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----
-license: cc-by-nc-sa-4.0
----
-
-### This dataset contains images used in the documentation of HuggingFace's libraries.
-
-HF Team: Please make sure you optimize the assets before uploading them.
-
-My favorite tool for this is https://tinypng.com/.
+This dataset contains images used in the documentation of HuggingFace's libraries.
\ No newline at end of file
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deleted file mode 100644
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diff --git a/blog/multi-lora-serving/LoRA.ipynb b/blog/multi-lora-serving/LoRA.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "substantial-impact",
-   "metadata": {},
-   "source": [
-    "# Welcome to Manim!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "11e0c778-c8bc-4972-8bc3-8dc929de6caa",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from manim import *\n",
-    "\n",
-    "config.media_width = \"75%\"\n",
-    "config.verbosity = \"WARNING\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "antique-knock",
-   "metadata": {},
-   "source": [
-    "## LoRA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "f7875452-fdc2-4f5d-8da2-a333f2a82e64",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Manim Community <span style=\"color: #008000; text-decoration-color: #008000\">v0.18.1</span>\n",
-       "\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "Manim Community \u001b[32mv0.\u001b[0m\u001b[32m18.1\u001b[0m\n",
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[06/09/24 14:46:08] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Output format set as webm, this can be slower than other formats         <a href=\"file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py#1061\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1061</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[06/09/24 14:46:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Output format set as webm, this can be slower than other formats         \u001b]8;id=477302;file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=649502;file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py#1061\u001b\\\u001b[2m1061\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                      \r"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<video src=\"media/jupyter/LoRA@2024-06-09@14-47-19.webm\" controls autoplay loop style=\"max-width: 75%;\"  >\n",
-       "      Your browser does not support the <code>video</code> element.\n",
-       "    </video>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Video object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%manim -qm --format webm LoRA\n",
-    "\n",
-    "from manim import *\n",
-    "\n",
-    "class LoRA(Scene):\n",
-    "    def construct(self):\n",
-    "        text_rotation = PI/2\n",
-    "        # Create the input\n",
-    "        input_rect = Rectangle(width=1, height=4, color=GREEN).to_edge(LEFT)\n",
-    "        input_label = Tex(\"$x \\\\in \\\\mathbb{R}^{d}$\", color=WHITE).move_to(input_rect).rotate(text_rotation)\n",
-    "        \n",
-    "        # Create weights\n",
-    "        weights = Rectangle(width=3, height=3, color=BLUE).move_to(input_rect).shift(RIGHT*3.5).shift(UP*1.75)\n",
-    "        weights_label = Tex(\"$W \\\\in \\\\mathbb{R}^{d \\\\times d}$\", color=WHITE).move_to(weights)\n",
-    "        vector_d = Tex(\"$d$\", color=WHITE).next_to(weights, UP, buff=0.2)\n",
-    "\n",
-    "        # Create trapezoids\n",
-    "        trapezoid1 = Polygon([-2, 1, 0], [2, 1, 0], [0.5, -0.5, 0], [-0.5, -0.5, 0], color=ORANGE).rotate(PI/2).scale(0.7)\n",
-    "        trapezoid1.next_to(weights, DOWN, buff=0.5)\n",
-    "        trapezoid1.align_to(weights, LEFT)  \n",
-    "        trapezoid1_label = Tex(\"$A$\", color=WHITE).move_to(trapezoid1)\n",
-    "\n",
-    "        trapezoid2 = Polygon([-0.5, 0.5, 0], [0.5, 0.5, 0], [2, -1, 0], [-2, -1, 0], color=ORANGE).rotate(PI/2).scale(0.7)\n",
-    "        trapezoid2.next_to(trapezoid1, RIGHT, buff=0.5)\n",
-    "        trapezoid2.align_to(weights, RIGHT)  \n",
-    "        trapezoid2_label = Tex(\"$B$\", color=WHITE).move_to(trapezoid2)\n",
-    "        \n",
-    "        r_brace = Brace(Line([-0.5, 0.5, 0], [0.5, 0.5, 0]), color=GREY).rotate(PI/2).move_to(trapezoid1, RIGHT).shift(.3*RIGHT).scale(0.7)\n",
-    "        vector_r = Tex(\"$r$\", color=GREY).next_to(r_brace, RIGHT, buff=0.1)\n",
-    "        \n",
-    "        wx = Rectangle(width=1, height=3, color=PURPLE).move_to(weights, RIGHT).shift(2*RIGHT)\n",
-    "        wx_label = Tex(\"$Wx$\").move_to(wx)\n",
-    "        \n",
-    "        lorax = Rectangle(width=1, height=3, color=PURPLE).move_to(trapezoid2, RIGHT).shift(2*RIGHT)\n",
-    "        lorax_label = Tex(\"$BAx$\").move_to(lorax)\n",
-    "        \n",
-    "        h = Rectangle(width=1, height=4, color=RED).align_to(lorax, RIGHT).align_to(input_rect, DOWN).shift(2*RIGHT)\n",
-    "        h_label = Tex(\"$h$\").move_to(h)\n",
-    "\n",
-    "        # Arrows\n",
-    "        arrow_input_weights = Arrow(input_rect.get_right(), weights.get_left(), buff=0.1, color=GREY)\n",
-    "        arrow_weights_wx = Arrow(weights.get_right(), wx.get_left(), buff=0.1, color=GREY)\n",
-    "        arrow_input_matA = Arrow(input_rect.get_right(), trapezoid1.get_left(), buff=0.1, color=GREY)\n",
-    "        arrow_matB_BAx = Arrow(trapezoid2.get_right(), lorax.get_left(), buff=0.1, color=GREY)\n",
-    "        \n",
-    "        # Create Plus Sign\n",
-    "        vertical_line = Line(UP, DOWN, stroke_width=12, color=GREY)\n",
-    "        horizontal_line = Line(LEFT, RIGHT, stroke_width=12, color=GREY)\n",
-    "        plus_sign = VGroup(vertical_line, horizontal_line).scale(0.25)\n",
-    "        plus_sign.move_to(h, LEFT).shift(0.75*LEFT)\n",
-    "        \n",
-    "        # Legend Box\n",
-    "        legend_box = VGroup(\n",
-    "            Tex(\"input\", color=GREEN),\n",
-    "            Tex(\"frozen weights\", color=BLUE),\n",
-    "            Tex(\"low rank matrices\", color=ORANGE),\n",
-    "            Tex(\"activations\", color=PURPLE),\n",
-    "            Tex(\"output\", color=RED)\n",
-    "        ).arrange(DOWN, aligned_edge=LEFT, buff=0.1).scale(0.7)\n",
-    "        \n",
-    "        legend_background = Rectangle(width=legend_box.width+0.6, height=legend_box.height+0.6, stroke_color=GREY)\n",
-    "        legend_background.to_edge(RIGHT).to_edge(UP)\n",
-    "        legend_background.align_to(weights, UP)\n",
-    "        legend_box.move_to(legend_background.get_center())\n",
-    "\n",
-    "        # Adjust elements\n",
-    "        block_elements = [input_rect, weights, trapezoid1, trapezoid2, wx, lorax, h, legend_background]\n",
