diff --git a/keras_cv/models/feature_extractor/clip/clip_model.py b/keras_cv/models/feature_extractor/clip/clip_model.py
index e81dbd5d09..c3e6d49caf 100644
--- a/keras_cv/models/feature_extractor/clip/clip_model.py
+++ b/keras_cv/models/feature_extractor/clip/clip_model.py
@@ -72,7 +72,7 @@ def __init__(
         vision_patch_size=32,
         context_length=77,
         vocab_size=49408,
-        transformer_width=768,
+        transformer_width=512,
         transformer_heads=8,
         transformer_layers=12,
         **kwargs,
diff --git a/keras_cv/models/feature_extractor/clip/clip_model_test.py b/keras_cv/models/feature_extractor/clip/clip_model_test.py
index d5c777c653..14304b73ef 100644
--- a/keras_cv/models/feature_extractor/clip/clip_model_test.py
+++ b/keras_cv/models/feature_extractor/clip/clip_model_test.py
@@ -52,9 +52,9 @@ def test_clip_model_golden_values(self):
             processed_image, processed_text, attention_mask
         )
         print(image_logits)
-        self.assertAllClose(image_logits, [[2.932678, 2.932678, 2.932675]])
+        self.assertAllClose(image_logits, [[1.896713, 1.896713, 1.896713]])
         self.assertAllClose(
-            text_logits, ops.transpose([[2.932678, 2.932678, 2.932675]])
+            text_logits, ops.transpose([[1.896713, 1.896713, 1.896713]])
         )
 
     def test_clip_preprocessor(self):
@@ -77,8 +77,8 @@ def test_clip_preprocessor_tf_data(self):
 
     @pytest.mark.large
     def test_presets(self):
-        self.skipTest("TODO: Enable after Kaggle model is public")
-        model = CLIP.from_preset("clip-vit-base-patch32")
+        # self.skipTest("TODO: Enable after Kaggle model is public")
+        model = CLIP.from_preset("clip-vit-base-patch16")
         processed_image = np.ones(shape=[1, 224, 224, 3])
         processed_text = np.ones(shape=[3, 77])
         attention_mask = np.ones(shape=[3, 77])
@@ -109,7 +109,7 @@ def test_text_encoder_golden_values(self):
         print(model.text_embeddings)
         self.assertAllClose(
             model.text_embeddings[0, :3],
-            [-0.018502, 0.000906, 0.020372],
+            [0.007531, -0.038361, -0.035686],
         )
 
     @pytest.mark.large  # Saving is slow, so mark these large.
diff --git a/keras_cv/models/feature_extractor/clip/clip_presets.py b/keras_cv/models/feature_extractor/clip/clip_presets.py
index 6b4d98727e..656c9ad8ed 100644
--- a/keras_cv/models/feature_extractor/clip/clip_presets.py
+++ b/keras_cv/models/feature_extractor/clip/clip_presets.py
@@ -28,7 +28,7 @@
             "official_name": "CLIP",
             "path": "clip",
         },
-        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch16/2",
+        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch16/4",
     },
     "clip-vit-base-patch32": {
         "metadata": {
@@ -44,7 +44,7 @@
             "official_name": "CLIP",
             "path": "clip",
         },
-        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch32/2",
+        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch32/4",
     },
     "clip-vit-large-patch14": {
         "metadata": {
@@ -60,7 +60,7 @@
             "official_name": "CLIP",
             "path": "clip",
         },
-        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14/2",
+        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14/4",
     },
     "clip-vit-large-patch14-336": {
         "metadata": {
@@ -76,6 +76,6 @@
             "official_name": "CLIP",
             "path": "clip",
         },
-        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14-336/2",  # noqa: E501
+        "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14-336/4",  # noqa: E501
     },
 }
diff --git a/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb b/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb
index 13e443669a..ff3bb4c991 100644
--- a/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb
+++ b/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb
@@ -17,7 +17,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "cRzYR-oFgxt1",
-    "outputId": "e4b01fcd-9f71-4ba7-b8a2-1796f7ef260d"
+    "outputId": "80b8db20-da09-43bd-9b70-fad93b1e1ca1"
    },
    "outputs": [
     {
@@ -27,30 +27,14 @@
