\/Mixtral-7Bx2-truthy<\/a> \ud83d\udcd1<\/a>","139":"moreh\/MoMo-72B-LoRA-V1.4<\/a> \ud83d\udcd1<\/a>","140":"jondurbin\/bagel-dpo-34b-v0.2<\/a> \ud83d\udcd1<\/a>","141":"senseable\/Westlake-7B<\/a> \ud83d\udcd1<\/a>","142":"rizla\/trrapi-16b<\/a> \ud83d\udcd1<\/a>","143":"abacusai\/MM-OV-bagel-DPO-34b-c1000-250<\/a> \ud83d\udcd1<\/a>","144":"BarryFutureman\/WestLakeX-7B-EvoMerge<\/a> \ud83d\udcd1<\/a>","145":"yunconglong\/Truthful_DPO_MOE_19B<\/a> \ud83d\udcd1<\/a>","146":"Kukedlc\/NeuralExperiment-7b-MagicCoder-v7.5<\/a> \ud83d\udcd1<\/a>","147":"ResplendentAI\/Datura_7B<\/a> \ud83d\udcd1<\/a>","148":"FelixChao\/Patronum-7B<\/a> \ud83d\udcd1<\/a>","149":"bhavinjawade\/SOLAR-10B-OrcaDPO-Jawade<\/a> \ud83d\udcd1<\/a>","150":"ResplendentAI\/Flora_DPO_7B<\/a> \ud83d\udcd1<\/a>","151":"ResplendentAI\/Flora_7B<\/a> \ud83d\udcd1<\/a>","152":"NeuralNovel\/Valor-7B-v0.1<\/a> \ud83d\udcd1<\/a>","153":"VAGOsolutions\/SauerkrautLM-SOLAR-Instruct<\/a> \ud83d\udcd1<\/a>","154":"upstage\/SOLAR-10.7B-Instruct-v1.0<\/a> \ud83d\udcd1<\/a>","155":"fblgit\/UNA-SOLAR-10.7B-Instruct-v1.0<\/a> \ud83d\udcd1<\/a>","156":"bhavinjawade\/SOLAR-10B-Nector-DPO-Jawade<\/a> \ud83d\udcd1<\/a>","157":"abacusai\/Liberated-Qwen1.5-72B<\/a> \ud83d\udcd1<\/a>","158":"dddsaty\/SOLAR-Instruct-ko-Adapter-Attach<\/a> \ud83d\udcd1<\/a>","159":"macadeliccc\/SOLAR-10.7b-Instruct-truthy-dpo<\/a> \ud83d\udcd1<\/a>","160":"abacusai\/Liberated-Qwen1.5-72B<\/a> \ud83d\udcd1<\/a>","161":"cloudyu\/19B_MATH_DPO<\/a> \ud83d\udcd1<\/a>","162":"dhanushreddy29\/BrokenKeyboard<\/a> \ud83d\udcd1<\/a>","163":"fblgit\/UNA-SOLAR-10.7B-Instruct-v1.0<\/a> \ud83d\udcd1<\/a>","164":"fblgit\/UNA-POLAR-10.7B-InstructMath-v2<\/a> \ud83d\udcd1<\/a>","165":"fblgit\/UNAversal-2x7B-v1<\/a> \ud83d\udcd1<\/a>","166":"dddsaty\/Merge_Sakura_Solar<\/a> \ud83d\udcd1<\/a>","167":"rishiraj\/meow<\/a> \ud83d\udcd1<\/a>","168":"vicgalle\/ConfigurableSOLAR-10.7B<\/a> \ud83d\udcd1<\/a>","169":"fblgit\/UNA-TheBeagle-7b-v1<\/a> \ud83d\udcd1<\/a>","170":"vicgalle\/OpenBeagle-11B<\/a> \ud83d\udcd1<\/a>","171":"InferenceIllusionist\/Excalibur-7b-DPO<\/a> \ud83d\udcd1<\/a>","172":"JaeyeonKang\/CCK_Gony_v3<\/a> \ud83d\udcd1<\/a>","173":"Sao10K\/Typhon-Mixtral-v1<\/a> \ud83d\udcd1<\/a>","174":"fblgit\/UNAversal-8x7B-v1beta<\/a> \ud83d\udcd1<\/a>","175":"sophosympatheia\/Aurora-Nights-70B-v1.0<\/a> \ud83d\udcd1<\/a>","176":"allenai\/tulu-2-dpo-70b<\/a> \ud83d\udcd1<\/a>","177":"Steelskull\/Lumosia-v2-MoE-4x10.7<\/a> \ud83d\udcd1<\/a>","178":"NousResearch\/Nous-Hermes-2-Yi-34B<\/a> \ud83d\udcd1<\/a>","179":"decruz07\/kellemar-DPO-Orca-Distilled-7B-SLERP<\/a> \ud83d\udcd1<\/a>","180":"abacusai\/MM-Orc-Vic-bagel-34b-c1000<\/a> \ud83d\udcd1<\/a>","181":"JaeyeonKang\/CCK_Asura_v2<\/a> \ud83d\udcd1<\/a>","182":"Qwen\/Qwen-72B<\/a> \ud83d\udcd1<\/a>","183":"yam-peleg\/Experiment7-7B<\/a> \ud83d\udcd1<\/a>","184":"macadeliccc\/SOLAR-10.7b-Instruct-dpo<\/a> \ud83d\udcd1<\/a>","185":"yam-peleg\/Experiment15-7B<\/a> \ud83d\udcd1<\/a>","186":"yam-peleg\/Experiment10-7B<\/a> \ud83d\udcd1<\/a>","187":"yam-peleg\/Experiment8-7B<\/a> \ud83d\udcd1<\/a>","188":"cloudyu\/Mixtral-8x7B-Instruct-v0.1-DPO<\/a> \ud83d\udcd1<\/a>","189":"Neuronovo\/neuronovo-9B-v0.4<\/a> \ud83d\udcd1<\/a>","190":"cloudyu\/Mixtral_7Bx5_MoE_30B<\/a> \ud83d\udcd1<\/a>","191":"yam-peleg\/Experiment9-7B<\/a> \ud83d\udcd1<\/a>","192":"yam-peleg\/Experiment1-7B<\/a> \ud83d\udcd1<\/a>","193":"yam-peleg\/Experiment2-7B<\/a> \ud83d\udcd1<\/a>","194":"yam-peleg\/Experiment4-7B<\/a> \ud83d\udcd1<\/a>","195":"Sao10K\/Franziska-Mixtral-v1<\/a> \ud83d\udcd1<\/a>","196":"NousResearch\/Nous-Hermes-2-Mixtral-8x7B-DPO<\/a> \ud83d\udcd1<\/a>","197":"ResplendentAI\/DaturaCookie_7B<\/a> \ud83d\udcd1<\/a>","198":"ibndias\/Nous-Hermes-2-MoE-2x34B<\/a> \ud83d\udcd1<\/a>","199":"gradientai\/v-alpha-tross<\/a> \ud83d\udcd1<\/a>","200":"carsenk\/flippa-exp26-v3-7b<\/a> \ud83d\udcd1<\/a>","201":"SUSTech\/SUS-Chat-34B<\/a> \ud83d\udcd1<\/a>","202":"Sao10K\/SOLAR-10.7B-NahIdWin<\/a> \ud83d\udcd1<\/a>","203":"yunconglong\/7Bx4_DPO<\/a> \ud83d\udcd1<\/a>","204":"gradientai\/v-alpha-tross<\/a> \ud83d\udcd1<\/a>","205":"ResplendentAI\/Luna-2x7B-MoE<\/a> \ud83d\udcd1<\/a>","206":"NousResearch\/Nous-Hermes-2-Mixtral-8x7B-DPO<\/a> \ud83d\udcd1<\/a>","207":"ibivibiv\/multimaster-7b-v3<\/a> \ud83d\udcd1<\/a>","208":"yunconglong\/7Bx4_DPO_2e<\/a> \ud83d\udcd1<\/a>","209":"argilla\/notux-8x7b-v1<\/a> \ud83d\udcd1<\/a>","210":"The-Face-Of-Goonery\/HuginnV5.5-12.6B<\/a> \ud83d\udcd1<\/a>","211":"Qwen\/Qwen1.5-72B<\/a> \ud83d\udcd1<\/a>","212":"logicker\/SkkuDS-DPO-72B-v1<\/a> \ud83d\udcd1<\/a>","213":"VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct<\/a> \ud83d\udcd1<\/a>","214":"Himitsui\/Kaiju-11B<\/a> \ud83d\udcd1<\/a>","215":"PetroGPT\/Severus-7B-DPO<\/a> \ud83d\udcd1<\/a>","216":"S-miguel\/The-Trinity-Coder-7B<\/a> \ud83d\udcd1<\/a>","217":"logicker\/SkkuDS-DPO-72B-v3<\/a> \ud83d\udcd1<\/a>","218":"PSanni\/MPOMixtral-8x7B-Instruct-v0.1<\/a> \ud83d\udcd1<\/a>","219":"cloudyu\/19B_TRUTH_DPO<\/a> \ud83d\udcd1<\/a>","220":"JaeyeonKang\/CCK_Gony_v3.3<\/a> \ud83d\udcd1<\/a>","221":"VAGOsolutions\/SauerkrautLM-Mixtral-8x7B-Instruct<\/a> \ud83d\udcd1<\/a>","222":"tenyx\/TenyxChat-8x7B-v1<\/a> \ud83d\udcd1<\/a>","223":"mistralai\/Mixtral-8x7B-Instruct-v0.1<\/a> \ud83d\udcd1<\/a>","224":"ChaoticNeutrals\/RPMix-4x7B-MoE<\/a> \ud83d\udcd1<\/a>","225":"SJ-Donald\/SJ-SOLAR-10.7b-DPO<\/a> \ud83d\udcd1<\/a>","226":"senseable\/garten2-7b<\/a> \ud83d\udcd1<\/a>","227":"Azure99\/blossom-v5-34b<\/a> \ud83d\udcd1<\/a>","228":"Sao10K\/Fimbulvetr-11B-v2<\/a> \ud83d\udcd1<\/a>","229":"mistralai\/Mixtral-8x7B-Instruct-v0.1<\/a> \ud83d\udcd1<\/a>","230":"FelixChao\/Severus-7B<\/a> \ud83d\udcd1<\/a>","231":"Himitsui\/KuroMitsu-11B<\/a> \ud83d\udcd1<\/a>","232":"maywell\/PiVoT-SUS-RP<\/a> \ud83d\udcd1<\/a>","233":"jondurbin\/bagel-dpo-8x7b-v0.2<\/a> \ud83d\udcd1<\/a>","234":"ignos\/Mistral-T5-7B-v1<\/a> \ud83d\udcd1<\/a>","235":"SanjiWatsuki\/Kunoichi-DPO-v2-7B<\/a> \ud83d\udcd1<\/a>","236":"Brillibits\/Instruct_Mixtral-8x7B-v0.1_Dolly15K<\/a> \ud83d\udcd1<\/a>","237":"Sao10K\/Fimbulvetr-11B-v2<\/a> \ud83d\udcd1<\/a>","238":"SanjiWatsuki\/Kunoichi-DPO-v2-7B<\/a> \ud83d\udcd1<\/a>","239":"cognitivecomputations\/mixtral-instruct-0.1-laser<\/a> \ud83d\udcd1<\/a>","240":"cognitivecomputations\/laserxtral<\/a> \ud83d\udcd1<\/a>","241":"OpenBuddy\/openbuddy-deepseek-67b-v15.2<\/a> \ud83d\udcd1<\/a>","242":"macadeliccc\/piccolo-math-2x7b<\/a> \ud83d\udcd1<\/a>","243":"JaeyeonKang\/CCK_Gony_v0.1<\/a> \ud83d\udcd1<\/a>","244":"R136a1\/InfinityKuno-2x7B<\/a> \ud83d\udcd1<\/a>","245":"jan-ai\/Solar-10.7B-SLERP<\/a> \ud83d\udcd1<\/a>","246":"mncai\/yi-34B-v3<\/a> \ud83d\udcd1<\/a>","247":"NeverSleep\/CausalLM-RP-34B<\/a> \ud83d\udcd1<\/a>","248":"Sao10K\/Fimbulvetr-10.7B-v1<\/a> \ud83d\udcd1<\/a>","249":"SanjiWatsuki\/Kunoichi-DPO-7B<\/a> \ud83d\udcd1<\/a>","250":"jan-hq\/supermario-slerp-v3<\/a> \ud83d\udcd1<\/a>","251":"viethq188\/LeoScorpius-7B<\/a> \ud83d\udcd1<\/a>","252":"JaeyeonKang\/CCK_Gony_v3.1<\/a> \ud83d\udcd1<\/a>","253":"adonlee\/Mistral_7B_SFT_DPO_v0<\/a> \ud83d\udcd1<\/a>","254":"mncai\/yi-34B-v2<\/a> \ud83d\udcd1<\/a>","255":"NousResearch\/Nous-Hermes-2-Mixtral-8x7B-SFT<\/a> \ud83d\udcd1<\/a>","256":"CausalLM\/72B-preview-llamafied-qwen-llamafy<\/a> \ud83d\udcd1<\/a>","257":"OpenPipe\/mistral-ft-optimized-1218<\/a> \ud83d\udcd1<\/a>","258":"bn22\/Nous-Hermes-2-SOLAR-10.7B-MISALIGNED<\/a> \ud83d\udcd1<\/a>","259":"arlineka\/Brunhilde-2x7b-MOE-DPO-v.01.5<\/a> \ud83d\udcd1<\/a>","260":"OpenBuddy\/openbuddy-deepseek-67b-v18.1-4k<\/a> \ud83d\udcd1<\/a>","261":"deepseek-ai\/deepseek-llm-67b-chat<\/a> \ud83d\udcd1<\/a>","262":"mlabonne\/NeuralDarewin-7B<\/a> \ud83d\udcd1<\/a>","263":"OpenBuddy\/openbuddy-deepseek-67b-v15.1<\/a> \ud83d\udcd1<\/a>","264":"migtissera\/Tess-M-Creative-v1.0<\/a> \ud83d\udcd1<\/a>","265":"VitalContribution\/Evangelion-7B<\/a> \ud83d\udcd1<\/a>","266":"bhenrym14\/platypus-yi-34b<\/a> \ud83d\udcd1<\/a>","267":"RatanRohith\/NeuralPizza-7B-V0.3<\/a> \ud83d\udcd1<\/a>","268":"PracticeLLM\/SOLAR-tail-10.7B-Merge-v1.0<\/a> \ud83d\udcd1<\/a>","269":"Azure99\/blossom-v4-yi-34b<\/a> \ud83d\udcd1<\/a>","270":"llmixer\/BigWeave-v15-103b<\/a> \ud83d\udcd1<\/a>","271":"MaziyarPanahi\/Calme-7B-Instruct-v0.1<\/a> \ud83d\udcd1<\/a>","272":"Samee-ur\/NeuralPipe-7B-slerp-DPO<\/a> \ud83d\udcd1<\/a>","273":"VAGOsolutions\/SauerkrautLM-14b-MoE-LaserChat<\/a> \ud83d\udcd1<\/a>","274":"RatanRohith\/NeuralPizza-7B-V0.2<\/a> \ud83d\udcd1<\/a>","275":"RatanRohith\/NeuralPizza-7B-V0.1<\/a> \ud83d\udcd1<\/a>","276":"R136a1\/InfinityKumon-2x7B<\/a> \ud83d\udcd1<\/a>","277":"deepseek-ai\/deepseek-llm-67b-chat<\/a> \ud83d\udcd1<\/a>","278":"Sao10K\/14B-Glacier-Stack<\/a> \ud83d\udcd1<\/a>","279":"jan-hq\/supermario-slerp-v2<\/a> \ud83d\udcd1<\/a>","280":"dillfrescott\/amadeus-v0.1<\/a> \ud83d\udcd1<\/a>","281":"OpenBuddy\/openbuddy-deepseek-67b-v15.3-4k<\/a> \ud83d\udcd1<\/a>","282":"KnutJaegersberg\/Deita-20b<\/a> \ud83d\udcd1<\/a>","283":"LDCC\/LDCC-SOLAR-10.7B<\/a> \ud83d\udcd1<\/a>","284":"LDCC\/LDCC-SOLAR-10.7B<\/a> \ud83d\udcd1<\/a>"},"Average \u2b06\ufe0f":{"0":81.22,"1":81.0,"2":80.98,"3":80.81,"4":80.79,"5":80.72,"6":80.48,"7":79.3,"8":78.55,"9":77.91,"10":77.74,"11":77.52,"12":77.5,"13":77.44,"14":77.43,"15":77.41,"16":77.38,"17":77.3,"18":77.29,"19":77.29,"20":77.28,"21":77.22,"22":77.19,"23":77.1,"24":77.08,"25":77.07,"26":77.06,"27":77.05,"28":77.03,"29":76.95,"30":76.74,"31":76.74,"32":76.7,"33":76.67,"34":76.65,"35":76.62,"36":76.62,"37":76.61,"38":76.6,"39":76.6,"40":76.59,"41":76.59,"42":76.58,"43":76.57,"44":76.56,"45":76.55,"46":76.55,"47":76.53,"48":76.53,"49":76.5,"50":76.49,"51":76.49,"52":76.48,"53":76.47,"54":76.45,"55":76.44,"56":76.44,"57":76.43,"58":76.42,"59":76.41,"60":76.41,"61":76.4,"62":76.4,"63":76.38,"64":76.37,"65":76.36,"66":76.34,"67":76.34,"68":76.29,"69":76.23,"70":76.16,"71":76.13,"72":76.1,"73":76.09,"74":76.06,"75":76.05,"76":76.04,"77":76.04,"78":76.0,"79":76.0,"80":75.98,"81":75.96,"82":75.94,"83":75.93,"84":75.91,"85":75.9,"86":75.89,"87":75.89,"88":75.86,"89":75.81,"90":75.76,"91":75.74,"92":75.71,"93":75.66,"94":75.64,"95":75.63,"96":75.49,"97":75.48,"98":75.48,"99":75.47,"100":75.45,"101":75.44,"102":75.41,"103":75.41,"104":75.4,"105":75.4,"106":75.39,"107":75.37,"108":75.36,"109":75.36,"110":75.31,"111":75.29,"112":75.29,"113":75.29,"114":75.28,"115":75.25,"116":75.24,"117":75.22,"118":75.17,"119":75.14,"120":75.12,"121":75.1,"122":75.04,"123":75.01,"124":74.98,"125":74.87,"126":74.8,"127":74.8,"128":74.78,"129":74.75,"130":74.71,"131":74.7,"132":74.69,"133":74.69,"134":74.68,"135":74.67,"136":74.65,"137":74.65,"138":74.64,"139":74.64,"140":74.5,"141":74.48,"142":74.48,"143":74.47,"144":74.37,"145":74.3,"146":74.28,"147":74.28,"148":74.27,"149":74.27,"150":74.26,"151":74.26,"152":74.21,"153":74.21,"154":74.2,"155":74.2,"156":74.19,"157":74.13,"158":74.11,"159":74.11,"160":74.11,"161":74.1,"162":74.08,"163":74.07,"164":74.07,"165":74.05,"166":74.03,"167":73.94,"168":73.94,"169":73.87,"170":73.85,"171":73.84,"172":73.83,"173":73.81,"174":73.78,"175":73.77,"176":73.77,"177":73.75,"178":73.74,"179":73.71,"180":73.68,"181":73.62,"182":73.6,"183":73.55,"184":73.54,"185":73.48,"186":73.47,"187":73.47,"188":73.44,"189":73.42,"190":73.39,"191":73.39,"192":73.39,"193":73.38,"194":73.38,"195":73.36,"196":73.35,"197":73.35,"198":73.3,"199":73.28,"200":73.25,"201":73.22,"202":73.21,"203":73.2,"204":73.16,"205":73.13,"206":73.12,"207":73.07,"208":72.99,"209":72.97,"210":72.93,"211":72.91,"212":72.89,"213":72.89,"214":72.82,"215":72.81,"216":72.81,"217":72.8,"218":72.8,"219":72.8,"220":72.76,"221":72.73,"222":72.72,"223":72.7,"224":72.68,"225":72.67,"226":72.65,"227":72.65,"228":72.63,"229":72.62,"230":72.58,"231":72.58,"232":72.57,"233":72.49,"234":72.47,"235":72.46,"236":72.44,"237":72.4,"238":72.4,"239":72.36,"240":72.34,"241":72.33,"242":72.32,"243":72.32,"244":72.32,"245":72.31,"246":72.26,"247":72.26,"248":72.25,"249":72.24,"250":72.22,"251":72.21,"252":72.2,"253":72.17,"254":72.12,"255":72.07,"256":72.0,"257":71.94,"258":71.83,"259":71.81,"260":71.8,"261":71.79,"262":71.79,"263":71.76,"264":71.73,"265":71.71,"266":71.69,"267":71.68,"268":71.68,"269":71.67,"270":71.67,"271":71.63,"272":71.6,"273":71.6,"274":71.59,"275":71.53,"276":71.52,"277":71.52,"278":71.47,"279":71.45,"280":71.42,"281":71.42,"282":71.4,"283":71.4,"284":71.4},"ARC":{"0":79.78,"1":78.67,"2":78.58,"3":76.79,"4":76.19,"5":76.19,"6":76.02,"7":73.89,"8":70.82,"9":74.06,"10":77.47,"11":74.06,"12":73.81,"13":74.91,"14":73.89,"15":74.57,"16":73.72,"17":71.25,"18":70.14,"19":74.23,"20":72.87,"21":73.63,"22":74.49,"23":74.32,"24":74.66,"25":72.95,"26":73.63,"27":74.32,"28":72.95,"29":73.21,"30":73.38,"31":72.87,"32":72.95,"33":73.12,"34":73.29,"35":73.38,"36":73.04,"37":73.12,"38":73.21,"39":73.38,"40":72.95,"41":74.06,"42":73.55,"43":73.55,"44":73.81,"45":74.23,"46":73.21,"47":73.12,"48":73.46,"49":72.78,"50":72.95,"51":73.04,"52":71.25,"53":73.55,"54":73.12,"55":73.12,"56":71.25,"57":73.04,"58":73.21,"59":73.04,"60":72.53,"61":73.21,"62":73.04,"63