-    "        label_elements = [input_label, weights_label, trapezoid1_label, trapezoid2_label, wx_label, lorax_label, h_label, vector_r, r_brace, legend_box]\n",
-    "        arrow_elements = [arrow_input_weights, arrow_weights_wx, arrow_input_matA, arrow_matB_BAx, plus_sign]\n",
-    "        for element in block_elements + label_elements + arrow_elements:\n",
-    "            element.shift(0.2 * DOWN)\n",
-    "\n",
-    "        # Add title\n",
-    "        title = Text(\"LoRA Inference\", font_size=36, color=WHITE).to_edge(UP).shift(0.30*UP)\n",
-    "        block_elements += title\n",
-    "        \n",
-    "        # Animation for emphasis\n",
-    "        self.play(FadeIn(*block_elements), run_time=1/3)\n",
-    "        self.wait(1/3)\n",
-    "        \n",
-    "        # Animation for emphasis\n",
-    "        self.play(FadeIn(*label_elements), run_time=1/3)\n",
-    "        self.wait(1/3)\n",
-    "        \n",
-    "        # Animation for emphasis\n",
-    "        self.play(FadeIn(*arrow_elements), run_time=1/3)\n",
-    "        self.wait(1/3)\n",
-    "        \n",
-    "        # Explain LoRA\n",
-    "        explain_shift = 0.45\n",
-    "        explain = Tex(\"$A,B $\", \"$ \\\\in \\\\mathbb{R}^{d \\\\times r}$\", \" LoRA reduces an input to a low rank dim: \", \"$r$\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('$A,B $', ORANGE).set_color_by_tex('$r$', YELLOW)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        self.play(FadeIn(explain), vector_r.animate.set_color(YELLOW), r_brace.animate.set_color(YELLOW), run_time=1)\n",
-    "        self.play(Indicate(trapezoid1, rate_func=there_and_back), Indicate(trapezoid2, rate_func=there_and_back), Indicate(vector_r), Flash(r_brace), run_time=1)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), vector_r.animate.set_color(GREY), r_brace.animate.set_color(GREY), run_time=1)\n",
-    "        self.wait(0.5)\n",
-    "\n",
-    "        # Explain Wx\n",
-    "        explain = Tex(\"$Wx$\", \" is calculated from our \", \"input\", \" and our \", \"pre-trained weights\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('Wx', PURPLE).set_color_by_tex(\"input\", GREEN).set_color_by_tex(\"pre-trained weights\", BLUE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        self.play(FadeIn(explain), arrow_input_weights.animate.set_color(YELLOW), arrow_weights_wx.animate.set_color(YELLOW), run_time=1)\n",
-    "        self.play(Indicate(input_rect, rate_func=there_and_back), Indicate(weights, rate_func=there_and_back), Indicate(wx, rate_func=there_and_back), run_time=1)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), arrow_input_weights.animate.set_color(GREY), arrow_weights_wx.animate.set_color(GREY))\n",
-    "        self.wait(0.5)\n",
-    "        \n",
-    "        # Explain BAx\n",
-    "        explain = Tex(\"$BAx$\", \" is calculated from our \", \"input\", \" and our \", \"low-rank adapters\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('BAx', PURPLE).set_color_by_tex(\"input\", GREEN).set_color_by_tex(\"low-rank adapters\", ORANGE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        self.play(FadeIn(explain), arrow_input_matA.animate.set_color(YELLOW), arrow_matB_BAx.animate.set_color(YELLOW), run_time=1)\n",
-    "        self.play(Indicate(input_rect, rate_func=there_and_back), Indicate(trapezoid1, rate_func=there_and_back), Indicate(trapezoid2, rate_func=there_and_back), Indicate(lorax, rate_func=there_and_back), run_time=1)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), arrow_input_matA.animate.set_color(GREY), arrow_matB_BAx.animate.set_color(GREY))\n",
-    "        self.wait(0.5)\n",
-    "        \n",
-    "        # Explain h\n",
-    "        explain = Tex(\"$h=Wx + BAx$\", \" is the sum of \", \"$Wx$\", \" and \", \"$BAx$\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('$h=Wx + BAx$', RED).set_color_by_tex(\"$Wx$\", PURPLE).set_color_by_tex(\"$BAx$\", PURPLE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        self.play(FadeIn(explain), plus_sign.animate.set_color(YELLOW), run_time=1)\n",
-    "        self.play(Indicate(wx, rate_func=there_and_back), Indicate(lorax, rate_func=there_and_back), Indicate(h, rate_func=there_and_back), run_time=1)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), plus_sign.animate.set_color(GREY), run_time=1)\n",
-    "        self.wait(0.5)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "cbe45556-cbb0-47a1-8d21-f25ff1f4e0d3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Manim Community <span style=\"color: #008000; text-decoration-color: #008000\">v0.18.1</span>\n",
-       "\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "Manim Community \u001b[32mv0.\u001b[0m\u001b[32m18.1\u001b[0m\n",
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[06/09/24 14:47:19] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> Output format set as webm, this can be slower than other formats         <a href=\"file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py#1061\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1061</span></a>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[06/09/24 14:47:19]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m Output format set as webm, this can be slower than other formats         \u001b]8;id=998741;file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=978614;file:///home/user/miniconda/lib/python3.9/site-packages/manim/_config/utils.py#1061\u001b\\\u001b[2m1061\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                 \r"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<video src=\"media/jupyter/MultiLoRA@2024-06-09@14-48-45.webm\" controls autoplay loop style=\"max-width: 75%;\"  >\n",
-       "      Your browser does not support the <code>video</code> element.\n",
-       "    </video>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Video object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%manim -qm --format webm MultiLoRA\n",
-    "\n",
-    "from manim import *\n",
-    "\n",
-    "class MultiLoRA(Scene):\n",
-    "    def construct(self):\n",
-    "        # Add title\n",
-    "        title = Text(\"Multi-LoRA Inference\", font_size=36, color=WHITE).to_edge(UP).shift(0.30*UP)\n",
-    "        \n",
-    "        # Create the base input\n",
-    "        x_width = 2.5\n",
-    "        input_rect = Rectangle(width=x_width, height=2, color=GREEN).to_edge(LEFT, buff=0.5)\n",
-    "        input_label = Tex(\"$x$\", color=WHITE).move_to(input_rect)\n",
-    "\n",
-    "        # Create W matrix rectangle\n",
-    "        w_rect = Rectangle(width=2.25, height=2.25, color=BLUE).next_to(input_rect, RIGHT, buff=1.25).shift(1.25*UP)\n",
-    "        w_label = Text(\"W\", color=WHITE).scale(0.75).move_to(w_rect)\n",
-    "\n",
