diff --git a/_quarto.yml b/_quarto.yml
index 555d825b..125a4784 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -51,6 +51,7 @@ website:
           - "examples_and_demos/mmpretrain_to_jax.ipynb"
           - "examples_and_demos/image_segmentation_with_ivy_unet.ipynb"
           - "examples_and_demos/alexnet_demo.ipynb"
+          -"examples_and_demos/mlpmixer_demo.ipynb"
 
 format:
   html:
@@ -65,4 +66,4 @@ format:
     execute:
       echo: true
       output: true
-      eval: true
\ No newline at end of file
+      eval: true
diff --git a/examples_and_demos/mlpmixer_demo.ipynb b/examples_and_demos/mlpmixer_demo.ipynb
new file mode 100644
index 00000000..479f189c
--- /dev/null
+++ b/examples_and_demos/mlpmixer_demo.ipynb
@@ -0,0 +1,553 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Ivy MLP Mixer Demo"
+      ],
+      "metadata": {
+        "id": "QYPzLwARXmvF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In this demo, we show how a [MLP Mixer](https://arxiv.org/pdf/2105.01601.pdf) model written in Ivy native code, can be used for image classification, and integrated with all three of the major ML frameworks: TensorFlow, PyTorch and JAX."
+      ],
+      "metadata": {
+        "id": "kOVCkJvwXvVV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Installing Ivy"
+      ],
+      "metadata": {
+        "id": "3y4tjrZwoCZM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Since we want the packages to be available after installing, after running the first two cells, the notebook will automatically restart."
+      ],
+      "metadata": {
+        "id": "AZTmwNqemvLS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4xvWcWI9nc8P"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/unifyai/ivy.git\n",
+        "%cd ivy\n",
+        "!pip install --user -e .\n",
+        "!pip install colorama\n",
+        "!pip install huggingface_hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -q dm-haiku\n",
+        "!git clone https://github.com/unifyai/models.git\n",
+        "\n",
+        "# Using a stable commit from models repository to ensure compatibility with Ivy 😄\n",
+        "!cd models/ && pip install .\n",
+        "\n",
+        "exit()"
+      ],
+      "metadata": {
+        "id": "MR-dEsF1V3Gm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Initialising Compiler API Key"
+      ],
+      "metadata": {
+        "id": "dnps7QF7oF16"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Replace `API_KEY` with your API key"
+      ],
+      "metadata": {
+        "id": "x-6ZFcvom5UQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "API_KEY = \"API_KEY\"\n",
+        "!mkdir -p .ivy\n",
+        "!echo -n $API_KEY > .ivy/key.pem"
+      ],
+      "metadata": {
+        "id": "TYxrxb2qn-av"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Importing Ivy"
+      ],
+      "metadata": {
+        "id": "--OSdfjPoMtS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import ivy"
+      ],
+      "metadata": {
+        "id": "Uue8IJ15n_q6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Loading and preprocessing a sample image"
+      ],
+      "metadata": {
+        "id": "BzBeTOtdpqYL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "filename = \"horse.jpg\""
+      ],
+      "metadata": {
+        "id": "NHxsjaqcVfG-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Loading the image\n",
+        "from PIL import Image\n",
+        "import numpy as np\n",
+        "\n",
+        "img = Image.open(filename)\n",
+        "img = np.asarray(img)"
+      ],
+      "metadata": {
+        "id": "wqGPKsMipp-d"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Preprocess the image\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from keras import layers\n",
+        "\n",
+        "def preprocess():\n",
+        "  transform = keras.Sequential([\n",
+        "                tf.keras.layers.experimental.preprocessing.Normalization(\n",
+        "                    mean=(0.5, 0.5, 0.5), variance=(0.25, 0.25, 0.25)\n",
+        "                ),\n",
+        "                tf.keras.layers.experimental.preprocessing.Resizing(72, 72),\n",
+        "            ]\n",
+        "        )\n",
+        "  return transform\n",
+        "\n",
+        "prepoc = preprocess()\n",
+        "tf_img = prepoc(img)\n",
+        "tf_img = tf.expand_dims(tf_img, 0)\n",
+        "\n",
+        "img = tf_img.numpy()"
+      ],
+      "metadata": {
+        "id": "Qerl2luT6MIE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import Image, display\n",
+        "display(Image(filename))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 699
+        },
+        "id": "Mig6X_VOVUW5",
+        "outputId": "a3492fe0-39f2-4109-8d6f-d9cd359571a7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/jpeg": "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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Getting the different classes in\n",
+        "categories = ['plane', 'automobile', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
+      ],
+      "metadata": {
+        "id": "60gSFuKxXUv1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Ivy MLP Mixer Inference In Tensorflow"
+      ],
+      "metadata": {
+        "id": "W8pooFtmCYhY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Here we import the [MLP Mixer Implementation](https://colab.research.google.com/drive/18cVLO7RaznNHkkRboWcZvvGeAVeX60hQ#scrollTo=clM2QrQX87hk&line=1&uniqifier=1) in Native Ivy."