       "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
       "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
       "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
       "\u001b[?25h  Building wheel for keras-cv (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m415.4/415.4 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hCollecting keras==3.0.2\n",
-      "  Downloading keras-3.0.2-py3-none-any.whl (1.0 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hRequirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (1.4.0)\n",
-      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (1.23.5)\n",
-      "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (13.7.0)\n",
-      "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (0.0.7)\n",
-      "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (3.9.0)\n",
-      "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (0.1.8)\n",
-      "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras==3.0.2) (3.0.0)\n",
-      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras==3.0.2) (2.16.1)\n",
-      "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras==3.0.2) (0.1.2)\n",
-      "Installing collected packages: keras\n",
-      "  Attempting uninstall: keras\n",
-      "    Found existing installation: keras 2.15.0\n",
-      "    Uninstalling keras-2.15.0:\n",
-      "      Successfully uninstalled keras-2.15.0\n",
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.2/465.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
       "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.0.2 which is incompatible.\u001b[0m\u001b[31m\n",
-      "\u001b[0mSuccessfully installed keras-3.0.2\n"
+      "\u001b[0m"
      ]
     }
    ],
@@ -59,29 +43,7 @@
     "!pip install -q keras-nlp\n",
     "!pip install -q tf-keras\n",
     "!pip install -q tensorflow-text\n",
-    "!pip install keras==3.0.2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mdGT8Em4Mc4b"
-   },
-   "source": [
-    "# Import"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GDvJmQuug4-x"
-   },
-   "outputs": [],
-   "source": [
-    "from keras_cv.models.feature_extractor.clip import CLIPProcessor\n",
-    "import keras\n",
-    "from keras_cv.models import CLIP"
+    "!pip install -q keras==3.0.2"
    ]
   },
   {
@@ -92,48 +54,94 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "nuFgha2jTshi",
-    "outputId": "b99d73eb-cc97-47d0-f46e-687c9e8b8237"
+    "outputId": "63d4160e-42b3-4f6b-e672-ba30c9402d25"
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-02-02 22:19:20--  https://i.imgur.com/8H7XCH0.jpg\n",
-      "Resolving i.imgur.com (i.imgur.com)... 151.101.52.193\n",
-      "Connecting to i.imgur.com (i.imgur.com)|151.101.52.193|:443... connected.\n",
+      "--2024-02-21 20:54:06--  https://i.imgur.com/8H7XCH0.jpg\n",
+      "Resolving i.imgur.com (i.imgur.com)... 146.75.76.193\n",
+      "Connecting to i.imgur.com (i.imgur.com)|146.75.76.193|:443... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 44544 (44K) [image/jpeg]\n",
       "Saving to: ‘cat.jpg’\n",
       "\n",
       "\rcat.jpg               0%[                    ]       0  --.-KB/s               \rcat.jpg             100%[===================>]  43.50K  --.-KB/s    in 0.01s   \n",
       "\n",
-      "2024-02-02 22:19:20 (3.58 MB/s) - ‘cat.jpg’ saved [44544/44544]\n",
+      "2024-02-21 20:54:06 (4.16 MB/s) - ‘cat.jpg’ saved [44544/44544]\n",
       "\n",
-      "--2024-02-02 22:19:20--  http://images.cocodataset.org/val2017/000000039769.jpg\n",
-      "Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.78.4, 3.5.1.13, 52.217.139.73, ...\n",
-      "Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.78.4|:80... connected.\n",
+      "--2024-02-21 20:54:06--  http://images.cocodataset.org/val2017/000000039769.jpg\n",
+      "Resolving images.cocodataset.org (images.cocodataset.org)... 52.217.206.137, 16.182.42.89, 54.231.201.177, ...\n",
+      "Connecting to images.cocodataset.org (images.cocodataset.org)|52.217.206.137|:80... connected.\n",
       "HTTP request sent, awaiting response... 200 OK\n",
       "Length: 173131 (169K) [image/jpeg]\n",
-      "Saving to: ‘test.jpg’\n",
+      "Saving to: ‘two_cats.jpg’\n",
+      "\n",