-    "        # Create batched inputs for weight multiplication\n",
-    "        x1_w = Rectangle(width=x_width, height=0.75, color=GREEN).next_to(w_rect, RIGHT, buff=1.125).shift(0.75*UP)\n",
-    "        x1_w_label = Tex(\"$x_1$\", color=WHITE).move_to(x1_w)\n",
-    "        x2_w = Rectangle(width=x_width, height=1.25, color=GREEN).next_to(x1_w, DOWN)\n",
-    "        x2_w_label = Tex(\"$x_2$\", color=WHITE).move_to(x2_w)\n",
-    "\n",
-    "        # Create LoRA computation blocks\n",
-    "        l1_rect = Rectangle(width=2.25, height=1, color=ORANGE).next_to(input_rect, RIGHT, buff=1.25).shift(1.25*DOWN)\n",
-    "        l1_label = Tex(\"$B_1 * A_1$\", color=WHITE).scale(0.75).move_to(l1_rect)\n",
-    "        l2_rect = Rectangle(width=2.25, height=1, color=ORANGE).next_to(l1_rect, DOWN)\n",
-    "        l2_label = Tex(\"$B_2 * A_2$\", color=WHITE).scale(0.75).move_to(l2_rect)\n",
-    "        \n",
-    "        x1_l = Rectangle(width=x_width, height=0.75, color=GREEN).next_to(l1_rect, RIGHT, buff=1.125).align_to(l1_rect, UP)\n",
-    "        x1_l_label = Tex(\"$x_1$\", color=WHITE).move_to(x1_l)\n",
-    "        x2_l = Rectangle(width=x_width, height=1.25, color=GREEN).next_to(x1_l, DOWN)\n",
-    "        x2_l_label = Tex(\"$x_2$\", color=WHITE).move_to(x2_l)\n",
-    "\n",
-    "        # Grouping boxes\n",
-    "        group_box_w = SurroundingRectangle(VGroup(x1_w, x2_w, w_rect), color=GREY, buff=0.1)\n",
-    "        group_box_l = SurroundingRectangle(VGroup(x1_l, x2_l, l1_rect, l2_rect), color=GREY, buff=0.1)\n",
-    "\n",
-    "        # Labels for Grouping Boxes\n",
-    "        group_box_w_label = Text(\"Batched Base Computation\", font_size=24, color=GREY).next_to(group_box_w, UP, buff=0.1)\n",
-    "        group_box_l_label = Text(\"Batched LoRA Computation\", font_size=24, color=GREY).next_to(group_box_l, UP, buff=0.1)\n",
-    "\n",
-    "        # Create Multiplication Sign\n",
-    "        times_x = (x2_w.get_left()[0] + w_rect.get_right()[0]) / 2\n",
-    "        times_sign_w = self.create_multiplication_sign(w_rect.get_center()).set_x(times_x)\n",
-    "        times_sign_l = self.create_multiplication_sign(VGroup(l1_rect, l2_rect).get_center()).set_x(times_x)\n",
-    "        \n",
-    "        # Output\n",
-    "        output_rect = Rectangle(width=x_width, height=2, color=RED).to_edge(RIGHT, buff=0.5)\n",
-    "        output_label = Tex(\"$h$\", color=WHITE).move_to(output_rect)\n",
-    "        \n",
-    "        # Create Plus Sign\n",
-    "        plus_x = (group_box_w.get_right()[0] + output_rect.get_left()[0]) / 2\n",
-    "        plus_sign = self.create_plus_sign(output_rect.get_center()).set_x(plus_x)\n",
-    "        \n",
-    "        # Create Arrows\n",
-    "        arrow_input_weights = Arrow(input_rect.get_right(), group_box_w.get_left(), buff=0.1, color=GREY)\n",
-    "        arrow_input_lora = Arrow(input_rect.get_right(), group_box_l.get_left(), buff=0.1, color=GREY)\n",
-    "        \n",
-    "        # Grouping elements\n",
-    "        block_elements = [input_rect, x1_w, x2_w, w_rect, x1_l, x2_l, l1_rect, l2_rect, output_rect]\n",
-    "        label_elements = [input_label, x1_w_label, x2_w_label, w_label, x1_l_label, x2_l_label, l1_label, l2_label, output_label]\n",
-    "        arrow_elements = [arrow_input_weights, arrow_input_lora]\n",
-    "        sign_elements = [times_sign_w, times_sign_l, plus_sign]\n",
-    "        group_box_elements = [group_box_w, group_box_l, group_box_w_label, group_box_l_label]\n",
-    "        \n",
-    "        # Fade in elements\n",
-    "        self.play(FadeIn(title), run_time=1/3)\n",
-    "        self.play(*[FadeIn(element) for element in block_elements + label_elements], run_time=1/3)\n",
-    "        self.play(*[FadeIn(element) for element in arrow_elements], run_time=1/3)\n",
-    "        self.play(*[FadeIn(element) for element in sign_elements], run_time=1/3)\n",
-    "        self.play(*[FadeIn(element) for element in group_box_elements], run_time=1/3)\n",
-    "\n",
-    "        # Explain LoRA\n",
-    "        explain_shift = 0.3\n",
-    "        \n",
-    "        explain = Tex(\"$x$\", \" is a heterogeneous batch of \", \"inputs\", \" to different \", \" LoRA adapters\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('$x$', GREEN).set_color_by_tex('inputs', GREEN).set_color_by_tex(' LoRA adapters', ORANGE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        x_elements = [x1_w, x2_w, x1_l, x2_l]\n",
-    "        self.play(FadeIn(explain), run_time=2/3)\n",
-    "        self.play(Indicate(input_rect), run_time=2/3)\n",
-    "        self.play(*[Indicate(arrow_element, rate_func=there_and_back) for arrow_element in arrow_elements], run_time=2/3)\n",
-    "        self.play(*[Indicate(x_element, rate_func=there_and_back) for x_element in x_elements], run_time=1)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), run_time=1)\n",
-    "        self.wait(0.5)\n",
-    "\n",
-    "        # Explain Wx\n",
-    "        explain = Tex(\"Batched Base Computation\", \" = \", \"frozen weights\", \" $*$ \", \"input\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('input', GREEN).set_color_by_tex('frozen weights', BLUE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        x_elements = [x1_w, x2_w]\n",
-    "        self.play(FadeIn(explain), group_box_w.animate.set_color(YELLOW), group_box_w_label.animate.set_color(YELLOW), run_time=1)\n",
-    "        self.play(Indicate(w_rect), run_time=0.5)\n",
-    "        self.play(Indicate(times_sign_w), run_time=0.5)\n",
-    "        self.play(*[Indicate(x_element, rate_func=there_and_back) for x_element in x_elements], run_time=0.5)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), group_box_w.animate.set_color(GREY), group_box_w_label.animate.set_color(GREY), run_time=1)\n",
-    "        self.wait(0.5)\n",
-    "\n",
-    "        # Explain BAx\n",
-    "        explain = Tex(\"Batched LoRA Computation\", \" = \", \"LoRA adapters\", \" $*$ \", \"input\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex('input', GREEN).set_color_by_tex('LoRA adapters', ORANGE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        x_elements = [x1_l, x2_l]\n",
-    "        l_elements = [l1_rect, l2_rect]\n",
-    "        self.play(FadeIn(explain), group_box_l.animate.set_color(YELLOW), group_box_l_label.animate.set_color(YELLOW), run_time=1)\n",
-    "        self.play(*[Indicate(l_element, rate_func=there_and_back) for l_element in l_elements], run_time=0.5)\n",
-    "        self.play(Indicate(times_sign_l), run_time=0.5)\n",
-    "        self.play(*[Indicate(x_element, rate_func=there_and_back) for x_element in x_elements], run_time=0.5)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain), group_box_l.animate.set_color(GREY), group_box_l_label.animate.set_color(GREY), run_time=1)\n",
