diff --git a/examples/huggingface/share_models.ipynb b/examples/huggingface/share_models.ipynb
index ae93f742..5638a5ed 100644
--- a/examples/huggingface/share_models.ipynb
+++ b/examples/huggingface/share_models.ipynb
@@ -1,24 +1,284 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "view-in-github"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/not-lain/torchquantum/blob/huggingface/examples/huggingface/share_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4syF6X3Fl5dg",
+        "outputId": "d4c21587-73a1-4e3f-d4c5-9a22e2fbd3f0"
+      },
+      "outputs": [],
+      "source": [
+        "%pip install -q git+https://github.com/mit-han-lab/torchquantum.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8J3xd8iWSEX0"
+      },
+      "source": [
+        "# login with your writing token\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331,
+          "referenced_widgets": [
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+            "57537ab729da49e3a53fe8e42fbe772c"
+          ]
+        },
+        "id": "ydSz4YpbUVU_",
+        "outputId": "958b42a4-3084-462e-e814-75324adfed09"
+      },
+      "outputs": [],
+      "source": [
+        "# token can be found here https://huggingface.co/settings/tokens\n",
+        "from huggingface_hub import notebook_login\n",
+        "\n",
+        "notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hH800Lx4XFuz"
+      },
+      "source": [
+        "# Create an AI model\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "6s9D_oxmRaYY"
+      },
+      "outputs": [],
+      "source": [
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torchquantum as tq\n",
+        "import torchquantum.functional as tqf\n",
+        "from huggingface_hub import PyTorchModelHubMixin\n",
+        "\n",
+        "\n",
+        "class QFCModel(nn.Module, PyTorchModelHubMixin, tags=[\"torchquantum\"]):\n",
+        "    def __init__(self, n_wires):\n",
+        "        super().__init__()\n",
+        "\n",
+        "        self.n_wires = n_wires  # an init parameter\n",
+        "\n",
+        "        self.measure = tq.MeasureAll(tq.PauliZ)\n",
+        "\n",
+        "        self.encoder_gates = [tqf.rx] * 4 + [tqf.ry] * 4 + [tqf.rz] * 4 + [tqf.rx] * 4\n",
+        "        self.rx0 = tq.RX(has_params=True, trainable=True)\n",
+        "        self.ry0 = tq.RY(has_params=True, trainable=True)\n",
+        "        self.rz0 = tq.RZ(has_params=True, trainable=True)\n",
+        "        self.crx0 = tq.CRX(has_params=True, trainable=True)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        bsz = x.shape[0]\n",
+        "        # down-sample the image\n",
+        "        x = F.avg_pool2d(x, 6).view(bsz, 16)\n",
+        "\n",
+        "        # create a quantum device to run the gates\n",
+        "        qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=bsz, device=x.device)\n",
+        "\n",
+        "        # encode the classical image to quantum domain\n",
+        "        for k, gate in enumerate(self.encoder_gates):\n",
+        "            gate(qdev, wires=k % self.n_wires, params=x[:, k])\n",
+        "\n",
+        "        # add some trainable gates (need to instantiate ahead of time)\n",
+        "        self.rx0(qdev, wires=0)\n",
+        "        self.ry0(qdev, wires=1)\n",
+        "        self.rz0(qdev, wires=3)\n",
+        "        self.crx0(qdev, wires=[0, 2])\n",
+        "\n",
+        "        # add some more non-parameterized gates (add on-the-fly)\n",
+        "        qdev.h(wires=3)\n",
+        "        qdev.sx(wires=2)\n",
+        "        qdev.cnot(wires=[3, 0])\n",
+        "        qdev.qubitunitary(\n",
+        "            wires=[1, 2],\n",
+        "            params=[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1j], [0, 0, -1j, 0]],\n",
+        "        )\n",
+        "\n",
+        "        # perform measurement to get expectations (back to classical domain)\n",
+        "        x = self.measure(qdev).reshape(bsz, 2, 2)\n",
+        "\n",
+        "        # classification\n",
+        "        x = x.sum(-1).squeeze()\n",
+        "        x = F.log_softmax(x, dim=1)\n",
+        "\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "model = QFCModel(n_wires=4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "083_CavNXjnf"
+      },
+      "source": [
+        "# push your weights to huggingface\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eAVkBtd1XLqj"
+      },
+      "source": [
+        "now you can push your weights to Hugginface or you can use `model.save_pretrained(\"path\")`\n",
+        "to save your model locally\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84,
+          "referenced_widgets": [
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+            "33985956758947f2b6e19049350194de",
+            "dbf965d4653f4d4a880102c90c63ac61"
+          ]
+        },
+        "id": "bJGfwC5ERpLl",
+        "outputId": "1389ac4f-e55f-4f5f-fb84-6e5d52ca0049"
+      },
+      "outputs": [],
+      "source": [
+        "model.push_to_hub(\"qfc-model\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kpNNU_O4XaIG"
+      },