+      "two_cats.jpg        100%[===================>] 169.07K  --.-KB/s    in 0.09s   \n",
       "\n",
-      "test.jpg            100%[===================>] 169.07K  --.-KB/s    in 0.06s   \n",
+      "2024-02-21 20:54:07 (1.77 MB/s) - ‘two_cats.jpg’ saved [173131/173131]\n",
       "\n",
-      "2024-02-02 22:19:20 (2.67 MB/s) - ‘test.jpg’ saved [173131/173131]\n",
+      "--2024-02-21 20:54:07--  https://i.imgur.com/PpgZzP4.jpeg\n",
+      "Resolving i.imgur.com (i.imgur.com)... 146.75.76.193\n",
+      "Connecting to i.imgur.com (i.imgur.com)|146.75.76.193|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1610285 (1.5M) [image/jpeg]\n",
+      "Saving to: ‘mountain.jpg’\n",
+      "\n",
+      "mountain.jpg        100%[===================>]   1.54M  --.-KB/s    in 0.06s   \n",
+      "\n",
+      "2024-02-21 20:54:07 (27.6 MB/s) - ‘mountain.jpg’ saved [1610285/1610285]\n",
       "\n"
      ]
     }
    ],
    "source": [
     "!wget https://i.imgur.com/8H7XCH0.jpg -O cat.jpg\n",
-    "!wget http://images.cocodataset.org/val2017/000000039769.jpg -O test.jpg"
+    "!wget http://images.cocodataset.org/val2017/000000039769.jpg -O two_cats.jpg\n",
+    "!wget https://i.imgur.com/PpgZzP4.jpeg -O mountain.jpg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mdGT8Em4Mc4b"
+   },
+   "source": [
+    "# Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0mtj1abS2cVf"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"KERAS_BACKEND\"] = \"torch\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "GDvJmQuug4-x"
+   },
+   "outputs": [],
+   "source": [
+    "from keras_cv.models.feature_extractor.clip import CLIPProcessor\n",
+    "import keras\n",
+    "from keras_cv.models import CLIP"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "cellView": "form",
     "id": "X3kkmK6h_gFH"
    },
    "outputs": [],
@@ -190,8 +198,8 @@
     "    },\n",
     "}\n",
     "model_map_hf = {\n",
-    "    \"CLIP_B16\": \"openai/clip-vit-base-patch32\",\n",
-    "    \"CLIP_B32\": \"openai/clip-vit-base-patch16\",\n",
+    "    \"CLIP_B16\": \"openai/clip-vit-base-patch16\",\n",
+    "    \"CLIP_B32\": \"openai/clip-vit-base-patch32\",\n",
     "    \"CLIP_L14\": \"openai/clip-vit-large-patch14\",\n",
     "    \"CLIP_L14_336\": \"openai/clip-vit-large-patch14-336\",\n",
     "}\n",
@@ -249,7 +257,7 @@
      "height": 193
     },
     "id": "uE6x7gfqa3Ee",
-    "outputId": "9a080569-7ab9-49ad-8589-87f335ef2f31"
+    "outputId": "f55fc358-04a4-42ce-c397-3f81a238ab1e"
    },
    "outputs": [
     {
@@ -293,11 +301,11 @@
     {
      "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=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,425</span> (154.00 KB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1</span> (4.00 B)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m39,425\u001b[0m (154.00 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1\u001b[0m (4.00 B)\n"
       ]
      },
      "metadata": {},
@@ -306,11 +314,11 @@
     {
      "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=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">39,425</span> (154.00 KB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1</span> (4.00 B)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m39,425\u001b[0m (154.00 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1\u001b[0m (4.00 B)\n"
       ]
      },
      "metadata": {},
@@ -342,14 +350,23 @@
    },
    "outputs": [],
    "source": [
-    "processor = CLIPProcessor(224, \"vocab.json\", \"merges.txt\")\n",
-    "image = processor.process_images([\"cat.jpg\"])\n",
-    "text_input = [\n",
-    "    \"photo of a cat on a tortoise\",\n",
-    "    \"tortoise on a dog\",\n",
-    "    \"a photo of a tortoise\",\n",
-    "]\n",
-    "text = processor.process_texts(text_input)"
+    "processor = CLIPProcessor(\n",
+    "    MODEL_CONFIGS[config_name][\"image_resolution\"], \"vocab.json\", \"merges.txt\"\n",
+    ")\n",
+    "image = processor.process_images([\"two_cats.jpg\"])\n",
+    "text_input = [\"mountains\", \"cat on tortoise\", \"two cats\"]\n",
+    "text, attention_mask = processor.process_texts(text_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BHSpMv0PT5SX"
+   },
+   "outputs": [],
+   "source": [