-    "        self.wait(0.5)\n",
-    "\n",
-    "        # Explain Output\n",
-    "        explain = Tex(\"$h$\", \" = \", \"Batched Base Computation\", \" + \", \"Batched LoRA Computation\", color=WHITE)\n",
-    "        explain = explain.set_color_by_tex(\"$h$\", RED).set_color_by_tex('Batched Base Computation', BLUE).set_color_by_tex('Batched LoRA Computation', ORANGE)\n",
-    "        explain = explain.to_edge(DOWN).shift(explain_shift*DOWN)\n",
-    "        x_elements = [x1_l, x2_l]\n",
-    "        l_elements = [l1_rect, l2_rect]\n",
-    "        self.play(FadeIn(explain),group_box_w.animate.set_color(BLUE), group_box_w_label.animate.set_color(BLUE), group_box_l.animate.set_color(ORANGE), group_box_l_label.animate.set_color(ORANGE), run_time=1)\n",
-    "        self.play(Indicate(output_rect), run_time=0.5)\n",
-    "        self.play(Indicate(VGroup(group_box_w, group_box_w_label)), run_time=0.5)\n",
-    "        self.play(Indicate(plus_sign), run_time=0.5)\n",
-    "        self.play(Indicate(VGroup(group_box_l, group_box_l_label)), run_time=0.5)\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(explain),group_box_w.animate.set_color(GREY), group_box_w_label.animate.set_color(GREY), group_box_l.animate.set_color(GREY), group_box_l_label.animate.set_color(GREY), run_time=1)\n",
-    "        self.wait(0.5)\n",
-    "\n",
-    "    def create_multiplication_sign(self, position):\n",
-    "        vertical_line = Line(UP*0.5 + LEFT*0.5, DOWN*0.5 + RIGHT*0.5, stroke_width=12, color=GREY)\n",
-    "        horizontal_line = Line(UP*0.5 + RIGHT*0.5, DOWN*0.5 + LEFT*0.5, stroke_width=12, color=GREY)\n",
-    "        times_sign = VGroup(vertical_line, horizontal_line).scale(0.25)\n",
-    "        times_sign.move_to(position)\n",
-    "        return times_sign\n",
-    "\n",
-    "    def create_plus_sign(self, position):\n",
-    "        vertical_line = Line(UP, DOWN, stroke_width=12, color=GREY)\n",
-    "        horizontal_line = Line(LEFT, RIGHT, stroke_width=12, color=GREY)\n",
-    "        plus_sign = VGroup(vertical_line, horizontal_line).scale(0.25)\n",
-    "        plus_sign.move_to(position)\n",
-    "        return plus_sign\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afdd5793-1466-4d05-b3e5-e926cf509e9f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/blog/multi-lora-serving/LoRA.webm b/blog/multi-lora-serving/LoRA.webm
deleted file mode 100644
index 976e58a1e18c8dde305f7c1a8958255227d500c0..0000000000000000000000000000000000000000
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diff --git a/blog/multi-lora-serving/MultiLoRA.webm b/blog/multi-lora-serving/MultiLoRA.webm
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index b5c842e0166ca601a004fd93721cda92a4fa2af1..0000000000000000000000000000000000000000
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diff --git a/blog/multi-lora-serving/multi-lora-cost.ipynb b/blog/multi-lora-serving/multi-lora-cost.ipynb
deleted file mode 100644
index b1e8e0cf6bcae0547ed92dcffd96ce20a666e6f4..0000000000000000000000000000000000000000
--- a/blog/multi-lora-serving/multi-lora-cost.ipynb
+++ /dev/null
@@ -1,1208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "caf5a33f-2c33-4824-a74a-bb7cb39f16f2",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2ee85de-bff3-4243-9aae-ca7fa4d074e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -q huggingface-hub plotly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "644fd907-3912-49dd-bc25-b73eb0abf532",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed69c6f0-44a0-43cf-96cf-143d2d92ad30",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Create Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "da6a0eba-9b3d-43d2-a689-f9399e680fa5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "marker": {
-          "color": "rgb(255, 215, 0)"
-         },
-         "name": "TGI Multi-LoRA",
-         "type": "bar",
-         "x": [
-          "1",
-          "2",
-          "4",
-          "8",
-          "16",
-          "32"
-         ],
-         "y": [
-          0.26,
-          0.26,
-          0.26,
-          0.26,
-          0.26,
-          0.26
-         ]
-        },
-        {
-         "marker": {
-          "color": "rgb(64, 64, 64)"
-         },
-         "name": "TGI Dedicated",
-         "type": "bar",
-         "x": [
-          "1",
-          "2",
-          "4",
-          "8",
-          "16",
-          "32"
-         ],
-         "y": [
-          0.26,
-          0.52,
-          1.04,
-          2.08,
-          4.16,
-          8.33
-         ]
-        },
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"title\":{\"font\":{\"size\":24}},\"xaxis\":{\"title\":{\"text\":\"Number of Fine-Tuned Models\",\"font\":{\"size\":28}},\"tickfont\":{\"size\":16},\"type\":\"category\"},\"yaxis\":{\"title\":{\"text\":\"Cost ($\\u002fmillion tokens)\",\"font\":{\"size\":28}},\"tickfont\":{\"size\":16}},\"legend\":{\"title\":{\"font\":{\"size\":28}},\"font\":{\"size\":24},\"x\":0.5,\"y\":-0.1,\"orientation\":\"h\",\"xanchor\":\"center\",\"yanchor\":\"top\"},\"barmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
-       "                            \n",
-       "var gd = document.getElementById('1d4ef925-74c3-4370-8148-03c1171b5426');\n",
-       "var x = new MutationObserver(function (mutations, observer) {{\n",
-       "        var display = window.getComputedStyle(gd).display;\n",
-       "        if (!display || display === 'none') {{\n",
-       "            console.log([gd, 'removed!']);\n",
-       "            Plotly.purge(gd);\n",
-       "            observer.disconnect();\n",
-       "        }}\n",
-       "}});\n",
-       "\n",
-       "// Listen for the removal of the full notebook cells\n",
-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
-       "if (outputEl) {{\n",
-       "    x.observe(outputEl, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "                        })                };                });            </script>        </div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import plotly.graph_objects as go\n",
-    "\n",
-    "# Updated data\n",
-    "number_of_models = [\"1\", \"2\", \"4\", \"8\", \"16\", \"32\"]\n",
-    "tgi_multilora = [0.26, 0.26, 0.26, 0.26, 0.26, 0.26]\n",
-    "tgi_dedicated = [0.26, 0.52, 1.04, 2.08, 4.16, 8.33]\n",
-    "gpt_3_5_turbo = [4.83, 4.83, 4.83, 4.83, 4.83, 4.83]\n",
-    "\n",
-    "# Create traces\n",
-    "fig = go.Figure()\n",
-    "fig.add_trace(go.Bar(x=number_of_models, y=tgi_multilora, name='TGI Multi-LoRA', marker_color='rgb(255, 215, 0)'))  # Golden color\n",
-    "fig.add_trace(go.Bar(x=number_of_models, y=tgi_dedicated, name='TGI Dedicated', marker_color='rgb(64, 64, 64)'))  # Dark grey\n",