":72.44,"64":73.89,"65":73.29,"66":73.12,"67":72.78,"68":74.23,"69":69.62,"70":74.06,"71":73.55,"72":72.27,"73":73.89,"74":72.35,"75":72.87,"76":72.87,"77":72.44,"78":74.06,"79":73.12,"80":72.95,"81":72.87,"82":73.04,"83":71.42,"84":73.55,"85":71.5,"86":70.48,"87":72.53,"88":73.21,"89":72.61,"90":72.44,"91":72.7,"92":73.04,"93":72.78,"94":74.91,"95":73.98,"96":74.66,"97":71.16,"98":69.71,"99":72.53,"100":74.15,"101":71.5,"102":73.55,"103":73.55,"104":71.33,"105":72.53,"106":69.71,"107":73.89,"108":72.35,"109":71.33,"110":72.35,"111":72.1,"112":71.42,"113":71.33,"114":73.55,"115":72.27,"116":69.71,"117":72.44,"118":70.73,"119":71.5,"120":71.67,"121":69.8,"122":72.53,"123":72.18,"124":73.63,"125":73.04,"126":71.93,"127":68.17,"128":73.29,"129":71.16,"130":72.44,"131":72.95,"132":72.44,"133":71.93,"134":73.04,"135":69.2,"136":70.73,"137":72.01,"138":72.18,"139":69.11,"140":72.01,"141":73.21,"142":72.1,"143":68.17,"144":71.42,"145":71.08,"146":71.33,"147":72.1,"148":71.67,"149":71.16,"150":71.76,"151":72.1,"152":72.27,"153":70.82,"154":71.08,"155":70.56,"156":71.33,"157":65.7,"158":71.08,"159":72.1,"160":65.7,"161":71.08,"162":71.25,"163":70.73,"164":70.73,"165":73.38,"166":70.73,"167":70.48,"168":70.39,"169":73.04,"170":70.48,"171":70.9,"172":71.33,"173":71.84,"174":69.8,"175":71.33,"176":72.1,"177":70.39,"178":66.89,"179":70.48,"180":67.32,"181":70.82,"182":65.19,"183":71.84,"184":71.76,"185":72.18,"186":72.18,"187":72.1,"188":69.8,"189":72.44,"190":69.97,"191":72.01,"192":72.53,"193":72.18,"194":72.18,"195":71.76,"196":71.08,"197":71.25,"198":66.64,"199":71.93,"200":68.09,"201":66.3,"202":64.51,"203":69.37,"204":71.84,"205":71.16,"206":71.42,"207":70.39,"208":68.94,"209":70.65,"210":72.01,"211":65.87,"212":65.96,"213":70.48,"214":69.97,"215":70.22,"216":69.37,"217":66.04,"218":70.99,"219":71.67,"220":70.39,"221":70.56,"222":69.71,"223":70.14,"224":71.08,"225":68.26,"226":69.37,"227":66.98,"228":70.14,"229":70.22,"230":68.43,"231":70.31,"232":66.55,"233":72.1,"234":68.6,"235":69.62,"236":69.28,"237":70.14,"238":69.37,"239":70.48,"240":69.03,"241":68.6,"242":69.11,"243":70.05,"244":69.62,"245":70.73,"246":67.06,"247":68.0,"248":68.94,"249":69.62,"250":69.28,"251":69.28,"252":69.62,"253":66.3,"254":66.13,"255":69.71,"256":65.19,"257":67.92,"258":68.26,"259":69.54,"260":67.75,"261":67.75,"262":70.14,"263":67.66,"264":66.81,"265":68.94,"266":68.43,"267":71.08,"268":66.13,"269":66.81,"270":69.71,"271":67.24,"272":69.28,"273":66.72,"274":68.77,"275":70.48,"276":69.62,"277":67.75,"278":71.67,"279":69.71,"280":68.94,"281":67.58,"282":63.91,"283":67.32,"284":67.58},"HellaSwag":{"0":91.15,"1":89.77,"2":89.74,"3":89.02,"4":89.44,"5":89.46,"6":89.27,"7":88.16,"8":85.96,"9":86.74,"10":91.88,"11":86.67,"12":89.22,"13":89.3,"14":89.07,"15":86.74,"16":86.46,"17":85.53,"18":86.03,"19":86.76,"20":86.52,"21":89.04,"22":86.76,"23":89.5,"24":89.51,"25":86.22,"26":86.59,"27":89.39,"28":88.86,"29":86.11,"30":89.15,"31":89.2,"32":89.23,"33":89.12,"34":89.11,"35":89.13,"36":89.04,"37":89.19,"38":89.13,"39":89.11,"40":89.15,"41":88.96,"42":89.19,"43":89.14,"44":89.06,"45":89.37,"46":89.19,"47":89.06,"48":89.09,"49":89.15,"50":89.26,"51":89.25,"52":85.24,"53":89.13,"54":89.09,"55":89.07,"56":85.1,"57":89.3,"58":89.03,"59":89.0,"60":88.75,"61":89.01,"62":89.11,"63":88.73,"64":89.26,"65":88.89,"66":89.09,"67":89.29,"68":88.82,"69":85.35,"70":88.9,"71":89.11,"72":89.31,"73":88.94,"74":89.77,"75":88.77,"76":88.47,"77":88.74,"78":88.97,"79":89.21,"80":88.19,"81":88.75,"82":89.23,"83":89.03,"84":88.84,"85":88.89,"86":84.63,"87":88.36,"88":89.26,"89":89.02,"90":88.41,"91":89.16,"92":88.62,"93":88.77,"94":87.79,"95":88.17,"96":87.72,"97":85.46,"98":85.28,"99":88.77,"100":85.98,"101":87.88,"102":85.93,"103":85.93,"104":88.5,"105":88.85,"106":85.18,"107":88.85,"108":88.61,"109":87.86,"110":88.77,"111":87.94,"112":88.27,"113":87.06,"114":88.87,"115":88.9,"116":85.17,"117":88.91,"118":88.01,"119":88.7,"120":87.96,"121":85.26,"122":88.52,"123":88.42,"124":89.03,"125":88.28,"126":88.44,"127":84.23,"128":88.66,"129":85.44,"130":88.03,"131":88.33,"132":85.64,"133":85.25,"134":88.65,"135":85.07,"136":87.75,"137":88.83,"138":87.88,"139":85.0,"140":85.24,"141":88.49,"142":88.88,"143":83.97,"144":88.08,"145":88.46,"146":87.94,"147":88.27,"148":88.33,"149":88.27,"150":88.28,"151":88.31,"152":86.59,"153":88.63,"154":88.16,"155":88.18,"156":88.62,"157":84.62,"158":88.2,"159":88.44,"160":84.58,"161":88.43,"162":88.34,"163":88.32,"164":88.2,"165":87.87,"166":88.51,"167":88.08,"168":88.03,"169":88.0,"170":88.76,"171":87.93,"172":88.71,"173":87.47,"174":86.9,"175":88.33,"176":88.99,"177":87.87,"178":85.49,"179":87.56,"180":83.52,"181":88.09,"182":85.94,"183":88.04,"184":88.08,"185":88.68,"186":87.96,"187":88.13,"188":87.83,"189":88.33,"190":86.82,"191":88.06,"192":88.17,"193":88.15,"194":88.09,"195":87.37,"196":87.29,"197":88.0,"198":85.73,"199":86.82,"200":86.5,"201":83.91,"202":85.67,"203":86.89,"204":86.84,"205":88.12,"206":87.21,"207":87.65,"208":86.8,"209":87.72,"210":86.7,"211":85.99,"212":86.0,"213":87.75,"214":87.72,"215":87.09,"216":86.17,"217":86.11,"218":87.95,"219":88.63,"220":87.88,"221":87.74,"222":87.76,"223":87.55,"224":87.79,"225":86.95,"226":87.54,"227":84.79,"228":87.79,"229":87.63,"230":86.89,"231":88.07,"232":84.23,"233":86.41,"234":86.3,"235":87.44,"236":87.59,"237":87.77,"238":87.42,"239":87.28,"240":86.76,"241":86.37,"242":87.27,"243":87.27,"244":87.44,"245":87.87,"246":85.11,"247":83.43,"248":87.27,"249":87.14,"250":86.71,"251":87.01,"252":87.45,"253":84.9,"254":85.0,"255":86.74,"256":83.24,"257":86.26,"258":86.11,"259":87.02,"260":84.65,"261":86.82,"262":86.4,"263":86.49,"264":85.14,"265":86.45,"266":85.21,"267":87.38,"268":86.54,"269":84.44,"270":86.41,"271":85.57,"272":86.34,"273":84.88,"274":86.11,"275":87.3,"276":87.09,"277":86.8,"278":88.35,"279":86.54,"280":86.98,"281":85.15,"282":83.11,"283":88.11,"284":88.11},"MMLU":{"0":77.95,"1":78.22,"2":78.27,"3":77.2,"4":77.07,"5":77.17,"6":77.15,"7":77.4,"8":77.13,"9":76.65,"10":68.1,"11":76.69,"12":64.92,"13":64.67,"14":75.44,"15":76.68,"16":76.72,"17":76.63,"18":77.4,"19":76.66,"20":76.96,"21":75.99,"22":76.55,"23":64.47,"24":64.53,"25":77.05,"26":76.55,"27":64.48,"28":75.41,"29":75.44,"30":64.32,"31":64.4,"32":64.42,"33":64.3,"34":64.35,"35":64.28,"36":64.44,"37":64.36,"38":64.34,"39":64.36,"40":64.44,"41":64.45,"42":64.36,"43":64.29,"44":64.34,"45":64.54,"46":64.39,"47":64.49,"48":64.4,"49":64.51,"50":64.32,"51":64.4,"52":77.28,"53":64.45,"54":64.8,"55":64.8,"56":77.36,"57":64.13,"58":64.53,"59":64.38,"60":74.96,"61":64.45,"62":64.79,"63":64.56,"64":64.94,"65":64.39,"66":64.79,"67":64.25,"68":65.01,"69":77.33,"70":65.0,"71":64.91,"72":66.55,"73":65.03,"74":66.35,"75":64.69,"76":64.29,"77":64.64,"78":64.41,"79":64.43,"80":64.6,"81":65.18,"82":64.43,"83":63.92,"84":64.68,"85":64.13,"86":76.64,"87":75.31,"88":64.47,"89":64.26,"90":64.9,"91":64.05,"92":63.23,"93":64.74,"94":70.08,"95":64.49,"96":70.06,"97":77.66,"98":77.33,"99":64.85,"100":76.52,"101":75.2,"102":76.45,"103":76.45,"104":64.7,"105":66.71,"106":77.25,"107":64.84,"108":63.08,"109":75.14,"110":64.17,"111":74.93,"112":64.79,"113":74.76,"114":64.63,"115":64.69,"116":77.24,"117":64.68,"118":64.93,"119":64.9,"120":74.9,"121":77.24,"122":64.77,"123":65.06,"124":64.63,"125":64.71,"126":65.63,"127":76.54,"128":64.72,"129":75.37,"130":65.29,"131":64.95,"132":76.41,"133":76.58,"134":64.71,"135":77.12,"136":64.4,"137":64.5,"138":65.2,"139":77.26,"140":76.58,"141":64.64,"142":64.26,"143":76.33,"144":64.84,"145":66.13,"146":64.62,"147":64.15,"148":64.84,"149":66.12,"150":64.13,"151":64.16,"152":64.09,"153":66.2,"154":66.21,"155":66.08,"156":66.22,"157":77.13,"158":66.09,"159":65.45,"160":77.08,"161":66.25,"162":66.04,"163":66.1,"164":66.03,"165":63.49,"166":66.03,"167":66.25,"168":66.44,"169":63.48,"170":66.94,"171":65.46,"172":71.07,"173":71.11,"174":70.39,"175":70.47,"176":69.84,"177":66.45,"178":76.7,"179":65.33,"180":76.09,"181":74.72,"182":77.37,"183":65.25,"184":66.06,"185":60.01,"186":65.32,"187":65.25,"188":71.05,"189":65.24,"190":64.42,"191":65.32,"192":65.28,"193":65.1,"194":65.03,"195":69.78,"196":72.17,"197":64.28,"198":76.49,"199":70.38,"200":64.42,"201":76.41,"202":64.17,"203":64.73,"204":70.44,"205":64.41,"206":72.28,"207":65.07,"208":64.5,"209":71.39,"210":64.5,"211":77.2,"212":77.33,"213":71.37,"214":66.79,"215":64.93,"216":64.9,"217":77.34,"218":70.26,"219":65.78,"220":71.43,"221":71.08,"222":71.12,"223":71.4,"224":64.36,"225":66.73,"226":65.44,"227":76.0,"228":66.83,"229":71.16,"230":65.2,"231":66.66,"232":76.23,"233":70.27,"234":64.62,"235":64.94,"236":70.96,"237":66.68,"238":64.83,"239":71.07,"240":64.68,"241":71.5,"242":63.69,"243":71.21,"244":64.49,"245":65.77,"246":75.8,"247":83.1,"248":66.59,"249":64.79,"250":65.11,"251":65.04,"252":71.2,"253":64.53,"254":75.64,"255":72.21,"256":77.04,"257":64.99,"258":66.26,"259":64.93,"260":70.58,"261":72.42,"262":64.85,"263":70.3,"264":75.54,"265":63.97,"266":78.13,"267":64.29,"268":66.52,"269":74.34,"270":71.25,"271":64.97,"272":63.7,"273":65.17,"274":64.32,"275":64.42,"276":64.97,"277":72.19,"278":66.73,"279":64.82,"280":64.69,"281":70.38,"282":67.4,"283":66.83,"284":66.63},"TruthfulQA":{"0":74.5,"1":75.18,"2":75.09,"3":79.02,"4":77.82,"5":77.78,"6":76.67,"7":72.69,"8":74.71,"9":72.24,"10":79.17,"11":71.32,"12":78.57,"13":78.02,"14":71.75,"15":70.17,"16":71.01,"17":71.99,"18":69.0,"19":70.22,"20":73.28,"21":70.19,"22":70.21,"23":78.66,"24":78.63,"25":71.31,"26":69.68,"27":78.47,"28":69.1,"29":72.78,"30":78.24,"31":77.92,"32":78.41,"33":78.04,"34":77.86,"35":77.98,"36":78.49,"37":78.0,"38":77.66,"39":77.3,"40":77.96,"41":77.67,"42":78.31,"43":78.43,"44":78.54,"45":74.26,"46":76.82,"47":78.72,"48":77.76,"49":78.8,"50":78.1,"51":78.17,"52":66.74,"53":78.7,"54":77.45,"55":77.46,"56":66.24,"57":78.71,"58":78.3,"59":77.81,"60":67.33,"61":78.49,"62":77.48,"63":78.37,"64":72.47,"65":77.47,"66":77.52,"67":78.57,"68":73.96,"69":64.64,"70":71.2,"71":74.0,"72":78.55,"73":71.24,"74":77.92,"75":73.68,"76":77.23,"77":74.9,"78":76.19,"79":77.9,"80":77.01,"81":76.44,"82":78.01,"83":79.79,"84":69.6,"85":79.47,"86":68.19,"87":66.5,"88":78.02,"89":79.29,"90":73.76,"91":78.09,"92":77.72,"93":70.89,"94":66.88,"95":79.36,"96":66.95,"97":63.84,"98":63.91,"99":70.74,"100":73.74,"101":61.96,"102":73.55,"103":73.55,"104":72.51,"105":77.13,"106":63.82,"107":69.81,"108":78.18,"109":61.95,"110":78.87,"111":63.48,"112":72.37,"113":63.8,"114":69.77,"115":73.07,"116":63.55,"117":73.03,"118":70.53,"119":72.49,"120":61.79,"121":64.82,"122":70.35,"123":70.37,"124":70.71,"125":70.94,"126":76.13,"127":65.44,"128":67.04,"129":67.01,"130":68.92,"131":70.01,"132":72.7,"133":70.05,"134":67.06,"135":62.66,"136":70.25,"137":70.24,"138":74.68,"139":62.71,"140":70.16,"141":67.36,"142":74.13,"143":63.67,"144":67.5,"145":72.29,"146":72.11,"147":71.03,"148":70.41,"149":71.57,"150":71.08,"151":71.19,"152":69.84,"153":71.95,"154":71.43,"155":72.05,"156":70.92,"157":60.64,"158":71.51,"159":76.75,"160":60.56,"161":72.11,"162":71.36,"163":72.52,"164":71.73,"165":69.93,"166":72.21,"167":70.49,"168":72.34,"169":69.85,"170":67.01,"171":70.82,"172":73.33,"173":68.81,"174":71.97,"175":62.81,"176":65.78,"177":68.48,"178":60.37,"179":64.97,"180":60.57,"181":56.97,"182":60.19,"183":70.59,"184":71.98,"185":77.05,"186":71.1,"187":70.25,"188":69.18,"189":71.07,"190":65.97,"191":70.42,"192":69.98,"193":69.97,"194":70.39,"195":70.07,"196":54.83,"197":68.48,"198":58.08,"199":65.21,"200":67.35,"201":57.04,"202":76.73,"203":65.66,"204":65.22,"205":68.66,"206":54.53,"207":59.7,"208":65.6,"209":66.21,"210":70.45,"211":59.61,"212":59.54,"213":65.71,"214":62.15,"215":64.41,"216":61.25,"217":59.73,"218":66.52,"219":72.23,"220":67.41,"221":65.72,"222":65.42,"223":64.98,"224":67.29,"225":67.74,"226":59.5,"227":62.68,"228":63.43,"229":64.58,"230":61.36,"231":61.36,"232":54.57,"233":72.83,"234":61.86,"235":66.06,"236":64.83,"237":63.42,"238":66.0,"239":65.83,"240":63.8,"241":56.2,"242":63.86,"243":63.23,"244":63.28,"245":65.72,"246":57.54,"247":54.51,"248":60.54,"249":67.31,"250":61.77,"251":63.95,"252":64.17,"253":69.72,"254":57.34,"255":51.22,"256":52.55,"257":59.48,"258":57.79,"259":65.47,"260":55.66,"261":55.85,"262":62.92,"263":54.42,"264":57.68,"265":64.01,"266":54.48,"267":67.93,"268":60.57,"269":57.89,"270":66.1,"271":59.38,"272":63.53,"273":57.64,"274":61.38,"275":67.22,"276":61.99,"277":55.83,"278":65.37,"279":63.06,"280":63.82,"281":54.88,"282":57.29,"283":68.85,"284":68.87},"Winogrande":{"0":87.85,"1":87.53,"2":87.37,"3":84.06,"4":84.93,"5":84.45,"6":85.08,"7":86.03,"8":84.06,"9":83.35,"10":87.45,"11":83.43,"12":87.37,"13":88.24,"14":86.35,"15":83.82,"16":83.35,"17":81.45,"18":84.37,"19":83.66,"20":83.19,"21":85.48,"22":83.27,"23":88.08,"24":88.08,"25":83.98,"26":83.43,"27":88.0,"28":85.08,"29":82.95,"30":84.93,"31":84.77,"32":84.93,"33":85.0,"34":84.93,"35":84.93,"36":85.4,"37":84.93,"38":84.85,"39":85.0,"40":85.0,"41":85.0,"42":85.0,"43":85.16,"44":85.16,"45":87.77,"46":85.32,"47":85.0,"48":84.85,"49":84.85,"50":85.16,"51":84.85,"52":84.29,"53":84.93,"54":84.77,"55":84.69,"56":84.77,"57