+    "image_logits, text_logits = model(image, text, attention_mask)"
    ]
   },
   {
@@ -359,24 +376,23 @@
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
-    "id": "BHSpMv0PT5SX",
-    "outputId": "566c92c4-fbf3-4e2d-87f1-6112b2cff96f"
+    "id": "JPn0gACJjKy5",
+    "outputId": "cbc7313a-4ddd-4021-9e84-fa668987849d"
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tf.Tensor([[ 0.42190465  0.6262117  -0.2368357 ]], shape=(1, 3), dtype=float32)\n",
-      "tortoise on a dog\n"
-     ]
+     "data": {
+      "text/plain": [
+       "tensor([[3.7318, 3.7792, 3.7633]], grad_fn=<MulBackward0>)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "image_logits, text_logits = model(image, text)\n",
-    "output = keras.layers.Softmax()(image_logits)\n",
-    "print(image_logits)\n",
-    "print(text_input[keras.ops.argmax(output)])"
+    "image_logits"
    ]
   },
   {
@@ -388,7 +404,7 @@
      "height": 193
     },
     "id": "GgNBvYCTtmA3",
-    "outputId": "35b9a26c-325e-4535-c33b-3f67ab112e19"
+    "outputId": "a667a9e5-397e-4299-fdc1-8899621112ad"
    },
    "outputs": [
     {
@@ -410,9 +426,9 @@
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\"> Layer (type)                       </span>┃<span style=\"font-weight: bold\"> Output Shape                  </span>┃<span style=\"font-weight: bold\">     Param # </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
-       "│ image_encoder (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CLIPImageEncoder</span>)   │ ?                             │  <span style=\"color: #00af00; text-decoration-color: #00af00\">87,849,216</span> │\n",
+       "│ image_encoder (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CLIPImageEncoder</span>)   │ ?                             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">304,293,888</span> │\n",
        "├────────────────────────────────────┼───────────────────────────────┼─────────────┤\n",
-       "│ text_encoder (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CLIPTextEncoder</span>)     │ ?                             │  <span style=\"color: #00af00; text-decoration-color: #00af00\">63,428,096</span> │\n",
+       "│ text_encoder (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CLIPTextEncoder</span>)     │ ?                             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">123,650,304</span> │\n",
        "└────────────────────────────────────┴───────────────────────────────┴─────────────┘\n",
        "</pre>\n"
       ],
@@ -420,9 +436,9 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
-       "│ image_encoder (\u001b[38;5;33mCLIPImageEncoder\u001b[0m)   │ ?                             │  \u001b[38;5;34m87,849,216\u001b[0m │\n",
+       "│ image_encoder (\u001b[38;5;33mCLIPImageEncoder\u001b[0m)   │ ?                             │ \u001b[38;5;34m304,293,888\u001b[0m │\n",
        "├────────────────────────────────────┼───────────────────────────────┼─────────────┤\n",
-       "│ text_encoder (\u001b[38;5;33mCLIPTextEncoder\u001b[0m)     │ ?                             │  \u001b[38;5;34m63,428,096\u001b[0m │\n",
+       "│ text_encoder (\u001b[38;5;33mCLIPTextEncoder\u001b[0m)     │ ?                             │ \u001b[38;5;34m123,650,304\u001b[0m │\n",
        "└────────────────────────────────────┴───────────────────────────────┴─────────────┘\n"
       ]
      },
@@ -432,11 +448,11 @@
     {
      "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=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">151,277,313</span> (577.08 MB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">427,944,193</span> (1.59 GB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m151,277,313\u001b[0m (577.08 MB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m427,944,193\u001b[0m (1.59 GB)\n"
       ]
      },
      "metadata": {},
@@ -445,11 +461,11 @@
     {
      "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=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">151,277,313</span> (577.08 MB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">427,944,193</span> (1.59 GB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m151,277,313\u001b[0m (577.08 MB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m427,944,193\u001b[0m (1.59 GB)\n"
       ]