-    "fig.add_trace(go.Bar(x=number_of_models, y=gpt_3_5_turbo, name='gpt-3.5-turbo', marker_color='rgb(169, 169, 169)'))  # Grey\n",
-    "\n",
-    "# Layout settings with larger text sizes\n",
-    "fig.update_layout(\n",
-    "    # title='Cost per Million Tokens by Model Configuration',\n",
-    "    title_font_size=24,  # Larger title font size\n",
-    "    xaxis_title='Number of Fine-Tuned Models',\n",
-    "    yaxis_title='Cost ($/million tokens)',\n",
-    "    xaxis=dict(\n",
-    "        type='category',\n",
-    "        title_font_size=28,  # Larger axis title font size\n",
-    "        tickfont_size=16  # Larger tick label font size\n",
-    "    ),\n",
-    "    yaxis=dict(\n",
-    "        title_font_size=28,  # Larger axis title font size\n",
-    "        tickfont_size=16  # Larger tick label font size\n",
-    "    ),\n",
-    "    legend=dict(\n",
-    "        x=0.5,  # Horizontal position, 0 is left\n",
-    "        y=-0.1,\n",
-    "        # y=-0.3,  # Vertical position, negative values to move it down\n",
-    "        orientation=\"h\",  # Horizontal layout\n",
-    "        xanchor='center',  # Anchor the legend at the left\n",
-    "        yanchor='top'  # Anchor the legend at the top\n",
-    "    ),\n",
-    "    barmode='group',\n",
-    "    # legend_title_text='Model',\n",
-    "    legend_title_font_size=28,  # Larger legend title font size\n",
-    "    legend_font_size=24  # Larger legend font size\n",
-    ")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5620af3-037f-4f78-81b8-ed80dea6ec6e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Push Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "da5bfebe-fe17-4e97-8d71-5db736642ac9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Set the size for 16:9 aspect ratio and save as PNG\n",
-    "image_out = \"multi-lora-cost.png\"\n",
-    "fig.write_image(image_out, width=1920, height=1080, scale=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "33bdcc3f-f94e-4a6b-bb96-05d39267994d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " Cleaning up notebook\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/datasets/huggingface/documentation-images/commit/d6302d45be5810016615dc065b07f244a6f94f98', commit_message='Cleaning up notebook', commit_description='', oid='d6302d45be5810016615dc065b07f244a6f94f98', pr_url=None, pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import HfApi\n",
-    "\n",
-    "commit_message_image = input()\n",
-    "api = HfApi()\n",
-    "api.upload_file(\n",
-    "    path_or_fileobj=image_out,\n",
-    "    path_in_repo=f\"blog/multi-lora-serving/{image_out}\",\n",
-    "    repo_id=\"huggingface/documentation-images\",\n",
-    "    repo_type=\"dataset\",\n",
-    "    commit_message=commit_message_image,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62fd36dc-2f6c-4e3f-8441-a544803258f5",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Push Notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89d6b741-a888-463a-9ea3-78437e89d453",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import HfApi\n",
-    "\n",
-    "notebook = \"multi-lora-cost.ipynb\"\n",
-    "commit_message_notebook = input()\n",
-    "\n",
-    "api = HfApi()\n",
-    "api.upload_file(\n",
-    "    path_or_fileobj=notebook,\n",
-    "    path_in_repo=f\"blog/multi-lora-serving/{notebook}\",\n",
-    "    repo_id=\"huggingface/documentation-images\",\n",
-    "    repo_type=\"dataset\",\n",
-    "    commit_message=commit_message_notebook,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc00cbac-773f-4acd-a338-6a22d2624edb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/blog/multi-lora-serving/multi-lora-cost.png b/blog/multi-lora-serving/multi-lora-cost.png
deleted file mode 100644
index 3e1075797273e165587bdff8839208e079a42190..0000000000000000000000000000000000000000
--- a/blog/multi-lora-serving/multi-lora-cost.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:38afbd8a19925194c8afe1973d1d878d9922eedba5d9fd54d9909ac3c97e8c5d
-size 303360
diff --git a/blog/multi-lora-serving/multi-lora-inference-endpoints-2.png b/blog/multi-lora-serving/multi-lora-inference-endpoints-2.png
deleted file mode 100644
index 5e3f079a2d75abc68230773cea1600e1323eaabb..0000000000000000000000000000000000000000
--- a/blog/multi-lora-serving/multi-lora-inference-endpoints-2.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f41c6eeabd13afdae1bc1259ea43469b4e4ff1c96fb6ba83a61e7666f37f407e
-size 482616
diff --git a/blog/multi-lora-serving/multi-lora-inference-endpoints.png b/blog/multi-lora-serving/multi-lora-inference-endpoints.png
deleted file mode 100644
index de394c3883e91240baf713a5eebf8e63f5330e72..0000000000000000000000000000000000000000
--- a/blog/multi-lora-serving/multi-lora-inference-endpoints.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3c2d271aaa9f566bad735dde14badee221eef03d3c6f59f21cadc30e44cc62cf
-size 268707
diff --git a/blog/multi-lora-serving/multi-lora-serving-pattern.ipynb b/blog/multi-lora-serving/multi-lora-serving-pattern.ipynb
deleted file mode 100644
index 12287853add1902bbb4ec596ba5db9ec1ea61ab9..0000000000000000000000000000000000000000
--- a/blog/multi-lora-serving/multi-lora-serving-pattern.ipynb
+++ /dev/null
@@ -1,3140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2cd0de88-d314-433d-bf0b-ada46405c6f4",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3b26d339-8e2d-4f7e-8dbf-24c649932de4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install -q huggingface-hub plotly numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afe2dc35-6aae-4096-830a-0e8ed44de157",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import login\n",
-    "\n",
-    "login()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4195315a-8616-47b0-a7a4-9792caeaff8e",
-   "metadata": {},
-   "source": [
-    "# Create Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "b7aebf99-b8fb-4892-90e2-2261202ac576",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "line": {
-          "color": "#B0C4DE",
-          "width": 3
-         },
-         "mode": "lines",
-         "name": "Low Usage",
-         "type": "scatter",
-         "x": [
-          0,
-          0.1,
-          0.2,
-          0.30000000000000004,
-          0.4,
-          0.5,
-          0.6000000000000001,
-          0.7000000000000001,
-          0.8,
-          0.9,
-          1,
-          1.1,
-          1.2000000000000002,
-          1.3,
-          1.4000000000000001,
-          1.5,
-          1.6,
-          1.7000000000000002,
-          1.8,
-          1.9000000000000001,
-          2,
-          2.1,