":85.32,"58":84.61,"59":84.53,"60":85.87,"61":85.4,"62":84.77,"63":83.82,"64":88.0,"65":84.69,"66":84.69,"67":84.93,"68":85.24,"69":84.14,"70":87.53,"71":85.56,"72":83.82,"73":87.61,"74":84.06,"75":84.37,"76":83.11,"77":85.24,"78":84.29,"79":84.61,"80":83.9,"81":83.98,"82":84.69,"83":85.48,"84":88.16,"85":84.77,"86":82.72,"87":85.32,"88":84.45,"89":85.16,"90":83.27,"91":85.16,"92":85.0,"93":86.42,"94":81.69,"95":81.69,"96":81.61,"97":84.93,"98":84.37,"99":86.27,"100":83.27,"101":84.77,"102":82.95,"103":82.95,"104":83.5,"105":83.27,"106":84.14,"107":86.66,"108":84.53,"109":84.53,"110":85.32,"111":84.85,"112":83.27,"113":83.98,"114":84.45,"115":85.32,"116":84.14,"117":85.56,"118":83.5,"119":83.19,"120":85.08,"121":84.77,"122":85.79,"123":86.03,"124":84.37,"125":84.69,"126":82.16,"127":82.24,"128":86.74,"129":82.56,"130":84.77,"131":82.64,"132":82.48,"133":83.35,"134":86.98,"135":83.74,"136":82.08,"137":86.66,"138":80.66,"139":83.74,"140":83.03,"141":86.03,"142":86.35,"143":82.4,"144":84.77,"145":83.35,"146":83.5,"147":84.53,"148":81.85,"149":83.66,"150":84.53,"151":84.45,"152":83.35,"153":83.5,"154":83.58,"155":83.66,"156":83.43,"157":83.03,"158":83.5,"159":82.72,"160":83.11,"161":82.95,"162":83.19,"163":83.35,"164":82.95,"165":82.08,"166":82.72,"167":83.43,"168":83.03,"169":82.16,"170":83.5,"171":82.48,"172":81.22,"173":81.77,"174":82.0,"175":83.35,"176":83.27,"177":84.21,"178":82.95,"179":81.93,"180":82.32,"181":85.24,"182":82.48,"183":80.82,"184":82.32,"185":84.21,"186":80.74,"187":80.66,"188":81.37,"189":80.66,"190":80.98,"191":80.74,"192":80.82,"193":81.22,"194":81.14,"195":80.9,"196":83.11,"197":82.79,"198":83.35,"199":83.58,"200":84.77,"201":83.5,"202":80.51,"203":80.58,"204":83.11,"205":83.27,"206":82.64,"207":84.06,"208":80.74,"209":80.74,"210":81.29,"211":83.03,"212":82.64,"213":81.22,"214":83.5,"215":80.66,"216":81.77,"217":82.64,"218":82.56,"219":82.16,"220":81.22,"221":81.45,"222":81.22,"223":81.06,"224":81.93,"225":84.21,"226":84.69,"227":83.43,"228":82.95,"229":81.37,"230":80.9,"231":84.69,"232":83.35,"233":83.27,"234":80.27,"235":80.82,"236":82.56,"237":82.72,"238":80.74,"239":80.82,"240":80.03,"241":84.45,"242":79.87,"243":80.35,"244":82.72,"245":82.48,"246":83.5,"247":82.16,"248":83.5,"249":80.58,"250":80.51,"251":81.53,"252":81.14,"253":81.77,"254":83.66,"255":82.95,"256":82.4,"257":80.74,"258":83.43,"259":80.9,"260":82.95,"261":84.21,"262":79.72,"263":84.77,"264":83.11,"265":79.95,"266":84.06,"267":80.51,"268":84.77,"269":82.4,"270":80.35,"271":83.35,"272":80.51,"273":81.93,"274":80.35,"275":80.35,"276":81.93,"277":84.21,"278":84.06,"279":80.74,"280":79.95,"281":83.35,"282":84.61,"283":83.66,"284":83.66},"GSM8K":{"0":76.12,"1":76.65,"2":76.8,"3":78.77,"4":79.3,"5":79.23,"6":78.7,"7":77.63,"8":78.62,"9":74.45,"10":62.4,"11":72.93,"12":71.11,"13":69.52,"14":68.08,"15":72.48,"16":73.01,"17":76.95,"18":76.8,"19":72.18,"20":70.89,"21":68.99,"22":71.87,"23":67.55,"24":67.1,"25":70.89,"26":72.48,"27":67.63,"28":70.81,"29":71.19,"30":70.43,"31":71.27,"32":70.28,"33":70.43,"34":70.36,"35":70.05,"36":69.29,"37":70.05,"38":70.43,"39":70.43,"40":70.05,"41":69.37,"42":69.07,"43":68.84,"44":68.46,"45":69.14,"46":70.36,"47":68.76,"48":69.6,"49":68.92,"50":69.14,"51":69.22,"52":74.07,"53":68.08,"54":69.45,"55":69.52,"56":73.92,"57":68.08,"58":68.84,"59":69.67,"60":68.99,"61":67.85,"62":69.22,"63":70.36,"64":69.67,"65":69.45,"66":68.84,"67":68.23,"68":70.51,"69":76.27,"70":70.28,"71":69.67,"72":66.11,"73":69.83,"74":65.88,"75":71.95,"76":70.28,"77":70.28,"78":68.08,"79":66.72,"80":69.22,"81":68.54,"82":66.26,"83":65.96,"84":70.66,"85":66.64,"86":72.71,"87":67.32,"88":65.73,"89":64.52,"90":71.8,"91":65.28,"92":66.64,"93":70.36,"94":72.48,"95":66.11,"96":71.95,"97":69.83,"98":72.25,"99":69.67,"100":59.06,"101":71.34,"102":60.05,"103":60.05,"104":71.87,"105":63.91,"106":72.25,"107":68.16,"108":65.43,"109":71.34,"110":62.4,"111":68.46,"112":71.65,"113":70.81,"114":70.43,"115":67.25,"116":71.65,"117":66.72,"118":73.31,"119":70.05,"120":69.29,"121":68.69,"122":68.31,"123":68.01,"124":67.48,"125":67.55,"126":64.52,"127":72.18,"128":68.23,"129":66.94,"130":68.84,"131":69.29,"132":58.45,"133":60.96,"134":67.63,"135":70.2,"136":72.71,"137":65.66,"138":67.25,"139":69.98,"140":59.97,"141":67.17,"142":61.18,"143":72.25,"144":69.6,"145":64.52,"146":66.19,"147":65.58,"148":68.54,"149":64.82,"150":65.81,"151":65.35,"152":69.14,"153":64.14,"154":64.75,"155":64.67,"156":64.59,"157":73.69,"158":64.29,"159":59.21,"160":73.62,"161":63.76,"162":64.29,"163":63.38,"164":64.75,"165":67.55,"166":63.99,"167":64.9,"168":63.38,"169":66.72,"170":66.41,"171":65.43,"172":57.32,"173":61.87,"174":61.64,"175":66.34,"176":62.62,"177":65.13,"178":70.05,"179":72.02,"180":72.25,"181":65.88,"182":70.43,"183":64.75,"184":61.03,"185":58.76,"186":63.53,"187":64.44,"188":61.41,"189":62.77,"190":72.18,"191":63.76,"192":63.53,"193":63.68,"194":63.46,"195":60.27,"196":71.65,"197":65.28,"198":69.52,"199":61.79,"200":68.39,"201":72.18,"202":67.7,"203":71.95,"204":61.49,"205":63.15,"206":70.66,"207":71.57,"208":71.34,"209":61.11,"210":62.62,"211":65.73,"212":65.88,"213":60.8,"214":66.79,"215":69.52,"216":73.39,"217":64.97,"218":58.53,"219":56.33,"220":58.23,"221":59.82,"222":61.11,"223":61.11,"224":63.61,"225":62.09,"226":69.37,"227":62.02,"228":64.67,"229":60.73,"230":72.71,"231":64.37,"232":70.51,"233":50.04,"234":73.16,"235":65.88,"236":59.44,"237":63.68,"238":66.03,"239":58.68,"240":69.75,"241":66.87,"242":70.13,"243":61.79,"244":66.34,"245":61.26,"246":64.52,"247":62.32,"248":66.64,"249":63.99,"250":69.98,"251":66.41,"252":59.59,"253":65.81,"254":64.97,"255":69.6,"256":71.57,"257":72.25,"258":69.14,"259":63.0,"260":69.22,"261":63.68,"262":66.72,"263":66.94,"264":62.09,"265":66.94,"266":59.82,"267":58.91,"268":65.58,"269":64.14,"270":56.18,"271":69.29,"272":66.26,"273":73.24,"274":68.61,"275":59.44,"276":63.53,"277":62.32,"278":52.62,"279":63.84,"280":64.14,"281":67.17,"282":72.1,"283":53.6,"284":53.53},"Type":{"0":"fine-tuned on domain-specific datasets","1":"chat models (RLHF, DPO, IFT, ...)","2":"fine-tuned on domain-specific datasets","3":"fine-tuned on domain-specific datasets","4":"fine-tuned on domain-specific datasets","5":"fine-tuned on domain-specific datasets","6":"fine-tuned on domain-specific datasets","7":"fine-tuned on domain-specific datasets","8":"chat models (RLHF, DPO, IFT, ...)","9":"fine-tuned on domain-specific datasets","10":"fine-tuned on domain-specific datasets","11":"fine-tuned on domain-specific datasets","12":"fine-tuned on domain-specific datasets","13":"chat models (RLHF, DPO, IFT, ...)","14":"fine-tuned on domain-specific datasets","15":"fine-tuned on domain-specific datasets","16":"fine-tuned on domain-specific datasets","17":"fine-tuned on domain-specific datasets","18":"chat models (RLHF, DPO, IFT, ...)","19":"fine-tuned on domain-specific datasets","20":"fine-tuned on domain-specific datasets","21":"fine-tuned on domain-specific datasets","22":"fine-tuned on domain-specific datasets","23":"fine-tuned on domain-specific datasets","24":"fine-tuned on domain-specific datasets","25":"fine-tuned on domain-specific datasets","26":"fine-tuned on domain-specific datasets","27":"fine-tuned on domain-specific datasets","28":"fine-tuned on domain-specific datasets","29":"fine-tuned on domain-specific datasets","30":"fine-tuned on domain-specific datasets","31":"fine-tuned on domain-specific datasets","32":"fine-tuned on domain-specific datasets","33":"fine-tuned on domain-specific datasets","34":"fine-tuned on domain-specific datasets","35":"fine-tuned on domain-specific datasets","36":"fine-tuned on domain-specific datasets","37":"fine-tuned on domain-specific datasets","38":"fine-tuned on domain-specific datasets","39":"fine-tuned on domain-specific datasets","40":"fine-tuned on domain-specific datasets","41":"fine-tuned on domain-specific datasets","42":"fine-tuned on domain-specific datasets","43":"fine-tuned on domain-specific datasets","44":"fine-tuned on domain-specific datasets","45":"chat models (RLHF, DPO, IFT, ...)","46":"base merges and moerges","47":"fine-tuned on domain-specific datasets","48":"fine-tuned on domain-specific datasets","49":"fine-tuned on domain-specific datasets","50":"fine-tuned on domain-specific datasets","51":"chat models (RLHF, DPO, IFT, ...)","52":"fine-tuned on domain-specific datasets","53":"fine-tuned on domain-specific datasets","54":"base merges and moerges","55":"base merges and moerges","56":"fine-tuned on domain-specific datasets","57":"fine-tuned on domain-specific datasets","58":"fine-tuned on domain-specific datasets","59":"fine-tuned on domain-specific datasets","60":"fine-tuned on domain-specific datasets","61":"fine-tuned on domain-specific datasets","62":"fine-tuned on domain-specific datasets","63":"fine-tuned on domain-specific datasets","64":"chat models (RLHF, DPO, IFT, ...)","65":"fine-tuned on domain-specific datasets","66":"base merges and moerges","67":"fine-tuned on domain-specific datasets","68":"fine-tuned on domain-specific datasets","69":"chat models (RLHF, DPO, IFT, ...)","70":"fine-tuned on domain-specific datasets","71":"fine-tuned on domain-specific datasets","72":"chat models (RLHF, DPO, IFT, ...)","73":"chat models (RLHF, DPO, IFT, ...)","74":"chat models (RLHF, DPO, IFT, ...)","75":"fine-tuned on domain-specific datasets","76":"fine-tuned on domain-specific datasets","77":"chat models (RLHF, DPO, IFT, ...)","78":"chat models (RLHF, DPO, IFT, ...)","79":"chat models (RLHF, DPO, IFT, ...)","80":"chat models (RLHF, DPO, IFT, ...)","81":"chat models (RLHF, DPO, IFT, ...)","82":"chat models (RLHF, DPO, IFT, ...)","83":"fine-tuned on domain-specific datasets","84":"fine-tuned on domain-specific datasets","85":"fine-tuned on domain-specific datasets","86":"fine-tuned on domain-specific datasets","87":"fine-tuned on domain-specific datasets","88":"chat models (RLHF, DPO, IFT, ...)","89":"fine-tuned on domain-specific datasets","90":"fine-tuned on domain-specific datasets","91":"chat models (RLHF, DPO, IFT, ...)","92":"fine-tuned on domain-specific datasets","93":"fine-tuned on domain-specific datasets","94":"fine-tuned on domain-specific datasets","95":"fine-tuned on domain-specific datasets","96":"fine-tuned on domain-specific datasets","97":"fine-tuned on domain-specific datasets","98":"fine-tuned on domain-specific datasets","99":"fine-tuned on domain-specific datasets","100":"fine-tuned on domain-specific datasets","101":"fine-tuned on domain-specific datasets","102":"fine-tuned on domain-specific datasets","103":"fine-tuned on domain-specific datasets","104":"fine-tuned on domain-specific datasets","105":"chat models (RLHF, DPO, IFT, ...)","106":"fine-tuned on domain-specific datasets","107":"fine-tuned on domain-specific datasets","108":"fine-tuned on domain-specific datasets","109":"fine-tuned on domain-specific datasets","110":"fine-tuned on domain-specific datasets","111":"chat models (RLHF, DPO, IFT, ...)","112":"fine-tuned on domain-specific datasets","113":"fine-tuned on domain-specific datasets","114":"fine-tuned on domain-specific datasets","115":"fine-tuned on domain-specific datasets","116":"fine-tuned on domain-specific datasets","117":"fine-tuned on domain-specific datasets","118":"fine-tuned on domain-specific datasets","119":"fine-tuned on domain-specific datasets","120":"fine-tuned on domain-specific datasets","121":"fine-tuned on domain-specific datasets","122":"fine-tuned on domain-specific datasets","123":"fine-tuned on domain-specific datasets","124":"chat models (RLHF, DPO, IFT, ...)","125":"chat models (RLHF, DPO, IFT, ...)","126":"chat models (RLHF, DPO, IFT, ...)","127":"chat models (RLHF, DPO, IFT, ...)","128":"fine-tuned on domain-specific datasets","129":"fine-tuned on domain-specific datasets","130":"chat models (RLHF, DPO, IFT, ...)","131":"chat models (RLHF, DPO, IFT, ...)","132":"fine-tuned on domain-specific datasets","133":"chat models (RLHF, DPO, IFT, ...)","134":"fine-tuned on domain-specific datasets","135":"fine-tuned on domain-specific datasets","136":"fine-tuned on domain-specific datasets","137":"chat models (RLHF, DPO, IFT, ...)","138":"chat models (RLHF, DPO, IFT, ...)","139":"fine-tuned on domain-specific datasets","140":"fine-tuned on domain-specific datasets","141":"fine-tuned on domain-specific datasets","142":"fine-tuned on domain-specific datasets","143":"chat models (RLHF, DPO, IFT, ...)","144":"fine-tuned on domain-specific datasets","145":"chat models (RLHF, DPO, IFT, ...)","146":"fine-tuned on domain-specific datasets","147":"chat models (RLHF, DPO, IFT, ...)","148":"fine-tuned on domain-specific datasets","149":"chat models (RLHF, DPO, IFT, ...)","150":"chat models (RLHF, DPO, IFT, ...)","151":"fine-tuned on domain-specific datasets","152":"fine-tuned on domain-specific datasets","153":"fine-tuned on domain-specific datasets","154":"chat models (RLHF, DPO, IFT, ...)","155":"fine-tuned on domain-specific datasets","156":"chat models (RLHF, DPO, IFT, ...)","157":"fine-tuned on domain-specific datasets","158":"chat models (RLHF, DPO, IFT, ...)","159":"fine-tuned