      },
      "metadata": {},
@@ -502,25 +518,239 @@
    "execution_count": null,
    "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/"
+     "base_uri": "https://localhost:8080/",
+     "height": 432,
+     "referenced_widgets": [
+      "46636db47838400cb7407fc2ab0720eb",
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+      "c9fdbe12b7934fd8accc09645df0f214",
+      "a5b8a21a005d4ef7b253b782dae3b88d",
+      "002207337b4b47c9a6b8ddda823128ba",
+      "f359ba8ef0cf4841b40acafcd770480c",
+      "0861f873665f4514abfd2b09ad944ab8",
+      "b8924ff1bbb5409c8dc141e65c4cdcdb",
+      "4b62b742aacb45c7a286a5d85a0194da",
+      "dfbe3622fad14e798c9f413a134fd107",
+      "0eda9fca096945a48059cbc2b1c9ffdf",
+      "e2bc0bc25b5044abb8bb1c752935922e",
+      "b1b3b62f8d7545938d1e2d8f8757c589",
+      "2d744d00de6745bda5835f0cd66e3909",
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+      "f0af662a1a884fb78c693ccf0d0b6d8e",
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+      "4ec11a213b0d4fdd8300c0ea5a8f8db7",
+      "49807785ba664c49a6b2395ebe7fbec8",
+      "ec7bc6e82f2042b8b29a6f21e6db1709",
+      "609cc0908e6f4edd95306f72b40afd0c",
+      "d5611bb67e8d49f19e2700652d5309c1",
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+      "b1cc5c487a364d3ba8d33e0aa3b2a305",
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+      "2d6ea61d0fa84510b44fff80ab10e553",
+      "098832b366c6410b824d2c210222dc24",
+      "e7e79c91380c478dabb2b1e7ddca647e"
+     ]
     },
     "id": "EntuvOq1MhwU",
-    "outputId": "e154a367-2f94-4fa1-e97d-d2f32db7a2cf"
+    "outputId": "cbd7cd77-6d8f-4a76-dae0-24530c12eeb6"
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n",
-      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"bos_token_id\"]` will be overriden.\n",
-      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"eos_token_id\"]` will be overriden.\n"
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46636db47838400cb7407fc2ab0720eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/4.76k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f359ba8ef0cf4841b40acafcd770480c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/1.71G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
+      "  return self.fget.__get__(instance, owner)()\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3af19b8b653c4b21a65f7e96dd463aac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c21455d9faa4112ba6f18819f7ef038",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/844 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "418143d2ad92458094259dfca0a747cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/862k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c06b5a6588eb42189210d1c20ccba87a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79020bd42626472a85bf9047d014830f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/2.22M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec7bc6e82f2042b8b29a6f21e6db1709",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/389 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "model_hf = CM.from_pretrained(config_name_hf)\n",
-    "processor = CP.from_pretrained(config_name_hf)"
+    "processor_hf = CP.from_pretrained(config_name_hf)"
    ]
   },
   {
@@ -531,32 +761,35 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "Ep8DRTkv3AwS",
-    "outputId": "770756bc-8829-484f-b6e5-763fe81e24d0"
+    "outputId": "6e3e802c-3db6-48ac-e3ab-4f52416449a8"
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor([[0.9957, 0.0023, 0.0020]], grad_fn=<SoftmaxBackward0>)"
+       "tensor([[11.7865, 31.2010, 11.9718]], grad_fn=<TBackward0>)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "url = \"https://i.imgur.com/8H7XCH0.jpg\"\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "photo = {\n",
+    "    \"cat\": \"https://i.imgur.com/8H7XCH0.jpg\",\n",
+    "    \"two_cats\": \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n",
+    "    \"mountain\": \"https://i.imgur.com/PpgZzP4.jpeg\",\n",
+    "}\n",
+    "url = photo[\"cat\"]\n",
     "image_hf = Image.open(requests.get(url, stream=True).raw)\n",
-    "text_inputs = [\n",
-    "    \"photo of a cat on a tortoise\",\n",
-    "    \"tortoise on a dog\",\n",