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(hours)\",\"font\":{\"size\":28}},\"tickfont\":{\"size\":16}},\"yaxis\":{\"title\":{\"text\":\"Requests\",\"font\":{\"size\":28}},\"tickfont\":{\"size\":16}},\"legend\":{\"title\":{\"text\":\"LoRA Usage Pattern\",\"font\":{\"size\":28}},\"font\":{\"size\":24},\"x\":0.5,\"y\":-0.1,\"orientation\":\"h\",\"xanchor\":\"center\",\"yanchor\":\"top\"}},                        {\"responsive\": true}                    ).then(function(){\n",
-       "                            \n",
-       "var gd = document.getElementById('878faf24-456c-43fd-bb01-7620c7d698a6');\n",
-       "var x = new MutationObserver(function (mutations, observer) {{\n",
-       "        var display = window.getComputedStyle(gd).display;\n",
-       "        if (!display || display === 'none') {{\n",
-       "            console.log([gd, 'removed!']);\n",
-       "            Plotly.purge(gd);\n",
-       "            observer.disconnect();\n",
-       "        }}\n",
-       "}});\n",
-       "\n",
-       "// Listen for the removal of the full notebook cells\n",
-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
-       "if (outputEl) {{\n",
-       "    x.observe(outputEl, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "                        })                };                });            </script>        </div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import plotly.graph_objects as go\n",
-    "import numpy as np\n",
-    "\n",
-    "# Setting the time scale\n",
-    "time = np.arange(0, 24, 0.1)  # 24 hours, in increments of 0.1 hour\n",
-    "\n",
-    "# Generating usage patterns\n",
-    "np.random.seed(42)\n",
-    "low_usage = (np.random.poisson(70, len(time)) * (np.random.rand(len(time)) < 0.15).astype(int))//2 \n",
-    "bursty_usage = np.random.poisson(70, len(time)) * (np.random.rand(len(time)) < 0.15).astype(int)\n",
-    "bursty_usage = np.random.poisson(100, len(time)) * (np.random.rand(len(time)) < 0.2).astype(int)//1.4\n",
-    "\n",
-    "high_volume = np.random.normal(loc=15, scale=2, size=len(time))*1.75\n",
-    "\n",
-    "# Applying smoothing\n",
-    "smooth_low_usage = np.convolve(low_usage, np.ones(50)/50, mode='same')\n",
-    "smooth_bursty_usage = np.convolve(bursty_usage, np.ones(50)/50, mode='same')\n",
-    "smooth_high_volume = np.convolve(high_volume, np.ones(50)/50, mode='same')\n",
-    "total_usage = smooth_low_usage + smooth_bursty_usage + smooth_high_volume\n",
-    "\n",
-    "# Plotting using Plotly\n",
-    "fig = go.Figure()\n",
-    "fig.add_trace(go.Scatter(x=time, y=smooth_low_usage, mode='lines', name='Low Usage', line=dict(color='#B0C4DE', width=3)))  # Light Steel Blue\n",
-    "fig.add_trace(go.Scatter(x=time, y=smooth_bursty_usage, mode='lines', name='Bursty Usage', line=dict(color='#FFB6C1', width=3)))  # Light Pink\n",
-    "fig.add_trace(go.Scatter(x=time, y=smooth_high_volume, mode='lines', name='High Volume', line=dict(color='#98FB98', width=3)))  # Pale Green\n",
-    "fig.add_trace(go.Scatter(x=time, y=total_usage, mode='lines', name='Multi-LoRA Serving', line=dict(color='#2F4F4F', width=4)))  # Dark Slate Gray\n",
-    "\n",
-    "fig.update_layout(\n",
-    "    xaxis_title='Time (hours)',\n",
-    "    yaxis_title='Requests',\n",
-    "    legend_title='LoRA Usage Pattern',\n",
-    "    xaxis=dict(\n",
-    "        title_font_size=28,  # Larger axis title font size\n",
-    "        tickfont_size=16  # Larger tick label font size\n",
-    "    ),\n",
-    "    yaxis=dict(\n",
-    "        title_font_size=28,  # Larger axis title font size\n",
-    "        tickfont_size=16  # Larger tick label font size\n",
-    "    ),\n",
-    "    legend=dict(\n",
-    "        x=0.5,  # Horizontal position, 0 is left\n",
-    "        y=-0.1,  # Vertical position, negative values to move it down\n",
-    "        orientation=\"h\",  # Horizontal layout\n",
-    "        xanchor='center',  # Anchor the legend at the center\n",
-    "        yanchor='top'  # Anchor the legend at the top\n",
-    "    ),\n",
-    "    template='plotly_white',\n",
-    "    legend_title_font_size=28,  # Larger legend title font size\n",
-    "    legend_font_size=24  # Larger legend font size\n",
-    ")\n",
-    "\n",
-    "\n",
-    "fig.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc54b448-2eb3-44cc-8304-983e27138296",
-   "metadata": {},
-   "source": [
-    "# Push Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "4ef615e2-ab36-43f1-b310-cac8b17d29b7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "image_out = \"multi-lora-serving-pattern.png\"\n",
-    "fig.write_image(image_out, width=1920, height=1080, scale=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "13d8ee16-65ec-4a32-84e9-e6b99aab92af",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " Fixed line width\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/datasets/huggingface/documentation-images/commit/621a34717b8f174ffc264b32fb96090b3d3434b0', commit_message='Fixed line width', commit_description='', oid='621a34717b8f174ffc264b32fb96090b3d3434b0', pr_url=None, pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import HfApi\n",
-    "\n",
-    "commit_message_image = input()\n",
-    "\n",
-    "api = HfApi()\n",
-    "api.upload_file(\n",
-    "    path_or_fileobj=image_out,\n",
-    "    path_in_repo=f\"blog/multi-lora-serving/{image_out}\",\n",
-    "    repo_id=\"huggingface/documentation-images\",\n",
-    "    repo_type=\"dataset\",\n",
-    "    commit_message=commit_message_image,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81e6b86b-c439-4acd-a072-2ef3ab782f90",
-   "metadata": {},
-   "source": [
-    "# Push Notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "adca7ec0-25c3-42e3-84b7-f7f9342e4e4f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " Fixed line width\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/datasets/huggingface/documentation-images/commit/481c188cb1ec30af0fb845fd680de2264a21b01c', commit_message='Fixed line width', commit_description='', oid='481c188cb1ec30af0fb845fd680de2264a21b01c', pr_url=None, pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import HfApi\n",
-    "\n",
-    "notebook = \"multi-lora-serving-pattern.ipynb\"\n",
-    "commit_message_notebook = input()\n",
-    "\n",
-    "api = HfApi()\n",
-    "api.upload_file(\n",
-    "    path_or_fileobj=notebook,\n",
-    "    path_in_repo=f\"blog/multi-lora-serving/{notebook}\",\n",
-    "    repo_id=\"huggingface/documentation-images\",\n",
-    "    repo_type=\"dataset\",\n",
-    "    commit_message=commit_message_notebook\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59fe0f00-27aa-428f-bc52-abcede301f8b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/blog/multi-lora-serving/multi-lora-serving-pattern.png b/blog/multi-lora-serving/multi-lora-serving-pattern.png
deleted file mode 100644
index 5c2e361eeebc88e7569722cfcddb87a99cf6f90e..0000000000000000000000000000000000000000