on domain-specific datasets","160":"fine-tuned on domain-specific datasets","161":"fine-tuned on domain-specific datasets","162":"chat models (RLHF, DPO, IFT, ...)","163":"chat models (RLHF, DPO, IFT, ...)","164":"fine-tuned on domain-specific datasets","165":"fine-tuned on domain-specific datasets","166":"base merges and moerges","167":"fine-tuned on domain-specific datasets","168":"chat models (RLHF, DPO, IFT, ...)","169":"chat models (RLHF, DPO, IFT, ...)","170":"chat models (RLHF, DPO, IFT, ...)","171":"chat models (RLHF, DPO, IFT, ...)","172":"fine-tuned on domain-specific datasets","173":"base merges and moerges","174":"fine-tuned on domain-specific datasets","175":"fine-tuned on domain-specific datasets","176":"chat models (RLHF, DPO, IFT, ...)","177":"base merges and moerges","178":"fine-tuned on domain-specific datasets","179":"chat models (RLHF, DPO, IFT, ...)","180":"fine-tuned on domain-specific datasets","181":"fine-tuned on domain-specific datasets","182":"pretrained","183":"fine-tuned on domain-specific datasets","184":"fine-tuned on domain-specific datasets","185":"fine-tuned on domain-specific datasets","186":"fine-tuned on domain-specific datasets","187":"fine-tuned on domain-specific datasets","188":"chat models (RLHF, DPO, IFT, ...)","189":"chat models (RLHF, DPO, IFT, ...)","190":"fine-tuned on domain-specific datasets","191":"fine-tuned on domain-specific datasets","192":"fine-tuned on domain-specific datasets","193":"fine-tuned on domain-specific datasets","194":"fine-tuned on domain-specific datasets","195":"base merges and moerges","196":"fine-tuned on domain-specific datasets","197":"fine-tuned on domain-specific datasets","198":"fine-tuned on domain-specific datasets","199":"chat models (RLHF, DPO, IFT, ...)","200":"fine-tuned on domain-specific datasets","201":"chat models (RLHF, DPO, IFT, ...)","202":"fine-tuned on domain-specific datasets","203":"chat models (RLHF, DPO, IFT, ...)","204":"fine-tuned on domain-specific datasets","205":"fine-tuned on domain-specific datasets","206":"chat models (RLHF, DPO, IFT, ...)","207":"fine-tuned on domain-specific datasets","208":"chat models (RLHF, DPO, IFT, ...)","209":"fine-tuned on domain-specific datasets","210":"fine-tuned on domain-specific datasets","211":"pretrained","212":"chat models (RLHF, DPO, IFT, ...)","213":"fine-tuned on domain-specific datasets","214":"fine-tuned on domain-specific datasets","215":"fine-tuned on domain-specific datasets","216":"base merges and moerges","217":"chat models (RLHF, DPO, IFT, ...)","218":"fine-tuned on domain-specific datasets","219":"fine-tuned on domain-specific datasets","220":"fine-tuned on domain-specific datasets","221":"fine-tuned on domain-specific datasets","222":"fine-tuned on domain-specific datasets","223":"fine-tuned on domain-specific datasets","224":"base merges and moerges","225":"chat models (RLHF, DPO, IFT, ...)","226":"fine-tuned on domain-specific datasets","227":"chat models (RLHF, DPO, IFT, ...)","228":"base merges and moerges","229":"chat models (RLHF, DPO, IFT, ...)","230":"fine-tuned on domain-specific datasets","231":"fine-tuned on domain-specific datasets","232":"chat models (RLHF, DPO, IFT, ...)","233":"fine-tuned on domain-specific datasets","234":"fine-tuned on domain-specific datasets","235":"fine-tuned on domain-specific datasets","236":"fine-tuned on domain-specific datasets","237":"base merges and moerges","238":"fine-tuned on domain-specific datasets","239":"chat models (RLHF, DPO, IFT, ...)","240":"fine-tuned on domain-specific datasets","241":"fine-tuned on domain-specific datasets","242":"base merges and moerges","243":"fine-tuned on domain-specific datasets","244":"base merges and moerges","245":"fine-tuned on domain-specific datasets","246":"chat models (RLHF, DPO, IFT, ...)","247":"fine-tuned on domain-specific datasets","248":"fine-tuned on domain-specific datasets","249":"fine-tuned on domain-specific datasets","250":"base merges and moerges","251":"fine-tuned on domain-specific datasets","252":"fine-tuned on domain-specific datasets","253":"chat models (RLHF, DPO, IFT, ...)","254":"chat models (RLHF, DPO, IFT, ...)","255":"chat models (RLHF, DPO, IFT, ...)","256":"fine-tuned on domain-specific datasets","257":"fine-tuned on domain-specific datasets","258":"chat models (RLHF, DPO, IFT, ...)","259":"fine-tuned on domain-specific datasets","260":"chat models (RLHF, DPO, IFT, ...)","261":"chat models (RLHF, DPO, IFT, ...)","262":"chat models (RLHF, DPO, IFT, ...)","263":"fine-tuned on domain-specific datasets","264":"fine-tuned on domain-specific datasets","265":"chat models (RLHF, DPO, IFT, ...)","266":"chat models (RLHF, DPO, IFT, ...)","267":"chat models (RLHF, DPO, IFT, ...)","268":"fine-tuned on domain-specific datasets","269":"chat models (RLHF, DPO, IFT, ...)","270":"base merges and moerges","271":"fine-tuned on domain-specific datasets","272":"chat models (RLHF, DPO, IFT, ...)","273":"fine-tuned on domain-specific datasets","274":"chat models (RLHF, DPO, IFT, ...)","275":"chat models (RLHF, DPO, IFT, ...)","276":"base merges and moerges","277":"fine-tuned on domain-specific datasets","278":"fine-tuned on domain-specific datasets","279":"base merges and moerges","280":"fine-tuned on domain-specific datasets","281":"chat models (RLHF, DPO, IFT, ...)","282":"fine-tuned on domain-specific datasets","283":"fine-tuned on domain-specific datasets","284":"fine-tuned on domain-specific datasets"},"Architecture":{"0":"LlamaForCausalLM","1":"LlamaForCausalLM","2":"LlamaForCausalLM","3":"?","4":"?","5":"?","6":"LlamaForCausalLM","7":"LlamaForCausalLM","8":"LlamaForCausalLM","9":"MixtralForCausalLM","10":"LlamaForCausalLM","11":"MixtralForCausalLM","12":"MixtralForCausalLM","13":"MixtralForCausalLM","14":"LlamaForCausalLM","15":"LlamaForCausalLM","16":"MixtralForCausalLM","17":"LlamaForCausalLM","18":"LlamaForCausalLM","19":"LlamaForCausalLM","20":"MixtralForCausalLM","21":"MixtralForCausalLM","22":"LlamaForCausalLM","23":"MixtralForCausalLM","24":"MixtralForCausalLM","25":"MixtralForCausalLM","26":"LlamaForCausalLM","27":"MixtralForCausalLM","28":"LlamaForCausalLM","29":"MixtralForCausalLM","30":"MistralForCausalLM","31":"MistralForCausalLM","32":"MistralForCausalLM","33":"MistralForCausalLM","34":"MistralForCausalLM","35":"MistralForCausalLM","36":"MistralForCausalLM","37":"MistralForCausalLM","38":"MistralForCausalLM","39":"MistralForCausalLM","40":"MistralForCausalLM","41":"MistralForCausalLM","42":"MistralForCausalLM","43":"MistralForCausalLM","44":"MistralForCausalLM","45":"MixtralForCausalLM","46":"MistralForCausalLM","47":"MistralForCausalLM","48":"MistralForCausalLM","49":"MistralForCausalLM","50":"MistralForCausalLM","51":"MistralForCausalLM","52":"MixtralForCausalLM","53":"MistralForCausalLM","54":"MistralForCausalLM","55":"MistralForCausalLM","56":"MixtralForCausalLM","57":"MistralForCausalLM","58":"MistralForCausalLM","59":"MistralForCausalLM","60":"LlamaForCausalLM","61":"MistralForCausalLM","62":"MistralForCausalLM","63":"MistralForCausalLM","64":"MixtralForCausalLM","65":"MistralForCausalLM","66":"MistralForCausalLM","67":"MistralForCausalLM","68":"MistralForCausalLM","69":"LlamaForCausalLM","70":"MixtralForCausalLM","71":"MistralForCausalLM","72":"MistralForCausalLM","73":"MixtralForCausalLM","74":"MistralForCausalLM","75":"MistralForCausalLM","76":"MistralForCausalLM","77":"MistralForCausalLM","78":"MistralForCausalLM","79":"MistralForCausalLM","80":"MistralForCausalLM","81":"MistralForCausalLM","82":"MistralForCausalLM","83":"MistralForCausalLM","84":"MixtralForCausalLM","85":"MistralForCausalLM","86":"MixtralForCausalLM","87":"MixtralForCausalLM","88":"MistralForCausalLM","89":"MistralForCausalLM","90":"MistralForCausalLM","91":"MistralForCausalLM","92":"MistralForCausalLM","93":"MixtralForCausalLM","94":"MixtralForCausalLM","95":"MixtralForCausalLM","96":"MixtralForCausalLM","97":"MixtralForCausalLM","98":"MixtralForCausalLM","99":"MixtralForCausalLM","100":"LlamaForCausalLM","101":"LlamaForCausalLM","102":"LlamaForCausalLM","103":"LlamaForCausalLM","104":"MistralForCausalLM","105":"MistralForCausalLM","106":"MixtralForCausalLM","107":"MistralForCausalLM","108":"MistralForCausalLM","109":"LlamaForCausalLM","110":"MistralForCausalLM","111":"LlamaForCausalLM","112":"MistralForCausalLM","113":"LlamaForCausalLM","114":"MistralForCausalLM","115":"MistralForCausalLM","116":"MixtralForCausalLM","117":"MistralForCausalLM","118":"MistralForCausalLM","119":"MistralForCausalLM","120":"LlamaForCausalLM","121":"MixtralForCausalLM","122":"MistralForCausalLM","123":"MixtralForCausalLM","124":"MistralForCausalLM","125":"MistralForCausalLM","126":"LlamaForCausalLM","127":"LlamaForCausalLM","128":"MistralForCausalLM","129":"MixtralForCausalLM","130":"MistralForCausalLM","131":"MistralForCausalLM","132":"LlamaForCausalLM","133":"LlamaForCausalLM","134":"MistralForCausalLM","135":"LlamaForCausalLM","136":"MistralForCausalLM","137":"MistralForCausalLM","138":"MixtralForCausalLM","139":"LlamaForCausalLM","140":"LlamaForCausalLM","141":"MistralForCausalLM","142":"MixtralForCausalLM","143":"LlamaForCausalLM","144":"MistralForCausalLM","145":"MixtralForCausalLM","146":"MistralForCausalLM","147":"MistralForCausalLM","148":"MistralForCausalLM","149":"LlamaForCausalLM","150":"MistralForCausalLM","151":"MistralForCausalLM","152":"MistralForCausalLM","153":"LlamaForCausalLM","154":"LlamaForCausalLM","155":"LlamaForCausalLM","156":"LlamaForCausalLM","157":"Qwen2ForCausalLM","158":"LlamaForCausalLM","159":"LlamaForCausalLM","160":"Qwen2ForCausalLM","161":"MixtralForCausalLM","162":"LlamaForCausalLM","163":"LlamaForCausalLM","164":"LlamaForCausalLM","165":"MixtralForCausalLM","166":"LlamaForCausalLM","167":"Unknown","168":"LlamaForCausalLM","169":"MistralForCausalLM","170":"MistralForCausalLM","171":"MistralForCausalLM","172":"MixtralForCausalLM","173":"MixtralForCausalLM","174":"MixtralForCausalLM","175":"LlamaForCausalLM","176":"LlamaForCausalLM","177":"MixtralForCausalLM","178":"LlamaForCausalLM","179":"MistralForCausalLM","180":"LlamaForCausalLM","181":"LlamaForCausalLM","182":"QWenLMHeadModel","183":"MistralForCausalLM","184":"LlamaForCausalLM","185":"MistralForCausalLM","186":"MistralForCausalLM","187":"MistralForCausalLM","188":"MixtralForCausalLM","189":"MistralForCausalLM","190":"MixtralForCausalLM","191":"MistralForCausalLM","192":"MistralForCausalLM","193":"MistralForCausalLM","194":"MistralForCausalLM","195":"MixtralForCausalLM","196":"MixtralForCausalLM","197":"MistralForCausalLM","198":"MixtralForCausalLM","199":"LlamaForCausalLM","200":"?","201":"LlamaForCausalLM","202":"LlamaForCausalLM","203":"MixtralForCausalLM","204":"LlamaForCausalLM","205":"MixtralForCausalLM","206":"MixtralForCausalLM","207":"MixtralForCausalLM","208":"MixtralForCausalLM","209":"MixtralForCausalLM","210":"MistralForCausalLM","211":"Qwen2ForCausalLM","212":"Qwen2ForCausalLM","213":"MixtralForCausalLM","214":"LlamaForCausalLM","215":"MistralForCausalLM","216":"MistralForCausalLM","217":"Qwen2ForCausalLM","218":"MixtralForCausalLM","219":"MixtralForCausalLM","220":"MixtralForCausalLM","221":"MixtralForCausalLM","222":"MixtralForCausalLM","223":"MixtralForCausalLM","224":"MixtralForCausalLM","225":"LlamaForCausalLM","226":"MistralForCausalLM","227":"LlamaForCausalLM","228":"LlamaForCausalLM","229":"MixtralForCausalLM","230":"MistralForCausalLM","231":"LlamaForCausalLM","232":"LlamaForCausalLM","233":"MixtralForCausalLM","234":"MistralForCausalLM","235":"MistralForCausalLM","236":"MixtralForCausalLM","237":"LlamaForCausalLM","238":"MistralForCausalLM","239":"MixtralForCausalLM","240":"MixtralForCausalLM","241":"LlamaForCausalLM","242":"MixtralForCausalLM","243":"MixtralForCausalLM","244":"MixtralForCausalLM","245":"LlamaForCausalLM","246":"LlamaForCausalLM","247":"LlamaForCausalLM","248":"LlamaForCausalLM","249":"MistralForCausalLM","250":"MistralForCausalLM","251":"MistralForCausalLM","252":"MixtralForCausalLM","253":"MistralForCausalLM","254":"LlamaForCausalLM","255":"MixtralForCausalLM","256":"LlamaForCausalLM","257":"MistralForCausalLM","258":"LlamaForCausalLM","259":"MixtralForCausalLM","260":"LlamaForCausalLM","261":"LlamaForCausalLM","262":"MistralForCausalLM","263":"LlamaForCausalLM","264":"LlamaForCausalLM","265":"MistralForCausalLM","266":"LlamaForCausalLM","267":"MistralForCausalLM","268":"LlamaForCausalLM","269":"LlamaForCausalLM","270":"LlamaForCausalLM","271":"MistralForCausalLM","272":"MistralForCausalLM","273":"MixtralForCausalLM","274":"MistralForCausalLM","275":"MistralForCausalLM","276":"MixtralForCausalLM","277":"LlamaForCausalLM","278":"LlamaForCausalLM","279":"MistralForCausalLM","280":"MixtralForCausalLM","281":"LlamaForCausalLM","282":"LlamaForCausalLM","283":"LlamaForCausalLM","284":"LlamaForCausalLM"},"Precision":{"0":"float16","1":"bfloat16","2":"float16","3":"bfloat16","4":"bfloat16","5":"bfloat16","6":"bfloat16","7":"float16","8":"bfloat16","9":"bfloat16","10":"bfloat16","11":"float16","12":"bfloat16","13":"bfloat16","14":"float16","15":"bfloat16","16":"bfloat16","17":"float16","18":"bfloat16","19":"bfloat16","20":"bfloat16","21":"float16","22":"float16","23":"bfloat16","24":"bfloat16","25":"bfloat16","26":"float16","27":"bfloat16","28":"float16","29":"4bit","30":"float16","31":"bfloat16","32":"float16","33":"bfloat16","34":"bfloat16","35":"float16","36":"float16","37":"float16","38":"bfloat16","39":"bfloat16","40":"float16","41":"float16","42":"bfloat16","43":"float16","44":"float16","45":"bfloat16","46":"bfloat16","47":"float16","48":"bfloat16","49":"float16","50":"float16","51":"float16","52":"bfloat16","53":"float16","54":"float16","55":"float16","56":"bfloat16","57":"float16","58":"bfloat16","59":"float16","60":"float16","61":"float16","62":"float16","63":"float16","64":"bfloat16","65":"float16","66":"float16","67":"float16","68":"bfloat16","69":"bfloat16","70":"float16","71":"float16","72":"float16","73":"float16","74":"float16","75":"float16","76":"bfloat16","77":"bfloat16","78":"float16","79":"float16","80":"float16","81":"float16","82":"float16","83":"float16","84":"bfloat16","85":"float16","86":"bfloat16","87":"bfloat16","88":"float16","89":"float16","90":"bfloat16","91":"float16","92":"float16","93":"float16","94":"bfloat16","95":"float16","96":"float16","97":"bfloat16","98":"bfloat16","99":"float16","100":"bfloat16","101":"float16","102":"bfloat16","103":"bfloat16","104":"float16","105":"float16","106":"bfloat16","107":"float16","108":"float16","109":"bfloat16","110":"float16","111":"bfloat16","112":"float16","113":"float16","114":"bfloat16","115":"bfloat16","116":"bfloat16","117":"float16","118":"float16","119":"float16","120":"float16","121":"bfloat16","122":"float16","123":"float16","124":"bfloat16","125":"float16","126":"bfloat16","127":"bfloat16","128":"float16","129":"bfloat16","130":"float16","131":"float16","132":"bfloat16","133":"float16","134":"float16","135":"bfloat16","136":"float16","137":"float16","138":"float16","139":"float16","140":"bfloat16","141":"float16","142":"bfloat16","143":"bfloat16","144":"float16","145":"bfloat16","146":"float16","147":"float16","148":"float16","149":"float16","150":"float16","151":"float16","152":"bfloat16","153":"bfloat16","154":"float16","155":"bfloat16","156":"float16","157":"bfloat16","158":"float16","159":"float16","160":"float16","161":"bfloat16","162":"float16","163":"float16","164":"bfloat16","165":"bfloat16","166":"float16","167":"bfloat16","168":"float16","169":"bfloat16","170":"float16","171":"bfloat16","172":"float16","173":"bfloat16","174":"bfloat16","175":"float16","176":"bfloat16","177":"bfloat16","178":"bfloat16","179":"float16","180":"bfloat16","181":"float16","182":"bfloat16","183":"float16","184":"bfloat16","185":"float16","186":"float16","187":"float16","188":"bfloat16","189":"bfloat16","190":"bfloat16","191":"float16","192":"float16","193":"float16","194":"float16","195":"bfloat16","196":"float16","197":"bfloat16","198":"bfloat16","199":"bfloat16","200":"float16","201":"bfloat16","202":"float16","203":"bfloat16","204":"float16","205":"bfloat16","206":"bfloat16","207":"float16","208":"bfloat16","209":"float16","210":"float16","211":"bfloat16","212":"float16","213":"float16","214":"float16","215":"float16","216":"float16","217":"float16","218":"bfloat16","219":"bfloat16","220":"float16","221":"bfloat16","222":"bfloat16","223":"float16","224":"bfloat16","225":"float16","226":"float16","227":"bfloat16","228":"float16","229":"bfloat16","230":"float16","231":"float16","232":"bfloat16","233":"bfloat16","234":"float16","235":"float16","236":"bfloat16","237":"bfloat16","238":"bfloat16","239":"float16","240":"bfloat16","241":"bfloat16","242":"bfloat16","243":"float16","244":"bfloat16","245":"bfloat16","246":"float16","247":"bfloat16","248":"float16","249":"bfloat16","250":"bfloat16","251":"float16","252":"float16","253":"float16","254":"float16","255":"bfloat16","256":"bfloat16","257":"bfloat16","258":"float16","259":"float16","260":"bfloat16","261":"float16","262":"float16","263":"bfloat16","264":"float16","265":"float16","266":"float16","267":"float16","268":"float16","269":"float16","270":"float16","271":"float16","272":"float16","273":"bfloat16","274":"float16","275":"float16","276":"float16","277":"bfloat16","278":"float16","279":"bfloat16","280":"float16","281":"bfloat16","282":"float16","283":"float16","284":"bfloat16"},"Merged":{"0":false,"1":false,"2":false,"3":false,"4":false,"5":false,"6":false,"7":false,"8":false,"9":false,"10":false,"11":false,"12":false,"13":false,"14":false,"15":false,"16":false,"17":false,"18":false,"19":false,"20":false,"21":false,"22":false,"23":false,"24":false,"25":false,"26":false,"27":false,"28":false,"29":false,"30":false,"31":false,"32":false,"33":false,"34":false,"35":false,"36":false,"37":false,"38":false,"39":false,"40":false,"41":false,"42":false,"43":false,"44":false,"45":false,"46":false,"47":false,"48":false,"49":false,"50":false,"51":false,"52":false,"53":false,"54":false,"55":false,"56":false,"57":false,"58":false,"59":false,"60":false,"61":false,"62":false,"63":false,"64":false,"65":false,"66":false,"67":false,"68":false,"69":false,"70":false,"71":false,"72":false,"73":false,"74":false,"75":false,"76":false,"77":false,"78":false,"79":false,"80":false,"81":false,"82":false,"83":false,"84":false,"85":false,"86":false,"87":false,"88":false,"89":false,"90":false,"91":false,"92":false,"93":false,"94":false,"95":false,"96":false,"97":false,"98":false,"99":false,"100":false,"101":false,"102":false,"103":false,"104":false,"105":false,"106":false,"107":false,"108":false,"109":false,"110":false,"111":false,"112":false,"113":false,"114":false,"115":false,"116":false,"117":false,"118":false,"119":false,"120":false,"121":false,"122":false,"123":false,"124":false,"125":false,"126":false,"127":false,"128":false,"129":false,"130":false,"131":false,"132":false,"133":false,"134":false,"135":false,"136":false,"137":false,"138":false,"139":false,"140":false,"141":false,"142":false,"143":false,"144":false,"145":false,"146":false,"147":false,"148":false,"149":false,"150":false,"151":false,"152":false,"153":false,"154":false,"155":false,"156":false,"157":false,"158":false,"159":false,"160":false,"161":false,"162":false,"163":false,"164":false,"165":false,"166":false,"167":false,"168":false,"169":false,"170":false,"171":false,"172":false,"173":false,"174":false,"175":false,"176":false,"177":false,"178":false,"179":false,"180":false,"181":false,"182":false,"183":false,"184":false,"185":false,"186":false,"187":false,"188":false,"189":false,"190":false,"191":false,"192":false,"193":false,"194":false,"195":false,"196":false,"197":false,"198":false,"199":false,"200":false,"201":false,"202":false,"203":false,"204":false,"205":false,"206":false,"207":false,"208":false,"209":false,"210":false,"211":false,"212":false,"213":false,"214":false,"215":false,"216":false,"217":false,"218":false,"219":false,"220":false,"221":false,"222":false,"223":false,"224":false,"225":false,"226":false,"227":false,"228":false,"229":false,"230":false,"231":false,"232":false,"233":false,"234":false,"235":false,"236":false,"237":false,"238":false,"239":false,"240":false,"241":false,"242":false,"243":false,"244":false,"245":false,"246":false,"247":false,"248":false,"249":false,"250":false,"251":false,"252":false,"253":false,"254":false,"255":false,"256":false,"257":false,"258":false,"259":false,"260":false,"261":false,"262":false,"263":false,"264":false,"265":false,"266":false,"267":false,"268":false,"269":false,"270":false,"271":false,"272":false,"273":false,"274":false,"275":false,"276":false,"277":false,"278":false,"279":false,"280":false,"281":false,"282":false,"283":false,"284":false},"Hub License":{"0":"apache-2.0","1":"other","2":"other","3":"apache-2.0","4":"apache-2.0","5":"apache-2.0","6":"other","7":"other","8":"mit","9":"other","10":"apache-2.0","11":"other","12":"apache-2.0","13":"mit","14":"cc-by-nc-4.0","15":"apache-2.0","16":"mit","17":"other","18":"mit","19":"other","20":"mit","21":"apache-2.0","22":"other","23":["other"],"24":"other","25":"mit","26":"other","27":"other","28":"cc-by-nc-4.0","29":"other","30":"apache-2.0","31":"apache-2.0","32":"apache-2.0","33":"apache-2.0","34":"mit","35":"apache-2.0","36":"apache-2.0","37":"apache-2.0","38":"mit","39":"mit","40":"apache-2.0","41":"openrail","42":"apache-2.0","43":"apache-2.0","44":"apache-2.0","45":"apache-2.0","46":"apache-2.0","47":"apache-2.0","48":"apache-2.0","49":"apache-2.0","50":"apache-2.0","51":"apache-2.0","52":"mit","53":"apache-2.0","54":"cc-by-nc-4.0","55":"cc-by-nc-4.0","56":"other","57":"apache-2.0","58":"cc-by-nc-4.0","59":"cc-by-4.0","60":"cc-by-nc-4.0","61":"apache-2.0","62":"cc-by-nc-4.0","63":"apache-2.0","64":"apache-2.0","65":"cc-by-4.0","66":"cc-by-nc-4.0","67":"apache-2.0","68":"apache-2.0","69":"mit","70":"mit","71":"cc","72":"apache-2.0","73":"mit","74":"apache-2.0","75":"apache-2.0","76":"apache-2.0","77":"mit","78":"apache-2.0","79":"cc-by-nc-4.0","80":"apache-2.0","81":"apache-2.0","82":"cc-by-nc-4.0","83":"apache-2.0","84":"mit","85":"apache-2.0","86":"other","87":"cc-by-nc-4.0","88":"cc-by-nc-4.0","89":"cc-by-nc-4.0","90":"apache-2.0","91":"cc-by-nc-4.0","92":"apache-2.0","93":"apache-2.0","94":"apache-2.0","95":"apache-2.0","96":"apache-2.0","97":"other","98":"other","99":"apache-2.0","100":"apache-2.0","101":"cc0-1.0","102":"other","103":"other","104":"apache-2.0","105":"apache-2.0","106":"other","107":"apache-2.0","108":"apache-2.0","109":"cc0-1.0","110":"apache-2.0","111":"cc-by-sa-4.0","112":"apache-2.0","113":"llama2","114":"cc-by-nc-4.0","115":"apache-2.0","116":"other","117":"apache-2.0","118":"apache-2.0","119":"apache-2.0","120":"cc-by-nc-4.0","121":"other","122":"apache-2.0","123":"apache-2.0","124":"cc-by-nc-4.0","125":"other","126":"apache-2.0","127":"apache-2.0","128":"apache-2.0","129":"other","130":"other","131":"cc-by-nc-4.0","132":"other","133":"other","134":"apache-2.0","135":"mit","136":"apache-2.0","137":"apache-2.0","138":"apache-2.0","139":"mit","140":"other","141":"apache-2.0","142":"cc-by-nc-nd-4.0","143":"apache-2.0","144":"apache-2.0","145":"other","146":"apache-2.0","147":"cc-by-sa-4.0","148":"apache-2.0","149":"mit","150":"cc-by-sa-4.0","151":"cc-by-sa-4.0","152":"apache-2.0","153":"cc-by-nc-4.0","154":"cc-by-nc-4.0","155":"cc-by-nc-nd-4.0","156":"mit","157":"other","158":"cc-by-nc-4.0","159":"cc","160":"other","161":"other","162":"cc-by-nc-4.0","163":"cc-by-nc-nd-4.0","164":"cc-by-nc-nd-4.0","165":"apache-2.0","166":"cc-by-nc-sa-4.0","167":"cc-by-nc-4.0","168":"apache-2.0","169":"cc-by-nc-nd-4.0","170":"apache-2.0","171":"apache-2.0","172":"cc-by-nc-4.0","173":"cc-by-nc-4.0","174":"cc-by-nc-sa-4.0","175":"llama2","176":"other","177":"apache-2.0","178":"apache-2.0","179":"cc-by-nc-4.0","180":"apache-2.0","181":"cc-by-nc-4.0","182":"other","183":"apache-2.0","184":"cc-by-nc-4.0","185":"apache-2.0","186":"apache-2.0","187":"apache-2.0","188":"apache-2.0","189":"apache-2.0","190":"cc-by-nc-4.0","191":"apache-2.0","192":"apache-2.0","193":"apache-2.0","194":"apache-2.0","195":"cc-by-nc-4.0","196":"apache-2.0","197":"apache-2.0","198":"apache-2.0","199":"llama2","200":"apache-2.0","201":"other","202":"cc-by-nc-4.0","203":"mit","204":"llama2","205":"apache-2.0","206":"apache-2.0","207":"apache-2.0","208":"mit","209":"apache-2.0","210":"cc-by-4.0","211":"other","212":"other","213":"apache-2.0","214":"cc-by-nc-4.0","215":"apache-2.0","216":"apache-2.0","217":"other","218":"apache-2.0","219":"cc-by-nc-4.0","220":"cc-by-nc-4.0","221":"apache-2.0","222":"apache-2.0","223":"apache-2.0","224":"apache-2.0","225":"cc-by-nc-4.0","226":"apache-2.0","227":"apache-2.0","228":"cc-by-nc-4.0","229":"apache-2.0","230":"apache-2.0","231":"cc-by-nc-4.0","232":"apache-2.0","233":"apache-2.0","234":"apache-2.0","235":"cc-by-nc-4.0","236":"apache-2.0","237":"cc-by-nc-4.0","238":"cc-by-nc-4.0","239":"apache-2.0","240":"cc-by-nc-2.0","241":"other","242":"mit","243":"cc-by-nc-4.0","244":"apache-2.0","245":"apache-2.0","246":"other","247":"cc-by-nc-4.0","248":"cc-by-nc-4.0","249":"cc-by-nc-4.0","250":"apache-2.0","251":"apache-2.0","252":"cc-by-nc-4.0","253":"apache-2.0","254":"other","255":"apache-2.0","256":"gpl-3.0","257":"cc-by-nc-4.0","258":"apache-2.0","259":"cc-by-nc-4.0","260":"other","261":"other","262":"apache-2.0","263":"other","264":"other","265":"apache-2.0","266":"other","267":"apache-2.0","268":"cc-by-nc-sa-4.0","269":"apache-2.0","270":"unknown","271":"apache-2.0","272":"apache-2.0","273":"apache-2.0","274":"apache-2.0","275":"apache-2.0","276":"apache-2.0","277":"other","278":"cc-by-nc-4.0","279":"apache-2.0","280":"wtfpl","281":"other","282":"other","283":"cc-by-nc-4.0","284":"cc-by-nc-4.0"},"#Params (B)":{"0":72.29,"1":72.29,"2":72.29,"3":72.0,"4":72.0,"5":72.0,"6":72.29,"7":72.29,"8":72.29,"9":60.81,"10":21.42,"11":60.81,"12":12.88,"13":12.88,"14":68.98,"15":34.39,"16":60.81,"17":72.29,"18":72.29,"19":34.39,"20":60.81,"21":125.35,"22":34.39,"23":12.88,"24":12.88,"25":60.81,"26":34.39,"27":12.88,"28":68.98,"29":31.8,"30":7.24,"31":7.24,"32":7.24,"33":7.24,"34":7.24,"35":7.24,"36":7.24,"37":7.24,"38":7.24,"39":7.24,"40":7.24,"41":7.24,"42":7.24,"43":7.24,"44":7.24,"45":12.88,"46":7.24,"47":7.24,"48":7.24,"49":7.24,"50":7.24,"51":7.24,"52":60.81,"53":7.24,"54":7.24,"55":7.24,"56":60.81,"57":7.24,"58":7.24,"59":7.24,"60":68.98,"61":7.24,"62":7.24,"63":7.24,"64":12.88,"65":7.24,"66":7.24,"67":7.24,"68":7.24,"69":72.29,"70":12.88,"71":7.24,"72":10.73,"73":12.88,"74":10.73,"75":7.24,"76":7.24,"77":7.24,"78":7.24,"79":7.24,"80":7.24,"81":7.24,"82":7.24,"83":7.24,"84":12.88,"85":7.24,"86":60.81,"87":125.35,"88":7.24,"89":7.24,"90":7.24,"91":7.24,"92":7.24,"93":35.43,"94":46.7,"95":12.88,"96":46.7,"97":60.81,"98":113.66,"99":35.43,"100":34.39,"101":68.98,"102":34.39,"103":34.39,"104":7.24,"105":10.73,"106":87.24,"107":7.24,"108":7.24,"109":68.98,"110":7.24,"111":68.98,"112":7.24,"113":68.98,"114":7.24,"115":7.24,"116":113.66,"117":7.24,"118":7.24,"119":7.24,"120":70.0,"121":60.81,"122":7.24,"123":35.43,"124":7.24,"125":7.24,"126":10.7,"127":34.39,"128":7.24,"129":60.81,"130":7.24,"131":7.24,"132":34.39,"133":34.39,"134":7.24,"135":72.29,"136":7.24,"137":7.24,"138":12.88,"139":72.29,"140":34.39,"141":7.24,"142":18.79,"143":34.39,"144":7.24,"145":19.19,"146":7.24,"147":7.24,"148":7.24,"149":10.73,"150":7.24,"151":7.24,"152":7.24,"153":10.73,"154":10.73,"155":10.73,"156":10.73,"157":72.0,"158":10.73,"159":10.73,"160":72.0,"161":19.19,"162":10.73,"163":10.73,"164":10.73,"165":12.88,"166":10.73,"167":10.73,"168":10.73,"169":7.24,"170":10.73,"171":7.24,"172":46.7,"173":46.7,"174":46.7,"175":68.98,"176":68.98,"177":36.1,"178":34.39,"179":7.24,"180":34.39,"181":68.98,"182":72.29,"183":8.99,"184":10.73,"185":7.24,"186":8.99,"187":8.99,"188":46.7,"189":8.99,"190":29.79,"191":8.99,"192":8.99,"193":8.99,"194":8.99,"195":46.7,"196":46.7,"197":7.24,"198":60.81,"199":68.98,"200":7.0,"201":34.0,"202":10.73,"203":24.15,"204":68.98,"205":12.88,"206":46.7,"207":35.43,"208":24.15,"209":46.7,"210":12.91,"211":72.29,"212":72.29,"213":46.7,"214":10.73,"215":7.24,"216":7.24,"217":72.29,"218":46.7,"219":19.19,"220":46.7,"221":46.7,"222":46.7,"223":46.7,"224":24.15,"225":10.86,"226":7.24,"227":34.39,"228":10.73,"229":46.7,"230":7.24,"231":11.0,"232":34.39,"233":46.7,"234":7.0,"235":7.24,"236":46.7,"237":10.73,"238":7.24,"239":46.7,"240":24.15,"241":67.42,"242":12.88,"243":46.7,"244":12.88,"245":10.73,"246":34.39,"247":34.0,"248":10.73,"249":7.24,"250":7.24,"251":7.24,"252":46.7,"253":7.24,"254":34.39,"255":46.7,"256":72.0,"257":7.24,"258":10.73,"259":12.88,"260":67.42,"261":67.0,"262":7.24,"263":67.42,"264":34.39,"265":7.24,"266":34.39,"267":7.24,"268":10.73,"269":34.0,"270":103.2,"271":7.24,"272":7.24,"273":12.88,"274":7.24,"275":7.24,"276":12.88,"277":67.0,"278":14.22,"279":7.24,"280":24.15,"281":67.42,"282":19.86,"283":10.86,"284":10.86},"Hub \u2764\ufe0f":{"0":29,"1":2,"2":2,"3":10,"4":4,"5":3,"6":430,"7":19,"8":67,"9":9,"10":19,"11":2,"12":27,"13":46,"14":2,"15":14,"16":7,"17":12,"18":32,"19":50,"20":4,"21":3,"22":11,"23":9,"24":0,"25":8,"26":7,"27":4,"28":0,"29":1,"30":71,"31":19,"32":2,"33":71,"34":0,"35":0,"36":0,"37":7,"38":0,"39":0,"40":2,"41":1,"42":1,"43":1,"44":0,"45":16,"46":2,"47":1,"48":0,"49":0,"50":9,"51":4,"52":0,"53":0,"54":4,"55":0,"56":2,"57":0,"58":0,"59":26,"60":0,"61":0,"62":0,"63":1,"64":3,"65":9,"66":0,"67":0,"68":0,"69":11,"70":6,"71":4,"72":8,"73":0,"74":6,"75":7,"76":0,"77":0,"78":1,"79":8,"80":8,"81":0,"82":9,"83":1,"84":35,"85":0,"86":1,"87":14,"88":3,"89":0,"90":0,"91":2,"92":0,"93":1,"94":11,"95":1,"96":11,"97":1,"98":11,"99":0,"100":5,"101":134,"102":1,"103":9,"104":0,"105":2,"106":9,"107":21,"108":0,"109":134,"110":1,"111":22,"112":3,"113":19,"114":1,"115":1,"116":3,"117":1,"118":4,"119":2,"120":16,"121":15,"122":0,"123":0,"124":0,"125":5,"126":1,"127":12,"128":33,"129":2,"130":2,"131":0,"132":35,"133":87,"134":85,"135":86,"136":1,"137":2,"138":1,"139":86,"140":87,"141":6,"142":2,"143":1,"144":0,"145":1,"146":0,"147":6,"148":0,"149":4,"150":2,"151":6,"152":8,"153":44,"154":554,"155":16,"156":1,"157":69,"158":0,"159":3,"160":69,"161":0,"162":2,"163":16,"164":5,"165":3,"166":1,"167":14,"168":1,"169":31,"170":2,"171":2,"172":1,"173":4,"174":8,"175":16,"176":142,"177":5,"178":215,"179":6,"180":1,"181":0,"182":314,"183":0,"184":1,"185":0,"186":1,"187":0,"188":1,"189":5,"190":1,"191":0,"192":1,"193":1,"194":0,"195":0,"196":320,"197":3,"198":0,"199":10,"200":0,"201":111,"202":7,"203":2,"204":10,"205":7,"206":320,"207":0,"208":1,"209":161,"210":7,"211":44,"212":0,"213":19,"214":10,"215":0,"216":1,"217":0,"218":0,"219":0,"220":0,"221":19,"222":12,"223":3536,"224":3,"225":0,"226":2,"227":1,"228":73,"229":3536,"230":1,"231":9,"232":5,"233":20,"234":6,"235":54,"236":0,"237":73,"238":54,"239":6,"240":76,"241":10,"242":1,"243":0,"244":0,"245":2,"246":5,"247":5,"248":32,"249":5,"250":0,"251":4,"252":0,"253":1,"254":3,"255":55,"256":73,"257":150,"258":0,"259":0,"260":1,"261":157,"262":3,"263":1,"264":32,"265":2,"266":10,"267":0,"268":0,"269":5,"270":2,"271":2,"272":0,"273":6,"274":1,"275":3,"276":0,"277":157,"278":2,"279":1,"280":9,"281":1,"282":0,"283":14,"284":14},"Model sha":{"0":"fda5cf998a0f2d89b53b5fa490793e3e50bb8239","1":"ea2b4ff8e5acd7a48993f56b2d7b99e049eb6939","2":"ea2b4ff8e5acd7a48993f56b2d7b99e049eb6939","3":"40d451f32b1a6c9ad694b32ba8ed4822c27f3022","4":"1f302e0e15f3d3711778cd61686eb9b28b0c72ae","5":"84d38e29fec0dc9c274237968fdafe9396702f9b","6":"54a8c35600ec5cb30ca2129247854ece23e57f57","7":"4df251a558c53b6b6a4c459045b161951cfc3c4e","8":"c64edea08b27be1e7e2ae6a95bcdd74849cb887e","9":"cd29cfa124072c96ba8601230bead65d76e04dcb","10":"ba3403eaafc6d1f6e3a73245314ee96025c08d96","11":"e8e558b5fd4ac9da839577b1295d10ca75fc2663","12":"2d8cff968dbfb31e0c1ccc42053ccc4d2698a390","13":"915651208ea9f40c65a60d1f971a09f9461ee691","14":"7dd3ddea090bd63f3143e70d7d6237cc40c046e4","15":"e1cdc5b02c662c5f29a50d0b22c64a8902ca856b","16":"6c7ec6d2ca1c0d126a26963fedc9bbdf5210b0d1","17":"dc092ecc5d5a424678eac445a9f4443069776691","18":"76389d5d825c3743cc70bc75b902bbfdad11beba","19":"7b74a95019f01b59630cbd6469814c752d0e59e5","20":"097b951c2524e6113252fcd98ba5830c85dc450f","21":"95b3b4e432d98b804d64cfe42dd9fa6b67198e5b","22":"3880710724abcaffbdf8fa4031e1d02066fbfe9d","23":"74c6e4fbd272c9d897e8c93ee7de8a234f61900f","24":"96c62ad90f2b82016a1cdbfe96cfa5c4bb278e21","25":"c5575550053c84a401baf56174cb2e5d5bd9e79a","26":"d3efc551679d7ec00da14722d44151c948a48d25","27":"d8d6a47f877fee3e638a158c2bd637c0013ed4e4","28":"06fd0e293aeb3b2722e3910daefcd185fad4558c","29":"331bb6bdba4140bbf0031bd37076f2c8a76d7dbb","30":"bbaef291e93a7f6c9f8cb76a4dbd8c3c054d3f3c","31":"a4ca706d1bbc263b95e223a80ad68b0f125840b3","32":"f136ec75c9fb7c86c071291ddf418089c8f43da0","33":"bbaef291e93a7f6c9f8cb76a4dbd8c3c054d3f3c","34":"cd8bfad664fb7f9b017388d974dd3265f8c40396","35":"ff261dadc107d0ce67b836a052d7131f9d9e4260","36":"5efde29924cf7158e4cbd642311a92a14e85597c","37":"a283f4e8169009d683b329ae1a96c9a77ce5936a","38":"cb04e33c4ff559b31767765100cd50c24ec2531c","39":"1dc4edde961960f7263dc3bdd37ca9e9f7e451ea","40":"03405145ca06170f1b2e0acc838f573f0e090df8","41":"0da1865ae1ce682d4002dd9935d20520e79ed520","42":"a27e0dfaf79af8da32fc4ff6c5eb8be46c9f5a13","43":"a27e0dfaf79af8da32fc4ff6c5eb8be46c9f5a13","44":"b7f5aa8d4c899c175a1dad40a03b4071df90bd8e","45":"69b9280ee4d2a20ef5645798621e62dd9777c139","46":"4774173a54be9a648e1cf03248af3ae3d51a0434","47":"11a51df04f85047e166d63eb64cedc1ec02732a1","48":"ff261dadc107d0ce67b836a052d7131f9d9e4260","49":"1e1cd6e84d02a9c1d70c2a2037f485bc2b646391","50":"22a9da7289d20a1d5452f77aa5bc49e97344af52","51":"cd343f0846ceb4180297920b2da50d6b28dcb242","52":"6ba7b5acb65dd62c28585cba298e0d3671c14f3a","53":"2b81b03b242a548e54e9e10af6a4c24f24a4c5fc","54":"f92057866ff68bf215487d34ca1080707bb4e98c","55":"c00b0fa78ab41aec778209fdf7640ebbe6d83065","56":"3d0181b920304bca0bdfd41aff55188a574c85e3","57":"b1ec306bf85762b28ce29ac71924bb9a8fa01e5a","58":"af1576f357ce8c5c3ee2e8bda45f8ffd7e0535f0","59":"25c0f5c1edad0ed1ab02347adf02fe03e0a3b62a","60":"546cdd443abc56b48aaadb4ebb5fb9249015f0bb","61":"aa6e42036cea01cb99426a9333481b353fd36e61","62":"dd1a314a04b8b4faf33e7d5037a71246d3e65bad","63":"ec27a21a66dc4411f24f36d585787853ba2e6354","64":"d718acb1b95c85009db8dd34af1318bcaf23ebcd","65":"aa3528c04c38fa49b5b65e1d064c46db3e9774f1","66":"ff89febead2585b2a1efae12b53887b18c283a8a","67":"87382cefca257137b983fd01d0e6a8839704d75e","68":"d696e99a2a4eeb13994c277f2fb113e9ddd1e632","69":"a2c3a87dd53a87dc9fc622ce4ddbb05d3e9cf6a9","70":"a0d648c1bcc3f1615bb2f0a94c6d32e7abde355d","71":"099b9c3e105fbb579d561fe93174ae3bd75dac8d","72":"5c649b6bbb8aa16d52dda26c5ce8574d1c7a3274","73":"0c9f2823a900408cf3c70c532288f89e452067f7","74":"7637cbf40c746030910154e0b344c5358f35a878","75":"7e26287665e6214be131f4e7ee20a312a07a4c1c","76":"bfba5a5114005c849a49662b4c7e53debac98105","77":"b7174ccf5c91095737cdb29f50853512017a1ac4","78":"c9beb3cba8030cb4fe7d96dd513c9e7ab40da126","79":"bff2cd7ba1f8a742cd22cd9df22485636c3b6410","80":"783a2f1231542b9fe8bc728dc676745c62f35b9f","81":"67f6f5ea337547b3f5e287e0ed1392ef0462e65a","82":"5b806671b663295f5212704dfb7373ddfefe804f","83":"36a9851b8c9213c4e1bcfd2c46b3f799c36caa69","84":"a619fd0fcbdfcc897054491c2f285677bee38a11","85":"46afad714b0528863bcf67b2bf5fcd4318235ccf","86":"a5965f77bbb0fe23f16a5137918af27c753800af","87":"30e44c452e38ff3d879d7ba92a130fa2cc072754","88":"34e3f31067be2bcbf86c8af9d137db227b2ece20","89":"c241960b69943b3d32b8af110bbed20508265334","90":"ff7d668721b961a73a95098cf7436db0170b1db6","91":"1a8a1ce0ceea0e298d9c8d5cce0b869a4a8c0514","92":"2491f12e51d7b74fb47ef5480d4b5f547d4d19ea","93":"cc18e2b0b9764f255341d3e530d018545987544b","94":"2f83b45077479bc3f663da50c4c40372894bf92e","95":"72f6e05eddabe6f3fa8891c99c4ba02aa60158c1","96":"98fdc8315906b0a8b9e7f24bad89914869fcfc20","97":"bd9ac169a0d6acb8fb66d55a6471ef162271b248","98":"583254a5a134243d7793b311c465da12b10a3ff2","99":"a89b5a4ce482c531b1cb3b8703e8eb2b9321994c","100":"ead4b4aedf94b98916f30388b85620a3583375e8","101":"cf06159aaaadda2ca50b19ce547a52424f7d47c3","102":"e02a631564990af3d9c8b0232f979af11cd8b6f6","103":"d6024b97f624e9169a63f5faccb8c5ab121eb13a","104":"ea5855b529987fde6eca87492bccbd28eef8d052","105":"fbf1c9958c47062e2db30276c723867c0d019652","106":"644f20245c08dbbc6baad20100fcf0c8bd3181a0","107":"e01fb197b4303ba63ba2f4d68a897006ec7ec4fd","108":"ebc7cba80494385e29bce8b1b86a75d14666c19e","109":"cf06159aaaadda2ca50b19ce547a52424f7d47c3","110":"1442ca4e728892f18ef101c4987bdf11ef5bbae5","111":"c8ad8ee000e4e042d80e4cf53fb6d0815d7743dd","112":"50dd207ce4319397d862a91f8295d902549dbdf7","113":"031e9404b7a1467fdcc96bc109e05b640d573209","114":"a892e9a26785d59d8bf4ccef48606664c6cbc48b","115":"2d197f7a290d191183b86f35c3857dd15a16d9b6","116":"513311818a707ccc0c7d007ddabfab19e1a2e470","117":"2d197f7a290d191183b86f35c3857dd15a16d9b6","118":"e13a48ef1524ba35615d7f63834e7c9192fa1836","119":"5ecc835f4137adac99198831c61c2afff4f340cf","120":"0dc1f9340fac9aadf883f52e6409e49e8d286af6","121":"d187b7bd6757d78bf89aaad8b0b5834ddbf29392","122":"2e36c528223443d6b8b5203b6a013e79f6d78d09","123":"95d031f0cad065bc18387f09ce37b256756f762f","124":"8a741a32e8d1d426c408c3eeb208eccc172c655e","125":"16e9d47cb25c33d57328638e5c56e257c6021ce1","126":"543b52f9b6c96a4922dc8ed1251625b1bd919e19","127":"0c670c988b61240e5f89ae9df0820db7dc572576","128":"c3227c2b48ac6b136c074871b72088677f2adca9","129":"ccd128942c5a6bb1672ceed21730d0e172655d77","130":"4c09542d0154eb09bf7be874e2c68189407114ed","131":"207cddf327154c23b484f1cbd972b3c7989b7554","132":"08903c93d929829aabbde2681c7ad2465d7d4189","133":"fcc6ada5ea6dbf2f644d26b545ac402d2202cc74","134":"6df7bb2069432bcab0971ab105284a66b3ec1ce0","135":"66bf25995056155b5d0796f7c0981e243bdd48f3","136":"448255ff2397e04c62ecba4c4d982531eb42d241","137":"f1d27aab09086a6e691db6892d50ba809cbe0607","138":"4bfad083e96a4ab129cc202fc941994be2e3adc4","139":"e5dd511955f4ac65bb1884f07426157740ad8574","140":"fcc6ada5ea6dbf2f644d26b545ac402d2202cc74","141":"645fa936256811f53f0c33f1e5298f6ad1095dce","142":"96b27c4205881920289b29ac3d83ba5edf5cf672","143":"1d697d32ba4f6ed471cd2857669029f425b827bb","144":"6bf661cdade79d96c4def4f09c27ad5ca1bae11a","145":"de574b57d45cfea00748c464af17f1c1ca53e548","146":"43ea8d27d652dc15e4d27f665c5d636a5937780b","147":"750463cf0946dd46c4504b302757f2bb6e2b4521","148":"78da696445e50002d29bf5610af059fd3f00f51b","149":"02a497125bbf85fe0355eb22424315c920d1aec4","150":"0d04b46ec6ce4c707bcdebb94b98e30fe8f4ae1d","151":"9c95dafc63de0e98627458369e87347df87fa17d","152":"875319a815400bdb73c309601c175d72997a4fa0","153":"8b9615124a0bcadd7fa984eaadd066da0fb4fbae","154":"d3167df97a44b8632538b32ee8cd887893ea1435","155":"08d3f07da7160e9657630ba98531850905619def","156":"669f8f726fac4a588ced06a4da3959eb8ca20f9f","157":"8761e9acb20bc475c095455fd754bf632e0f88f0","158":"c2519bf48d73f5751cfecfe2c4c796fbcb73c390","159":"31bbd3c348400c942a33c1f952dca8e7125996b7","160":"8761e9acb20bc475c095455fd754bf632e0f88f0","161":"0a25a243957b41c7ac8d59af50294547151ae621","162":"c03dfcda5d45ea4c518bd14641d9604726e00477","163":"c63d06344214886094d7ab6c7fd5692cc59fdf0d","164":"b47d17b0df02e38e97f565784bb3cf948b29a6ec","165":"514783cefac2b142adb50ee5f61dd724d62910cf","166":"0cce8842b179e19e6faac936a8c44ea1ba05b6b9","167":"d933dcd7cbb19916f4732ae7e3892a656a8c3