-    "    \"a photo of a tortoise\",\n",
-    "]\n",
-    "inputs = processor(\n",
+    "text_inputs = [\"mountains\", \"cat on tortoise\", \"two dogs\"]\n",
+    "inputs = processor_hf(\n",
     "    text=text_inputs, images=image_hf, return_tensors=\"pt\", padding=True\n",
     ")\n",
-    "\n",
     "outputs = model_hf(**inputs)\n",
     "logits_per_image = (\n",
     "    outputs.logits_per_image\n",
@@ -564,28 +797,28 @@
     "probs = logits_per_image.softmax(\n",
     "    dim=1\n",
     ")  # we can take the softmax to get the label probabilitiesprobs\n",
-    "probs"
+    "logits_per_image"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
-    "id": "wPa0cVnY3cBC"
+    "id": "ArkCHlVZVKfM"
    },
-   "outputs": [],
    "source": [
-    "# hugging face weights\n",
-    "hf_wts = model_hf.state_dict()"
+    "# Copy weights"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
-    "id": "ArkCHlVZVKfM"
+    "id": "wPa0cVnY3cBC"
    },
+   "outputs": [],
    "source": [
-    "# Copy weights"
+    "# hugging face weights\n",
+    "hf_wts = model_hf.state_dict()"
    ]
   },
   {
@@ -607,17 +840,17 @@
    "source": [
     "model.logit_scale.assign(hf_wts.pop(\"logit_scale\").numpy())\n",
     "model.get_layer(\"image_encoder\").get_layer(\n",
-    "    \"clip_patching_and_embedding\"\n",
+    "    \"clip_patch_embedding\"\n",
     ").class_embedding.assign(\n",
-    "    hf_wts.pop(\"vision_model.embeddings.class_embedding\").numpy()\n",
+    "    hf_wts.pop(\"vision_model.embeddings.class_embedding\").numpy().T\n",
     ")\n",
     "model.get_layer(\"image_encoder\").get_layer(\n",
-    "    \"clip_patching_and_embedding\"\n",
+    "    \"clip_patch_embedding\"\n",
     ").positional_embedding.assign(\n",
     "    hf_wts.pop(\"vision_model.embeddings.position_embedding.weight\").numpy()\n",
     ")\n",
     "model.get_layer(\"image_encoder\").get_layer(\n",
-    "    \"clip_patching_and_embedding\"\n",
+    "    \"clip_patch_embedding\"\n",
     ").conv1.weights[0].assign(\n",
     "    hf_wts.pop(\"vision_model.embeddings.patch_embedding.weight\")\n",
     "    .permute(3, 2, 1, 0)\n",
@@ -637,14 +870,14 @@
     ")\n",
     "model.get_layer(\"image_encoder\").get_layer(\"vision_projector\").weights[\n",
     "    0\n",
-    "].assign(hf_wts.pop(\"visual_projection.weight\").transpose(1, 0).numpy())"
+    "].assign(hf_wts.pop(\"visual_projection.weight\").numpy().T)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "qptfuWobZcbT"
+    "id": "YRXC2HZC3FjG"
    },
    "outputs": [],
    "source": [
@@ -653,104 +886,93 @@
     "        residual_attention = f\"residual_attention\"\n",
     "    else:\n",
     "        residual_attention = f\"residual_attention_{i}\"\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.q_proj.weights[0].assign(\n",
-    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.weight\")\n",
+    "\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.q_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.q_proj.weights[1].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.q_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.k_proj.weights[0].assign(\n",
-    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.weight\")\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.k_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.k_proj.weights[1].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.k_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.v_proj.weights[0].assign(\n",
-    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.weight\")\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.v_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.v_proj.weights[1].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.v_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.out_proj.weights[1].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.out_proj.weights[1].assign(\n",
     "        hf_wts.pop(\n",
     "            f\"vision_model.encoder.layers.{i}.self_attn.out_proj.bias\"\n",
     "        ).numpy()\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).attn.out_proj.weights[0].assign(\n",
-    "        hf_wts.pop(\n",
-    "            f\"vision_model.encoder.layers.{i}.self_attn.out_proj.weight\"\n",