--- a/blog/multi-lora-serving/multi-lora-serving-pattern.png
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diff --git a/blog/object-detection-leaderboard/11-pointInterpolation.png b/blog/object-detection-leaderboard/11-pointInterpolation.png
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diff --git a/blog/tgi-benchmarking/Latency_and_Throughput_Manim.ipynb b/blog/tgi-benchmarking/Latency_and_Throughput_Manim.ipynb
deleted file mode 100644
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--- a/blog/tgi-benchmarking/Latency_and_Throughput_Manim.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "substantial-impact",
-   "metadata": {},
-   "source": [
-    "# Welcome to Manim!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "first-armenia",
-   "metadata": {},
-   "source": [
-    "This is a temporary test environment in which you can play around with Manim without the need of installing it locally. Some basic knowledge of Python is helpful! Keep in mind that this is a *temporary* environment, though: your changes will not be saved and cannot be shared with others. To save your work, you will need to download the notebook file (\"File > Download as > Notebook (.ipynb)\"). Enjoy!\n",
-    "\n",
-    "> *Useful resources:* [Documentation](https://docs.manim.community), [Discord](https://discord.gg/mMRrZQW), [Reddit](https://www.reddit.com/r/manim/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "honest-cruise",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f8ffa0be-d1ae-4b1d-8827-01e11ef5be68",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2024-05-18 06:19:15--  https://pic.onlinewebfonts.com/thumbnails/icons_308642.svg\n",
-      "Resolving pic.onlinewebfonts.com (pic.onlinewebfonts.com)... 107.161.24.252\n",
-      "Connecting to pic.onlinewebfonts.com (pic.onlinewebfonts.com)|107.161.24.252|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: unspecified [image/svg+xml]\n",
-      "Saving to: ‘user.svg’\n",
-      "\n",
-      "user.svg                [ <=>                ]   1.44K  --.-KB/s    in 0s      \n",
-      "\n",
-      "2024-05-18 06:19:15 (234 MB/s) - ‘user.svg’ saved [1470]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget -O user.svg https://pic.onlinewebfonts.com/thumbnails/icons_308642.svg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7743cc54-42db-4002-a785-60070ddb628e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2024-05-18 06:19:16--  https://img.freepik.com/free-vector/yellow-beauty-butterfly-insect-icon_18591-82368.jpg\n",
-      "Resolving img.freepik.com (img.freepik.com)... 23.215.0.170, 23.215.0.167, 2600:1408:5400:13::17cf:caca, ...\n",
-      "Connecting to img.freepik.com (img.freepik.com)|23.215.0.170|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 75172 (73K) [image/jpeg]\n",
-      "Saving to: ‘butterfly.jpg’\n",
-      "\n",
-      "butterfly.jpg       100%[===================>]  73.41K  --.-KB/s    in 0.005s  \n",
-      "\n",
-      "2024-05-18 06:19:16 (14.6 MB/s) - ‘butterfly.jpg’ saved [75172/75172]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget -O butterfly.jpg https://img.freepik.com/free-vector/yellow-beauty-butterfly-insect-icon_18591-82368.jpg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "64771639-9c66-49a9-ad1f-b5e4dc83a768",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from manim import *\n",
-    "\n",
-    "config.media_width = \"75%\"\n",
-    "config.verbosity = \"WARNING\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "06a8c7a5-ac68-4c18-9d2f-6ad52cda7826",
-   "metadata": {},
-   "source": [
-    "# Latency Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "257cf337-948e-4ef7-aae3-2fb7b5b58602",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "%%manim -qm --format mp4 LatencyVisualization\n",
-    "\n",
-    "from manim import *\n",
-    "\n",
-    "text = \"The cake is a lie\"\n",
-    "text_list = text.split(\" \")\n",
-    "rounds = 2\n",
-    "class LatencyVisualization(Scene):\n",
-    "\n",
-    "    def construct(self):\n",
-    "        # Setup LLM and user\n",
-    "        llm_box = Rectangle(width=2, height=1).set_fill(BLUE, opacity=0.5).set_stroke(BLUE, width=2).to_edge(LEFT)\n",
-    "        llm_text = Text(\"LLM\", color=WHITE).move_to(llm_box.get_center())\n",
-    "        user_svg = SVGMobject(\"user.svg\", fill_color=WHITE, stroke_color=WHITE).scale(0.5).to_edge(RIGHT)\n",
-    "        user_text = Text(\"User\", font_size=20).move_to(user_svg.get_bottom())\n",
-    "        user = VGroup(user_svg, user_text)\n",
-    "        \n",
-    "        title_text = Text(\"Latency\").scale(0.75).to_edge(UP)\n",
-    "        conclusion_text = Text(\"Latency is the time it takes for you to receive a response\").scale(0.5).to_edge(DOWN)\n",
-    "        self.play(FadeIn(llm_box), FadeIn(llm_text), FadeIn(user), Write(title_text), Write(conclusion_text))\n",
-    "\n",
-    "        # Dynamic Packet Flow\n",
-    "        start_point = llm_box.get_right() + RIGHT * 0.5\n",
-    "        end_point = user_svg.get_left() + LEFT * 0.5\n",
-    "\n",
-    "        packets = VGroup()\n",
-    "        \n",
-    "        # Timer setup\n",
-    "        timer = ValueTracker(0)\n",
-    "        timer_text = always_redraw(lambda: Text(f\"Latency: {timer.get_value():.2f}s\", font_size=24).to_corner(UR))\n",
-    "\n",
-    "        self.add(timer_text)\n",
-    "\n",
-    "        def create_packet(word):\n",
-    "            packet = Square(color=BLUE).scale(0.3).move_to(start_point)\n",
-    "            packet_text = Text(word, font_size=12).move_to(packet.get_center())\n",
-    "            packet_with_text = VGroup(packet, packet_text)\n",
-    "            packets.add(packet_with_text)\n",
-    "            self.add(packet_with_text)\n",
-    "            return packet_with_text.animate.move_to(end_point).set_rate_func(rate_functions.linear)\n",
-    "        \n",
-    "        for _ in range(rounds):\n",
-    "            for word in text_list:\n",
-    "                self.wait(0.2)  # Adjust the wait time to change flow rate\n",
-    "                packet_animation = create_packet(word)\n",
-    "                self.play(packet_animation, timer.animate.set_value(timer.get_value() + 2), run_time=2)\n",
-    "                self.play(FadeOut(packets[-1]), run_time=0.5)  # Fade out the last packet (the one that reached the user)\n",
-    "                timer.set_value(0)  # Reset timer for next packet\n",
-    "\n",
-    "        self.wait(1)\n",
-    "        self.play(FadeOut(packets))\n",
-    "\n",
-    "        # Conclusion\n",
-    "        self.wait(2)\n",
-    "        self.play(FadeOut(conclusion_text), FadeOut(llm_box), FadeOut(llm_text), FadeOut(user), FadeOut(timer_text))\n"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "id": "59202547-0e0d-4ea1-a82a-1c12135844ed",