d27","168":"df83494a4366e081563659e1142464029a0dec82","169":"72084679bda2e7679259e9c0fa2fdcd48ecb158c","170":"b59ed47a8f30e7488f1faef65ff0a75597af0a44","171":"f2a7ecb1f539bb41a61c254150e404820851005f","172":"73b5302f1efc7ba87e123cfed0c9c998e098c16a","173":"8483318133a7763eb2dedc59294559febbf657c9","174":"db160d4bc5bd9f2e66a764aeb44dcd18fb8afa6d","175":"e4b4ee3d952b1e8360a82d2b3506fd5b4ab68df9","176":"0ab5c875f0070d5aee8d36bc55f41de440a13f02","177":"479d3907a5bce4f3edb476d3ae05fe4b38a0a6e4","178":"deb99d98742ec9691ef593418bea71a4437745a1","179":"efb6caff9804383600563a658ba18720ec3b2d11","180":"dc7dfbece1b31665b0456476f67ef97a17bd2323","181":"515d7d948b4274c7451fdef61eae9e76eac93a38","182":"f62c59844a8de3c27cf22735218d77e9fa9f6b17","183":"fff356f1e506e6801c5a60c165636e84a4bd302c","184":"0219ef0ce5c8aaa6abe5e6c30f287edb777c7e8c","185":"45b93bfc4297b0bc1ef0b7316cbae11d2bb527d1","186":"b79854d1c29b5caae403c29d484f969b31734a5e","187":"e17d301fb143b20ac943c99f34aa8b118f14e1e0","188":"9311a4300f61f4cba381ba8347b73f0f2977a8f9","189":"f4bfa8b298cbd0acc236117231d5b00de5f43240","190":"323fba03ac21b03df8d04ab575741429cc509d7b","191":"a3798e202aaa326b1027c0ee0a61ac78dc175e63","192":"aedfd66841e39a8db181d8549a42f4d2ee248b0a","193":"689dbca3e4bd977fa08b7a933e4e709277cd1394","194":"fa406117c67fc86cc8171f57b12184eecb8069be","195":"0121c0f6d769e8c0ecafeae0e85092855a4e95c9","196":"566cdea53950f86eb51dae62812c29e79405cffe","197":"755d702c80e5acee8c07676b4a4dee37de56e2a8","198":"af9757f0420e27e2a332cc16cbe1eeefe99cb5f1","199":"6188e34517a82298b0216c141ec728a5d9861658","200":"2dda3515c5bbf02824addbe2e8f924a48ce21156","201":"01f1a7861667c4869bb03251dfd10526bf846e9c","202":"94bad5a6b469d84f556d6cc52c44fd88c07476f3","203":"d26e345f256b8a8210637258a5973fd36227d8ec","204":"2d5b9af81408ebc5e45c944cc24c9bab85b7ae1f","205":"9f9e4ae1c294ea4301eeefd3cf6222d156916144","206":"6ba531f1aec62375bf94ad9c7bb064953c4e9868","207":"6ee0b7c59743c3047f307643c7c1f13ada56fdd1","208":"b2535b271d83f892de2fb3a790b298618565dcff","209":"1f8562051647d5537dc950315e74534b363a0812","210":"9cb2d09228ac87d761d23a1284c79b55f9f285d9","211":"cc2f19f5bc9ad693d4447e42e9844d9931ab8e81","212":"5e194e1e44c6c2ebe294f854733f5c5532de5688","213":"330eb185920d6a470b265a4b31217c60e810fb3e","214":"cb9c9b0fb1d49b085069617bd8dc9cdddfdba7fb","215":"9893dc24b32bc83ca63e7d06cfa296d66be3fb3d","216":"cca19971f846c6d45e089dd1425f86fa4cb48f0a","217":"5cf11f6e983a7c11b17c1b7c4aee6ff99e30ba82","218":"a4400d021e29279c8676d5c46cf76c4b36d748f6","219":"a388bd7af444f632e5e9370bedaeb69572f861af","220":"cbf9c2350f24d9d10ebb1961965e7fbb4361cafb","221":"330eb185920d6a470b265a4b31217c60e810fb3e","222":"86fd0b7d132126be49c02e061ebec02e1d3a4e38","223":"125c431e2ff41a156b9f9076f744d2f35dd6e67a","224":"c9fb38c846ba1f1ce9a7a3560e491ea9d4a8d875","225":"618e5aedf02d58358d6fda7d9fa67c169b7156d0","226":"96e7c78544d7eca96e3ae60ff80c728f3109e8ba","227":"05a9ef37686d678f267a15664b5ce66612b7996a","228":"afc90bd0690d0cbedd01f22d1d6ef0e44f30b5f4","229":"3de0408ae8b591d9ac516a2384925dd98ebc66f4","230":"adf7c513e9cadbe25cc2be61c43f3f36f1b488e9","231":"7bd8487fc3a5c3bac022bfe8c34d2f630c123d40","232":"1b3a5c98381f37a2ec97ce80d1d88d472a7d1802","233":"61822ea65b8a4c56d2b5622e2adf69e430fac29a","234":"e91dcc46d28fc0aa5553fb73c4eac5e28abfd3ec","235":"f55aef05f6632a1407fcddcbc6729613b07e87e2","236":"e593de223b662cfda40aa96163c6a42d6b32de5e","237":"afc90bd0690d0cbedd01f22d1d6ef0e44f30b5f4","238":"d7d33a1517c57b596162a71a48bc29c87d29d9aa","239":"5dbc14842c16f1fa315e682e7e5bdb0248a2b05e","240":"91e0a33fd2cb0a77401831e96536b91c5b7817e4","241":"c3caef28f8402d52d6a646a7e1e00a971db1c507","242":"b7cd9398c8797b4e90cdd90ec9f64300e6334e6a","243":"5aeed89b3b0eba74cea863b59a43c63c81be5989","244":"382698efc4e5ff54a4155e1f2c40547ac3b2aa64","245":"786e6492919d0d1eb07b5988f67e0ee61aa05c21","246":"f7605af56f29b42e72f9c2cbbd4ad8e443a8dae0","247":"e2a033646231bd947a3948d3aac198d34d04ea38","248":"bff7146aafe1a5b84631bd279112c8c5b95d2802","249":"4e21eea3c32d00b2fcfc5bcfd16d8dc9d0d8874d","250":"9bfa05ff62ddd960cb9fb3e9dff70d800ea1c0a1","251":"02e11fa83d18975f95c5d5047d0439897308c73b","252":"dfc889db0d02cebaadacc6726a8622a40f45eb5e","253":"03955e2748064dcfac121e35e4e060cf6f48e259","254":"bf7696c10077e73d06752c564ea35cc7e5e336ca","255":"6011e2ef7791738f3b78fa9e122360029df7c9ed","256":"f16df07e24654858a6b04c3ecb0670dcfc42337d","257":"f4f3f6144dd143d6ec43ece9ab0fdd740ed610f1","258":"e402c5ea1ba23d776062f18306690296a708d469","259":"d9bb402315f47764bf0f6002e513cd7e89c7c804","260":"e4ba7abdb25b00308f67589458cb9380a2ccd5e6","261":"79648bef7658bb824e4630740f6e1484c1b0620b","262":"72267d131001da8cdf253105c367fd913db79523","263":"3120e204e1b4928fd784ae78fa754bc937352c98","264":"26923a2648b9864e2ec6f0cc66b8b6fcfbbdd491","265":"7e3fdb60969ef0f7219cbcb9b05f7d1537af1c8d","266":"66abec7cba89b35c7b6cab2140c3532049de0157","267":"04cd413008c353ca558ab901c0d88132c25772c2","268":"957474e32057f19ef863c1c8ba3d16389cf58eed","269":"d1bdf5a5ea942b8236e48c17c3c07e3bd49ae5c8","270":"59004f5610548e626ad27cd4a7b92daa3ccfc9c8","271":"5f18e24665f62b8e9a3492af247978073fea54f9","272":"17af425d904f21f8500bf965b16d07603e01d125","273":"e3d7c73110dd6edd9e96b1f3d9b0dea91d83ce2d","274":"e2164a4cce391e1f4228e2e89689793ec037135e","275":"fb53c42ba7d5719e730f67c5356766d84e5f3619","276":"1cf1b3a313de0b3b22a61dd3741c1bd5a3d14c66","277":"79648bef7658bb824e4630740f6e1484c1b0620b","278":"cf6f466d227c041df3b892dff394df43ecf99b8b","279":"dd27a200fd3dd5500a0b5bbfc0e4a9289af486e5","280":"2d6dcf8bf9f1a758f135929de4a6fd81e26a38da","281":"7152f2dc8e0aceb0412e802653271cd9e59bf23e","282":"855035b23011e2a09182025a63a9252e19033163","283":"1055563879363d9ee2fba1d9fd1628eca6bcbb4e","284":"c8741ec6f4f24324a96041efaf2f627a99d946e6"},"model_name_for_query":{"0":"davidkim205\/Rhea-72b-v0.5","1":"MTSAIR\/MultiVerse_70B","2":"MTSAIR\/MultiVerse_70B","3":"SF-Foundation\/Ein-72B-v0.11","4":"SF-Foundation\/Ein-72B-v0.13","5":"SF-Foundation\/Ein-72B-v0.12","6":"abacusai\/Smaug-72B-v0.1","7":"ibivibiv\/alpaca-dragon-72b-v1","8":"moreh\/MoMo-72B-lora-1.8.7-DPO","9":"cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO_f16","10":"saltlux\/luxia-21.4b-alignment-v1.0","11":"cloudyu\/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO","12":"zhengr\/MixTAO-7Bx2-MoE-v8.1","13":"yunconglong\/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B","14":"JaeyeonKang\/CCK_Asura_v1","15":"fblgit\/UNA-SimpleSmaug-34b-v1beta","16":"TomGrc\/FusionNet_34Bx2_MoE_v0.1","17":"migtissera\/Tess-72B-v1.5b","18":"moreh\/MoMo-72B-lora-1.8.6-DPO","19":"abacusai\/Smaug-34B-v0.1","20":"cloudyu\/Truthful_DPO_TomGrc_FusionNet_34Bx2_MoE","21":"ibivibiv\/orthorus-125b-v2","22":"ConvexAI\/Luminex-34B-v0.2","23":"yunconglong\/DARE_TIES_13B","24":"yunconglong\/13B_MATH_DPO","25":"TomGrc\/FusionNet_34Bx2_MoE","26":"ConvexAI\/Luminex-34B-v0.1","27":"yunconglong\/MoE_13B_DPO","28":"JaeyeonKang\/CCK_Asura_v3.0","29":"cloudyu\/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO","30":"yam-peleg\/Experiment26-7B","31":"MTSAIR\/multi_verse_model","32":"chihoonlee10\/T3Q-Mistral-Orca-Math-DPO","33":"yam-peleg\/Experiment26-7B","34":"rwitz\/experiment26-truthy-iter-0","35":"yam-peleg\/Experiment30-7B","36":"yam-peleg\/Experiment28-7B","37":"MaziyarPanahi\/Calme-7B-Instruct-v0.2","38":"rwitz\/experiment26-truthy-iter-1","39":"rwitz\/experiment26-truthy-iter-2","40":"chlee10\/T3Q-Merge-Mistral7B","41":"LeroyDyer\/Mixtral_AI_Cyber_3.m1","42":"yam-peleg\/Experiment31-7B","43":"yam-peleg\/Experiment31-7B","44":"yam-peleg\/Experiment24-7B","45":"zhengr\/MixTAO-7Bx2-MoE-Instruct-v7.0","46":"bobofrut\/ladybird-base-7B-v8","47":"yam-peleg\/Experiment29-7B","48":"yam-peleg\/Experiment30-7B","49":"CorticalStack\/pastiche-crown-clown-7b-dare-dpo","50":"MaziyarPanahi\/Calme-7B-Instruct-v0.1.1","51":"mlabonne\/UltraMerge-7B","52":"cloudyu\/Truthful_DPO_cloudyu_Mixtral_34Bx2_MoE_60B","53":"yam-peleg\/Experiment27-7B","54":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO","55":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2","56":"cloudyu\/Yi-34Bx2-MoE-60B-DPO","57":"chihoonlee10\/T3Q-EN-DPO-Mistral-7B","58":"jefferylovely\/AiMaven-Merkaba-7b","59":"bardsai\/jaskier-7b-dpo-v5.6","60":"JaeyeonKang\/CCK_Asura_v2.1","61":"yam-peleg\/Experiment25-7B","62":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v3","63":"CorticalStack\/neurotic-crown-clown-7b-tak-stack-dpo","64":"jan-hq\/stealth-v2","65":"bardsai\/jaskier-7b-dpo-v6.1","66":"eren23\/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v4-test","67":"chihoonlee10\/T3Q-DPO-Mistral-7B","68":"Kukedlc\/Jupiter-k-7B-slerp","69":"moreh\/MoMo-72B-lora-1.8.4-DPO","70":"TomGrc\/FusionNet_7Bx2_MoE_v0.1","71":"macadeliccc\/MBX-7B-v3-DPO","72":"vicgalle\/CarbonBeagle-11B-truthy","73":"dddsaty\/FusionNet_7Bx2_MoE_Ko_DPO_Adapter_Attach","74":"vicgalle\/RoleBeagle-11B","75":"MaziyarPanahi\/Calme-7B-Instruct-v0.5","76":"FelixChao\/Capricorn-7B-DPO","77":"rwitz\/experiment26-SPIN-iter-0","78":"Kukedlc\/NeuralKrishna-7B-V2-DPO","79":"abideen\/AlphaMonarch-laser","80":"touqir\/Cyrax-7B","81":"Eric111\/UltraCatunaMayo-DPO","82":"abideen\/AlphaMonarch-daser","83":"yam-peleg\/Experiment21-7B","84":"TomGrc\/FusionNet_7Bx2_MoE_14B","85":"yam-peleg\/Experiment22-7B","86":"cloudyu\/Yi-34Bx2-MOE-200K","87":"NeverSleep\/MiquMaid-v2-2x70B-DPO","88":"abideen\/AlphaMonarch-dora","89":"AiMavenAi\/Prometheus-1.3","90":"FelixChao\/Capricorn-7B","91":"yleo\/EmertonMonarch-7B","92":"yam-peleg\/Experiment20-7B","93":"ibivibiv\/multimaster-7b-v6","94":"abacusai\/Smaug-Mixtral-v0.1","95":"daxiongshu\/Pluto_24B_DPO_63","96":"abacusai\/Smaug-Mixtral-v0.1","97":"cloudyu\/Phoenix_DPO_60B","98":"Weyaxi\/Helion-4x34B","99":"ibivibiv\/multimaster-7b-v4","100":"fblgit\/UNA-34BeagleSimpleMath-32K-v1","101":"ShinojiResearch\/Senku-70B-Full","102":"fblgit\/UNA-34Beagles-32K-v1","103":"one-man-army\/UNA-34Beagles-32K-bf16-v1","104":"FelixChao\/Scorpio-7B","105":"vicgalle\/ConfigurableBeagle-11B","106":"Weyaxi\/Cosmosis-3x34B","107":"macadeliccc\/WestLake-7B-v2-laser-truthy-dpo","108":"yam-peleg\/Experiment19-7B","109":"ShinojiResearch\/Senku-70B-Full","110":"yam-peleg\/Experiment23-7B","111":"maywell\/kiqu-70b","112":"FelixChao\/WestSeverus-7B-DPO-v2","113":"migtissera\/Tess-70B-v1.6","114":"alnrg2arg\/test3_sft_16bit","115":"FelixChao\/Faraday-7B","116":"Weyaxi\/Astralis-4x34B","117":"FelixChao\/Faraday-7B","118":"PetroGPT\/WestSeverus-7B-DPO","119":"FelixChao\/Sectumsempra-7B-DPO","120":"NeverSleep\/MiquMaid-v1-70B","121":"Weyaxi\/Bagel-Hermes-2x34B","122":"BarryFutureman\/WestLakeX-7B-EvoMerge-Variant2","123":"ibivibiv\/multimaster-7b-v5","124":"alnrg2arg\/test3_sft_16bit_dpo2","125":"ChaoticNeutrals\/Eris_Floramix_DPO_7B","126":"abhishekchohan\/SOLAR-10.7B-Instruct-Forest-DPO-v1","127":"abacusai\/MetaMath-Bagel-DPO-34B","128":"cognitivecomputations\/WestLake-7B-v2-laser","129":"cloudyu\/60B_MoE_Coder_v3","130":"ChaoticNeutrals\/Eris_Remix_DPO_7B","131":"Eric111\/CatunaLaserPi-DPO","132":"jondurbin\/nontoxic-bagel-34b-v0.2","133":"jondurbin\/bagel-dpo-34b-v0.2","134":"senseable\/WestLake-7B-v2","135":"moreh\/MoMo-72B-LoRA-V1.4","136":"MaziyarPanahi\/Calme-7B-Instruct-v0.4","137":"kevin009\/llamaRAGdrama","138":"vicgalle\/Mixtral-7Bx2-truthy","139":"moreh\/MoMo-72B-LoRA-V1.4","140":"jondurbin\/bagel-dpo-34b-v0.2","141":"senseable\/Westlake-7B","142":"rizla\/trrapi-16b","143":"abacusai\/MM-OV-bagel-DPO-34b-c1000-250","144":"BarryFutureman\/WestLakeX-7B-EvoMerge","145":"yunconglong\/Truthful_DPO_MOE_19B","146":"Kukedlc\/NeuralExperiment-7b-MagicCoder-v7.5","147":"ResplendentAI\/Datura_7B","148":"FelixChao\/Patronum-7B","149":"bhavinjawade\/SOLAR-10B-OrcaDPO-Jawade","150":"ResplendentAI\/Flora_DPO_7B","151":"ResplendentAI\/Flora_7B","152":"NeuralNovel\/Valor-7B-v0.1","153":"VAGOsolutions\/SauerkrautLM-SOLAR-Instruct","154":"upstage\/SOLAR-10.7B-Instruct-v1.0","155":"fblgit\/UNA-SOLAR-10.7B-Instruct-v1.0","156":"bhavinjawade\/SOLAR-10B-Nector-DPO-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..ce0d46e9181ea60262a6b50d8f5c597e1154dff1 --- /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": ["# type: ignore\n", "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()\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(css=str(abs_path / \"assets/custom_css.css\"))\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..c625bbd5bfd1f9f19274e6e057b57b673c8fd062 --- /dev/null +++ b/demos/mini_leaderboard/run.py @@ -0,0 +1,237 @@ +# type: ignore +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() +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(css=str(abs_path / "assets/custom_css.css")) diff --git a/demos/model3D/run.ipynb b/demos/model3D/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..969144681fe39d22afe2821924137402051f3bcb --- /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": ["import gradio as gr\n", "# get_model3d() returns the file path to sample 3D models included with Gradio\n", "from gradio.media import get_model3d, MEDIA_ROOT\n", "\n", "\n", "def load_mesh(mesh_file_name):\n", " return mesh_file_name\n", "\n", "\n", "demo = gr.Interface(\n", " fn=load_mesh,\n", " inputs=gr.Model3D(label=\"Other name\", display_mode=\"wireframe\"),\n", " outputs=gr.Model3D(\n", " clear_color=(0.0, 0.0, 0.0, 0.0), label=\"3D Model\", display_mode=\"wireframe\"\n", " ),\n", " examples=[\n", " [get_model3d(\"Bunny.obj\")],\n", " [get_model3d(\"Duck.glb\")],\n", " [get_model3d(\"Fox.gltf\")],\n", " [get_model3d(\"face.obj\")],\n", " [get_model3d(\"sofia.stl\")],\n", " [\n", " \"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat\"\n", " ],\n", " [\n", " \"https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply\"\n", " ],\n", " ],\n", " cache_examples=True,\n", " api_name=\"predict\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch(allowed_paths=[str(MEDIA_ROOT)])\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demos/model3D/run.py b/demos/model3D/run.py new file mode 100644 index 0000000000000000000000000000000000000000..dbe261e1e3feb3ee44644d7a54cb3b218faf8ef6 --- /dev/null +++ b/demos/model3D/run.py @@ -0,0 +1,34 @@ +import gradio as gr +# get_model3d() returns the file path to sample 3D models included with Gradio +from gradio.media import get_model3d, MEDIA_ROOT + + +def load_mesh(mesh_file_name): + return mesh_file_name + + +demo = gr.Interface( + fn=load_mesh, + inputs=gr.Model3D(label="Other name", display_mode="wireframe"), + outputs=gr.Model3D( + clear_color=(0.0, 0.0, 0.0, 0.0), label="3D Model", display_mode="wireframe" + ), + examples=[ + [get_model3d("Bunny.obj")], + [get_model3d("Duck.glb")], + [get_model3d("Fox.gltf")], + [get_model3d("face.obj")], + [get_model3d("sofia.stl")], + [ + "https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/bonsai/bonsai-7k-mini.splat" + ], + [ + "https://huggingface.co/datasets/dylanebert/3dgs/resolve/main/luigi/luigi.ply" + ], + ], + cache_examples=True, + api_name="predict", +) + +if __name__ == "__main__": + demo.launch(allowed_paths=[str(MEDIA_ROOT)]) diff --git a/demos/native_plots/bar_plot_demo.py b/demos/native_plots/bar_plot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..23bddf6e280bf68f7f1ed5fa0ba461b9c417adcc --- /dev/null +++ b/demos/native_plots/bar_plot_demo.py @@ -0,0 +1,80 @@ +import gradio as gr +from data import temp_sensor_data, food_rating_data # type: ignore + +with gr.Blocks() as bar_plots: + with gr.Row(): + start = gr.DateTime("2021-01-01 00:00:00", label="Start") + end = gr.DateTime("2021-01-05 00:00:00", label="End") + apply_btn = gr.Button("Apply", scale=0) + with gr.Row(): + group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by") + aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation") + + with gr.Draggable(): + temp_by_time = gr.BarPlot( + temp_sensor_data, + x="time", + y="temperature", + buttons=["export"], + ) + temp_by_time_location = gr.BarPlot( + temp_sensor_data, + x="time", + y="temperature", + color="location", + buttons=["export"], + ) + + time_graphs = [temp_by_time, temp_by_time_location] + group_by.change( + lambda group: [gr.BarPlot(x_bin=None if group == "None" else group)] * len(time_graphs), + group_by, + time_graphs + ) + aggregate.change( + lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs), + aggregate, + time_graphs + ) + + def rescale(select: gr.SelectData): + return select.index + rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) + + for trigger in [apply_btn.click, rescale_evt.then]: + trigger( + lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs + ) + + with gr.Row(): + price_by_cuisine = gr.BarPlot( + food_rating_data, + x="cuisine", + y="price", + buttons=["export"], + ) + with gr.Column(scale=0): + gr.Button("Sort $ > $$$").click(lambda: gr.BarPlot(sort="y"), None, price_by_cuisine) + gr.Button("Sort $$$ > $").click(lambda: gr.BarPlot(sort="-y"), None, price_by_cuisine) + gr.Button("Sort A > Z").click(lambda: gr.BarPlot(sort=["Chinese", "Italian", "Mexican"]), None, price_by_cuisine) + + with gr.Row(): + price_by_rating = gr.BarPlot( + food_rating_data, + x="rating", + y="price", + x_bin=1, + buttons=["export"], + ) + price_by_rating_color = gr.BarPlot( + food_rating_data, + x="rating", + y="price", + color="cuisine", + x_bin=1, + color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"}, + buttons=["export"], + ) + +if __name__ == "__main__": + bar_plots.launch() diff --git a/demos/native_plots/data.py b/demos/native_plots/data.py new file mode 100644 index 0000000000000000000000000000000000000000..52aef7942fc213ebda2eeae466a1d397c3a55256 --- /dev/null +++ b/demos/native_plots/data.py @@ -0,0 +1,20 @@ +import pandas as pd +from random import randint, random + +temp_sensor_data = pd.DataFrame( + { + "time": pd.date_range("2021-01-01", end="2021-01-05", periods=200), + "temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)], + "humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)], + "location": ["indoor", "outdoor"] * 100, + } +) + +food_rating_data = pd.DataFrame( + { + "cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)], + "rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)], + "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)], + "wait": [random() for i in range(100)], + } +) diff --git a/demos/native_plots/line_plot_demo.py b/demos/native_plots/line_plot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..de89bf9252909b23014fa5d389377d3c63ceaee1 --- /dev/null +++ b/demos/native_plots/line_plot_demo.py @@ -0,0 +1,75 @@ +import gradio as gr +from data import temp_sensor_data, food_rating_data # type: ignore + +with gr.Blocks() as line_plots: + with gr.Row(): + start = gr.DateTime("2021-01-01 00:00:00", label="Start") + end = gr.DateTime("2021-01-05 00:00:00", label="End") + apply_btn = gr.Button("Apply", scale=0) + with gr.Row(): + group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by") + aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation") + + temp_by_time = gr.LinePlot( + temp_sensor_data, + x="time", + y="temperature", + buttons=["export"], + ) + temp_by_time_location = gr.LinePlot( + temp_sensor_data, + x="time", + y="temperature", + color="location", + buttons=["export"], + ) + + time_graphs = [temp_by_time, temp_by_time_location] + group_by.change( + lambda group: [gr.LinePlot(x_bin=None if group == "None" else group)] * len(time_graphs), + group_by, + time_graphs + ) + aggregate.change( + lambda aggregate: [gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs), + aggregate, + time_graphs + ) + + def rescale(select: gr.SelectData): + return select.index + rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) + + for trigger in [apply_btn.click, rescale_evt.then]: + trigger( + lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs + ) + + price_by_cuisine = gr.LinePlot( + food_rating_data, + x="cuisine", + y="price", + buttons=["export"], + ) + with gr.Row(): + price_by_rating = gr.LinePlot( + food_rating_data, + title="Price by Rating (for Ratings >2)", + x="rating", + y="price", + x_lim=[2, None], + buttons=["export"], + ) + price_by_rating_color = gr.LinePlot( + food_rating_data, + title="Price by Rating (for Ratings <4)", + x="rating", + y="price", + x_lim=[None, 4], + color="cuisine", + color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"}, + buttons=["export"], + ) + +if __name__ == "__main__": + line_plots.launch() diff --git a/demos/native_plots/requirements.txt b/demos/native_plots/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe1f1ac6ca525551f0b8ff0acaa7b3a28a5f8e37 --- /dev/null +++ b/demos/native_plots/requirements.txt @@ -0,0 +1,2 @@ +vega_datasets +pandas diff --git a/demos/native_plots/run.ipynb b/demos/native_plots/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8e90ce32f170007dcec186be8e72871866b3e1d3 --- /dev/null +++ b/demos/native_plots/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: native_plots"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/bar_plot_demo.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/data.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/line_plot_demo.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/scatter_plot_demo.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from scatter_plot_demo import scatter_plots # type: ignore\n", "from line_plot_demo import line_plots # type: ignore\n", "from bar_plot_demo import bar_plots # type: ignore\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Tabs():\n", " with gr.TabItem(\"Line Plot\"):\n", " line_plots.render()\n", " with gr.TabItem(\"Scatter Plot\"):\n", " scatter_plots.render()\n", " with gr.TabItem(\"Bar Plot\"):\n", " bar_plots.render()\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/native_plots/run.py b/demos/native_plots/run.py new file mode 100644 index 0000000000000000000000000000000000000000..afad2779c9179363116249af76344cc84cb3c189 --- /dev/null +++ b/demos/native_plots/run.py @@ -0,0 +1,17 @@ +import gradio as gr + +from scatter_plot_demo import scatter_plots # type: ignore +from line_plot_demo import line_plots # type: ignore +from bar_plot_demo import bar_plots # type: ignore + +with gr.Blocks() as demo: + with gr.Tabs(): + with gr.TabItem("Line Plot"): + line_plots.render() + with gr.TabItem("Scatter Plot"): + scatter_plots.render() + with gr.TabItem("Bar Plot"): + bar_plots.render() + +if __name__ == "__main__": + demo.launch() diff --git a/demos/native_plots/scatter_plot_demo.py b/demos/native_plots/scatter_plot_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..0877615429f2d0285f6e7974e5cbe2ac32a22209 --- /dev/null +++ b/demos/native_plots/scatter_plot_demo.py @@ -0,0 +1,71 @@ +import gradio as gr +from data import temp_sensor_data, food_rating_data # type: ignore + +with gr.Blocks() as scatter_plots: + with gr.Row(): + start = gr.DateTime("2021-01-01 00:00:00", label="Start") + end = gr.DateTime("2021-01-05 00:00:00", label="End") + apply_btn = gr.Button("Apply", scale=0) + with gr.Row(): + group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by") + aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation") + + temp_by_time = gr.ScatterPlot( + temp_sensor_data, + x="time", + y="temperature", + buttons=["export"], + ) + temp_by_time_location = gr.ScatterPlot( + temp_sensor_data, + x="time", + y="temperature", + color="location", + buttons=["export"], + ) + + time_graphs = [temp_by_time, temp_by_time_location] + group_by.change( + lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs), + group_by, + time_graphs + ) + aggregate.change( + lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs), + aggregate, + time_graphs + ) + + # def rescale(select: gr.SelectData): + # return select.index + # rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) + + # for trigger in [apply_btn.click, rescale_evt.then]: + # trigger( + # lambda start, end: [gr.ScatterPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs + # ) + + price_by_cuisine = gr.ScatterPlot( + food_rating_data, + x="cuisine", + y="price", + buttons=["export"], + ) + with gr.Row(): + price_by_rating = gr.ScatterPlot( + food_rating_data, + x="rating", + y="price", + color="wait", + buttons=["actions", "export"], + ) + price_by_rating_color = gr.ScatterPlot( + food_rating_data, + x="rating", + y="price", + color="cuisine", + buttons=["export"], + ) + +if __name__ == "__main__": + scatter_plots.launch() diff --git a/demos/reverse_audio/requirements.txt b/demos/reverse_audio/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..24ce15ab7ead32f98c7ac3edcd34bb2010ff4326 --- /dev/null +++ b/demos/reverse_audio/requirements.txt @@ -0,0 +1 @@ +numpy diff --git a/demos/reverse_audio/run.ipynb b/demos/reverse_audio/run.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3d5687d6e90419f9a74c99404d01937c9696cca5 --- /dev/null +++ b/demos/reverse_audio/run.ipynb @@ -0,0 +1 @@ +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: reverse_audio"]}, {"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": ["\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "def reverse_audio(audio):\n", " sr, data = audio\n", " return (sr, np.flipud(data))\n", "\n", "input_audio = gr.Audio(\n", " sources=[\"microphone\"],\n", " waveform_options=gr.WaveformOptions(\n", " waveform_color=\"#01C6FF\",\n", " waveform_progress_color=\"#0066B4\",\n", " skip_length=2,\n", " show_recording_waveform=False,\n", " ),\n", ")\n", "demo = gr.Interface(\n", " fn=reverse_audio,\n", " inputs=input_audio,\n", " outputs=\"audio\",\n", " api_name=\"predict\",\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/reverse_audio/run.py b/demos/reverse_audio/run.py new file mode 100644 index 0000000000000000000000000000000000000000..63eb477316d5fa9e6bb5057b39e300d2ce5b150a --- /dev/null +++ b/demos/reverse_audio/run.py @@ -0,0 +1,27 @@ + +import numpy as np + +import gradio as gr + +def reverse_audio(audio): + sr, data = audio + return (sr, np.flipud(data)) + +input_audio = gr.Audio( + sources=["microphone"], + waveform_options=gr.WaveformOptions( + waveform_color="#01C6FF", + waveform_progress_color="#0066B4", + skip_length=2, + show_recording_waveform=False, + ), +) +demo = gr.Interface( + fn=reverse_audio, + inputs=input_audio, + outputs="audio", + api_name="predict", +) + +if __name__ == "__main__": + demo.launch()