+      ],
+      "metadata": {
+        "id": "8cbA32W-oKy5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import ivy\n",
+        "ivy.set_backend(\"tensorflow\")\n",
+        "\n",
+        "from ivy_models.mlpmixer import mlpmixer\n",
+        "ivy_mlpmixer = mlpmixer()"
+      ],
+      "metadata": {
+        "id": "9WxUkfwj7stc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In order to make sure the model is as quick as possible, we can call ivy.compile(). This can take a moment, but is a one-time cost."
+      ],
+      "metadata": {
+        "id": "SZYyVLBToltz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ivy_mlpmixer = ivy.compile(ivy_mlpmixer, args=(ivy.asarray(tf_img),))"
+      ],
+      "metadata": {
+        "id": "lis4jvhU7wQz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# output = ivy.softmax(ivy_mlpmixer(ivy.asarray(tf_img)))  # pass the image to the model\n",
+        "output = ivy_mlpmixer(ivy.asarray(img))\n",
+        "classes = ivy.argsort(output[0], descending=True)[:3]  # get the top 3 classes\n",
+        "logits = ivy.gather(output[0], classes)  # get the logits\n",
+        "\n",
+        "print(\"Indices of the top 3 classes are:\", classes)\n",
+        "print(\"Logits of the top 3 classes are:\", logits)\n",
+        "print(\"Categories of the top 3 classes are:\", [categories[i] for i in classes.to_list()])"
+      ],
+      "metadata": {
+        "id": "CTz1Xbxx76ZM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "83b4c48d-b0a1-4c72-9a9a-f4bb555c3478"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Indices of the top 3 classes are: ivy.array([7, 6, 9], dev=gpu:0)\n",
+            "Logits of the top 3 classes are: ivy.array([0.43874821, 0.12901554, 0.10044083], dev=gpu:0)\n",
+            "Categories of the top 3 classes are: ['horse', 'frog', 'truck']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Pytorch Inference"
+      ],
+      "metadata": {
+        "id": "2XlcNXeRCQFB"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Using the model with a PyTorch Backend"
+      ],
+      "metadata": {
+        "id": "O0lwGBvro86y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import ivy\n",
+        "import torch\n",
+        "ivy.set_backend(\"torch\")\n",
+        "\n",
+        "ivy_mlpmixer = mlpmixer()"
+      ],
+      "metadata": {
+        "id": "N85PGdPR6nLu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Speeding up inference using `ivy.compile`"
+      ],
+      "metadata": {
+        "id": "FbWGnSPdpJ3d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ivy_mlpmixer = ivy.compile(ivy_mlpmixer, args=(ivy.asarray(img),))"
+      ],
+      "metadata": {
+        "id": "zAK36TME7LvV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "output = ivy_mlpmixer(ivy.asarray(img)) # pass the image to the model\n",
+        "classes = ivy.argsort(output[0], descending=True)[:3]  # get the top 3 classes\n",
+        "logits = ivy.gather(output[0], classes)  # get the logits\n",
+        "\n",
+        "print(\"Indices of the top 3 classes are:\", classes)\n",
+        "print(\"Logits of the top 3 classes are:\", logits)\n",
+        "print(\"Categories of the top 3 classes are:\", [categories[i] for i in classes.to_list()])"
+      ],
+      "metadata": {
+        "id": "BjkgMDrY7Uj3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b5fd5a16-0ea8-4e14-8c3a-ca484fded6bb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Indices of the top 3 classes are: ivy.array([3, 0, 7])\n",
+            "Logits of the top 3 classes are: ivy.array([0.5907377 , 0.18112788, 0.13967107])\n",
+            "Categories of the top 3 classes are: ['cat', 'plane', 'horse']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Jax Inference"
+      ],
+      "metadata": {
+        "id": "71GpxP72Cn5Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import jax\n",
+        "ivy.set_backend(\"jax\")\n",
+        "ivy_mlpmixer = mlpmixer()"
+      ],
+      "metadata": {
+        "id": "uAzdJvwLCpC6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ivy_mlpmixer = ivy.compile(ivy_mlpmixer, args=(ivy.asarray(img),))"
+      ],
+      "metadata": {
+        "id": "ys_c1akj8ui4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "output = ivy_mlpmixer(ivy.asarray(img))  # pass the image to the model\n",
+        "classes = ivy.argsort(output[0], descending=True)[:3]  # get the top 3 classes\n",