-    "        ).numpy()\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.out_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.out_proj.weight\")\n",
+    "        .numpy()\n",
+    "        .T\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).ln_1.weights[0].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_1.weights[0].assign(\n",
     "        hf_wts.pop(\n",
     "            f\"vision_model.encoder.layers.{i}.layer_norm1.weight\"\n",
     "        ).numpy()\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).ln_1.weights[1].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_1.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.layer_norm1.bias\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).ln_2.weights[0].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_2.weights[0].assign(\n",
     "        hf_wts.pop(\n",
     "            f\"vision_model.encoder.layers.{i}.layer_norm2.weight\"\n",
     "        ).numpy()\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).ln_2.weights[1].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_2.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.layer_norm2.bias\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_fc\").weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.weight\")\n",
-    "        .transpose(1, 0)\n",
-    "        .numpy()\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_1.weights[0].assign(\n",
+    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.weight\").numpy().T\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_fc\").weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_1.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.bias\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_proj\").weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.weight\")\n",
-    "        .transpose(1, 0)\n",
-    "        .numpy()\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_2.weights[0].assign(\n",
+    "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.weight\").numpy().T\n",
     "    )\n",
-    "    model.get_layer(\"image_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_proj\").weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_2.weights[1].assign(\n",
     "        hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.bias\").numpy()\n",
     "    )"
    ]
@@ -764,17 +986,6 @@
     "## Text encoder"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5FtDROnynb0N"
-   },
-   "outputs": [],
-   "source": [
-    "num_transformer_layers = MODEL_CONFIGS[config_name][\"vision_layers\"]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -784,14 +995,14 @@
    "outputs": [],
    "source": [
     "model.get_layer(\"text_encoder\").get_layer(\"text_projector\").weights[0].assign(\n",
-    "    hf_wts.pop(\"text_projection.weight\").numpy()\n",
+    "    hf_wts.pop(\"text_projection.weight\").numpy().T\n",
     ")\n",
     "model.get_layer(\"text_encoder\").get_layer(\"token_embedding\").weights[0].assign(\n",
     "    hf_wts.pop(\"text_model.embeddings.token_embedding.weight\").numpy()\n",
     ")\n",
-    "model.get_layer(\"text_encoder\").positional_embedding.assign(\n",
-    "    hf_wts.pop(\"text_model.embeddings.position_embedding.weight\").numpy()\n",
-    ")\n",
+    "model.get_layer(\"text_encoder\").get_layer(\"positional_embedding\").weights[\n",
+    "    0\n",
+    "].assign(hf_wts.pop(\"text_model.embeddings.position_embedding.weight\").numpy())\n",
     "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[0].assign(\n",
     "    hf_wts.pop(\"text_model.final_layer_norm.weight\")\n",
     ")\n",
@@ -804,165 +1015,89 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "s6leOiFO6V2U"
+    "id": "IQFquy9R75G8"
    },
    "outputs": [],
    "source": [