-   "metadata": {},
-   "source": [
-    "%manim -qm --format gif LatencyVisualization"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "id": "9a4c8bd1-c03b-4f35-bb67-8ad3dd25df32",
-   "metadata": {},
-   "source": [
-    "%manim -qm --format webm LatencyVisualization"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8e185f7-8abd-43ed-a3cd-f370553ad58d",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Throughput Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "37ad0a6c-e068-4ace-b31f-2c8c1eb8f52b",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "%%manim -qm --format mp4 ThroughputVisualization\n",
-    "\n",
-    "from manim import *\n",
-    "\n",
-    "class ThroughputVisualization(Scene):\n",
-    "    text = [\"His hands can't hit what he can't see\", \n",
-    "            \"Float like a 🦋, sting like a bee\",\n",
-    "            \"I'm so mean, I make medic -ine sick.\",\n",
-    "            \"Suffer now and retire as a champ -ion\"]\n",
-    "    batch_size = 4\n",
-    "    rounds = 1\n",
-    "\n",
-    "    def construct(self):\n",
-    "        # Split text into words\n",
-    "        words = [sentence.split() for sentence in self.text]\n",
-    "        max_len = max(len(sentence) for sentence in words)\n",
-    "        \n",
-    "        # Extend shorter sentences with empty strings to match the longest sentence\n",
-    "        for sentence in words:\n",
-    "            sentence.extend([''] * (max_len - len(sentence)))\n",
-    "        \n",
-    "        # Transpose to get words from each sentence in each pass\n",
-    "        transposed_words = list(map(list, zip(*words)))\n",
-    "\n",
-    "        # Setup LLM and user\n",
-    "        llm_box = Rectangle(width=2, height=1).set_fill(GREEN, opacity=0.5).set_stroke(GREEN, width=2).to_edge(LEFT).shift(DOWN)\n",
-    "        llm_text = Text(\"LLM\", color=WHITE).move_to(llm_box.get_center())\n",
-    "        user_svg = SVGMobject(\"user.svg\", fill_color=WHITE, stroke_color=WHITE).scale(0.5).to_edge(RIGHT).shift(DOWN)\n",
-    "        user_text = Text(\"User\", font_size=20).move_to(user_svg.get_bottom())\n",
-    "        user = VGroup(user_svg, user_text)\n",
-    "        \n",
-    "        title_text = Text(\"Throughput\").scale(0.75).to_edge(UP)\n",
-    "        conclusion_text = Text(\"Throughput is the amount of data processed in a given time\").scale(0.5).to_edge(DOWN)\n",
-    "        self.play(FadeIn(llm_box), FadeIn(llm_text), FadeIn(user), Write(title_text), Write(conclusion_text))\n",
-    "\n",
-    "        # Dynamic Packet Flow\n",
-    "        start_point = llm_box.get_right() + RIGHT * 0.5\n",
-    "        end_point = user_svg.get_left() + LEFT * 1\n",
-    "        storage_start = start_point + UP * 2.5 + LEFT * 1.5\n",
-    "        \n",
-    "        # Define initial storage positions in a block formation\n",
-    "        storage_positions = [\n",
-    "            storage_start,\n",
-    "            storage_start + RIGHT * 6,\n",
-    "            storage_start + DOWN * 1,\n",
-    "            storage_start + RIGHT * 6 + DOWN * 1,\n",
-    "        ]\n",
-    "        storage_offsets = [0, 0, 0, 0]  # Initialize offsets for each storage position\n",
-    "\n",
-    "        packets = Group()\n",
-    "        \n",
-    "        # Timer setup\n",
-    "        timer = ValueTracker(0)\n",
-    "        timer_text = always_redraw(lambda: Text(f\"Time: {timer.get_value():.2f}s\\nThroughput: 4 tokens/s\", font_size=18).to_corner(UR))\n",
-    "\n",
-    "        self.add(timer_text)\n",
-    "\n",
-    "        def create_packet_group(words):\n",
-    "            packet_group = Group()\n",
-    "            for i, word in enumerate(words):\n",
-    "                row, col = divmod(i, 2)\n",
-    "                packet = Square(color=GREEN).scale(0.3).move_to(start_point + RIGHT * col * 0.6 + DOWN * row * 0.6)\n",
-    "                \n",
-    "                if \"🦋\" in word:\n",
-    "                    butterfly_image = ImageMobject(\"butterfly.jpg\").scale_to_fit_height(0.3)\n",
-    "                    butterfly_image.move_to(packet.get_center())\n",
-    "                    packet_with_image = Group(packet, butterfly_image)\n",
-    "                else:\n",
-    "                    packet_text = Text(word, font_size=12).move_to(packet.get_center())\n",
-    "                    packet_with_image = Group(packet, packet_text)\n",
-    "                \n",
-    "                packet_group.add(packet_with_image)\n",
-    "            packet_group.next_to(llm_box)\n",
-    "            return packet_group\n",
-    "\n",
-    "        for _ in range(self.rounds):\n",
-    "            for word_group in transposed_words:\n",
-    "                if any(word_group):  # Check if there are any non-empty words\n",
-    "                    packet_group = create_packet_group(word_group)\n",
-    "                    packets.add(packet_group)\n",
-    "                    self.play(FadeIn(packet_group))\n",
-    "                    self.play(packet_group.animate.move_to(end_point), timer.animate.set_value(timer.get_value() + 1), run_time=1)\n",
-    "\n",
-    "                    # Determine which storage position to use\n",
-    "                    new_storage_position = []\n",
-    "                    for i, packet in enumerate(packet_group):\n",
-    "                        storage_index = i % self.batch_size\n",
-    "                        storage_position = storage_positions[storage_index] + RIGHT * storage_offsets[storage_index]\n",
-    "                        new_storage_position.append(packet.animate.move_to(storage_position))\n",
-    "                        storage_offsets[storage_index] += 0.6  # Adjust based on the width of packet group\n",
-    "                    self.play(*new_storage_position, run_time=0.5)\n",
-    "\n",
-    "                self.wait(0.1)  # Adjust the wait time to change flow rate\n",
-    "                timer.set_value(0)  # Reset timer for next batch\n",
-    "\n",
-    "        self.wait(1)\n",
-    "\n",
-    "        # Conclusion\n",
-    "        self.wait(2)\n",
-    "        self.play(FadeOut(packets), FadeOut(conclusion_text), FadeOut(llm_box), FadeOut(llm_text), FadeOut(user), FadeOut(timer_text))\n"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "id": "d36ce190-a3ce-461a-b364-91e73b1300da",
-   "metadata": {},
-   "source": [
-    "%manim -qm --format gif ThroughputVisualization"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "code",
-   "id": "7dc2a1dd-489c-40ce-9c61-d097dee8a11d",
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-  },
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-   "execution_count": null,
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