+        "logits = ivy.gather(output[0], classes)  # get the logits\n",
+        "\n",
+        "print(\"Indices of the top 3 classes are:\", classes)\n",
+        "print(\"Logits of the top 3 classes are:\", logits)\n",
+        "print(\"Categories of the top 3 classes are:\", [categories[i] for i in classes.to_list()])"
+      ],
+      "metadata": {
+        "id": "_wUlpc6S8vDs",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "488d0a12-9d49-4168-dfb4-44bb483f3e78"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Indices of the top 3 classes are: ivy.array([1, 0, 4], dev=gpu:0)\n",
+            "Logits of the top 3 classes are: ivy.array([0.37148738, 0.25912502, 0.17902127], dev=gpu:0)\n",
+            "Categories of the top 3 classes are: ['automobile', 'plane', 'deer']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## MLP Mixer Implementation"
+      ],
+      "metadata": {
+        "id": "fBJ_1Ilg84uJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class MLPBlock(ivy.Module):\n",
+        "  def __init__(self,inp_dim,mlp_dim):\n",
+        "    self.linear0 = ivy.Linear(inp_dim,mlp_dim)\n",
+        "    self.gelu = ivy.GELU()\n",
+        "    self.linear1 = ivy.Linear(mlp_dim,inp_dim)\n",
+        "    self.dropout = ivy.Dropout(0.25)\n",
+        "    super(MLPBlock, self).__init__()\n",
+        "\n",
+        "  def _forward(self,x):\n",
+        "    y = self.linear0(x)\n",
+        "    y = self.gelu(y)\n",
+        "    y = self.dropout(y)\n",
+        "    y = self.linear1(y)\n",
+        "    y = self.dropout(y)\n",
+        "    return y\n",
+        "\n",
+        "class MixerBlock(ivy.Module):\n",
+        "  def __init__(self,hidden_dim,num_patches,token_mlp_dim,channel_mlp_dim):\n",
+        "    self.norm1 = ivy.LayerNorm([hidden_dim])\n",
+        "    self.mlp_block1 = MLPBlock(num_patches,token_mlp_dim)\n",
+        "    self.norm2 = ivy.LayerNorm([hidden_dim])\n",
+        "    self.mlp_block2 = MLPBlock(hidden_dim,channel_mlp_dim)\n",
+        "    super(MixerBlock, self).__init__()\n",
+        "\n",
+        "  def _forward(self,x):\n",
+        "    y = self.norm1(x)\n",
+        "    y = ivy.matrix_transpose(y)\n",
+        "    y = self.mlp_block1(y)\n",
+        "    y = ivy.matrix_transpose(y)\n",
+        "    x = x + y\n",
+        "    y = self.norm2(x)\n",
+        "    y = x + self.mlp_block2(y)\n",
+        "    return y\n",
+        "\n",
+        "class MLPMixer(ivy.Module):\n",
+        "  def __init__(self,vol,inp_dim,num_classes=10,num_blocks=2,patch_size=16,hidden_dim=128,token_mlp_dim=64,channel_mlp_dim=32):\n",
+        "    self.num_blocks = num_blocks\n",
+        "    self.conv = ivy.Conv2D(inp_dim,hidden_dim,[patch_size,patch_size],[patch_size,patch_size],0)\n",
+        "    self.num_patches = int((vol/patch_size)**2)\n",
+        "    self.mixer_block = MixerBlock(hidden_dim,self.num_patches,token_mlp_dim , channel_mlp_dim)\n",
+        "    self.norm = ivy.LayerNorm([hidden_dim])\n",
+        "    self.dropout = ivy.Dropout(0.25)\n",
+        "    self.linear = ivy.Linear(hidden_dim,num_classes)\n",
+        "    super(MLPMixer, self).__init__()\n",
+        "\n",
+        "  def _forward(self,x):\n",
+        "    x = self.conv(x)\n",
+        "    x = x.reshape((int(x.shape[0]),int(x.shape[1])*int(x.shape[2]),int(x.shape[3])))\n",
+        "    for i in range(self.num_blocks):\n",
+        "      x = self.mixer_block(x)\n",
+        "    x = self.norm(x)\n",
+        "    x = self.dropout(x)\n",
+        "    x = ivy.mean(x,axis=1)\n",
+        "    logits = self.linear(x)\n",
+        "\n",
+        "    return logits"
+      ],
+      "metadata": {
+        "id": "clM2QrQX87hk"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/index.qmd b/index.qmd
index f5e6200f..98bf0216 100644
--- a/index.qmd
+++ b/index.qmd
@@ -29,6 +29,7 @@ listing:
         - "examples_and_demos/mmpretrain_to_jax.ipynb"
         - "examples_and_demos/image_segmentation_with_ivy_unet.ipynb"
         - "examples_and_demos/alexnet_demo.ipynb"
+        - "examples_and_demos/mlpmixer_demo.ipynb"
       sort: "false"
 ---
 
@@ -58,4 +59,4 @@ If you are in a rush, you can jump straight into the **[Quickstart](quickstart.i
 
 ### Examples and Demos
 :::{#examples-and-demos}
-:::
\ No newline at end of file
+:::