     "for i in range(MODEL_CONFIGS[config_name][\"transformer_layers\"]):\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.k_proj.weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.weight\")\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.k_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.k_proj.weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.k_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.q_proj.weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.weight\")\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.q_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.q_proj.weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.q_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.v_proj.weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.weight\")\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.v_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.v_proj.weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.v_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.out_proj.weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.weight\")\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.out_proj.weights[0].assign(\n",
+    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.weight\").T\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).attn.out_proj.weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].attn.out_proj.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.bias\")\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).ln_1.weights[\n",
-    "        0\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_1.weights[0].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm1.weight\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).ln_1.weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_1.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm1.bias\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).ln_2.weights[\n",
-    "        0\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_2.weights[0].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm2.weight\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).ln_2.weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].ln_2.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm2.bias\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).mlp.get_layer(\n",
-    "        \"c_fc\"\n",
-    "    ).weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.weight\")\n",
-    "        .transpose(1, 0)\n",
-    "        .numpy()\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_1.weights[0].assign(\n",
+    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.weight\").numpy().T\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).mlp.get_layer(\n",
-    "        \"c_fc\"\n",
-    "    ).weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_1.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.bias\").numpy()\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).mlp.get_layer(\n",
-    "        \"c_proj\"\n",
-    "    ).weights[\n",
-    "        0\n",
-    "    ].assign(\n",
-    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.weight\")\n",
-    "        .transpose(1, 0)\n",
-    "        .numpy()\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_2.weights[0].assign(\n",
+    "        hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.weight\").numpy().T\n",
     "    )\n",
-    "    model.get_layer(\"text_encoder\").get_layer(\n",
-    "        \"clip_encoder\"\n",
-    "    ).resblocks.get_layer(\n",
-    "        f\"residual_attention_{num_transformer_layers+i}\"\n",
-    "    ).mlp.get_layer(\n",
-    "        \"c_proj\"\n",
-    "    ).weights[\n",
-    "        1\n",
-    "    ].assign(\n",
+    "    model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n",
+    "        i\n",
+    "    ].mlp_dense_2.weights[1].assign(\n",
     "        hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.bias\").numpy()\n",
     "    )"
    ]
@@ -975,7 +1110,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "Bgen7hxCCeZ7",
-    "outputId": "c777d6f1-4aa7-4f3e-8fd7-759364364c44"
+    "outputId": "e706ca82-d292-4868-9215-d8c160b3736c"
    },
    "outputs": [
     {
@@ -984,7 +1119,7 @@
        "odict_keys([])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1011,12 +1146,15 @@
    },
    "outputs": [],
    "source": [
-    "model.save_weights(\"clip-vit-base-patch32.weights.h5\")"
+    "model.save_weights(\"model.weights.h5\")"
    ]
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
   "colab": {
+   "gpuType": "V100",
+   "machine_shape": "hm",
    "provenance": []
   },
   "kernelspec": {
@@ -1025,6 +1163,2746 @@
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    "name": "python"
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