+      "source": [
+        "my model can be found here https://huggingface.co/not-lain/qfc-model\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VLMzVmXEXeR1"
+      },
+      "source": [
+        "# load weights from huggingface\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
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+            "d7250c64d86a4316b9b1fb6ab5d95030",
+            "a2867777e3d248d0ab1b3c49fa18463e",
+            "bdbf6d0e2b0d4ddd802a2787c2927285",
+            "327b087493c44bc1bcac526a486fc7f1",
+            "0b99b13bc14e4565b546d9485de0ecf7",
+            "13922082f4d04cbbb613c3b45689c121",
+            "95d55bb766ef413598a48b9a3478948a"
+          ]
+        },
+        "id": "r8K1DsjPRqEG",
+        "outputId": "21027571-9d67-4bd6-b610-3c667cd8fd9e"
+      },
+      "outputs": [],
+      "source": [
+        "# use the raw class to load the model\n",
+        "# no need to reinitialize the model or manually load the weights\n",
+        "# from pretrained can also be used to load a local model\n",
+        "new_model = QFCModel.from_pretrained(\"not-lain/qfc-model\", force_download=True)"
+      ]
+    }
+  ],
   "metadata": {
     "colab": {
-      "provenance": [],
-      "include_colab_link": true
+      "include_colab_link": true,
+      "provenance": []
     },
     "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+      "display_name": "Python 3",
+      "name": "python3"
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     "language_info": {
-      "name": "python"
+      "name": "python",
+      "version": "3.11.3"
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-            "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
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-            "_view_module": "@jupyter-widgets/controls",
-            "_view_module_version": "1.5.0",
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-            "description": "Add token as git credential?",
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-            "style": "IPY_MODEL_57537ab729da49e3a53fe8e42fbe772c",
-            "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
+            "style": "IPY_MODEL_bdbf6d0e2b0d4ddd802a2787c2927285",
+            "value": "model.safetensors: 100%"
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-            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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-            "\u001b[?25h  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[32m56.8/56.8 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.3/296.3 kB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  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[32m134.8/134.8 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.1/133.1 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.7/128.7 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.4/130.4 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.4/121.4 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.5/107.5 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Building wheel for torchquantum (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for ibm-platform-services (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for ibm-cloud-sdk-core (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# login with your writing token"
-      ],
-      "metadata": {
-        "id": "8J3xd8iWSEX0"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# token can be found here https://huggingface.co/settings/tokens\n",
-        "from huggingface_hub import notebook_login\n",
-        "notebook_login()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 331,
-          "referenced_widgets": [
-            "03ed14345fcd430fa4d692440b2b92de",
-            "5d56e244ff4a4a8c870e78dfe6ce0f4e",
-            "74550344bf054abd969ab40f5a90850e",
-            "c22dddecf8bd4b258e39dcd17559a07c",
-            "4f1ced08708b4070af3a09ab1bfd5c4a",
-            "425bd24e216b4b1392670b138d33e795",
-            "7ac966c2fff74b999086816a16ac9825",
-            "35a3481cf34d4b72ad1266a9664e2db4",
-            "3862dbd096054b7dbd3b9a3d26941a00",
-            "48e27c9c79854a2bbad92ca05cbf6842",
-            "340be088856c42f082c95c6a3db3f4b1",
-            "d5fd0caaeb154cf28e8ea9d72587fe0d",
-            "f9e45fc510a1459c8cb07b1ecc3b8514",
-            "586cbbcb2b3448b4a8bcf904965b25db",
-            "b0f383d785134544b2fd7e2bf3e54b33",
-            "72e8d4713c574538aeebcee32af4960d",
-            "57537ab729da49e3a53fe8e42fbe772c"
-          ]
-        },
-        "id": "ydSz4YpbUVU_",
-        "outputId": "958b42a4-3084-462e-e814-75324adfed09"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "03ed14345fcd430fa4d692440b2b92de"
-            }
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Create an AI model"
-      ],
-      "metadata": {
-        "id": "hH800Lx4XFuz"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch.nn as nn\n",
-        "import torch.nn.functional as F\n",
-        "import torchquantum as tq\n",
-        "import torchquantum.functional as tqf\n",
-        "from huggingface_hub import PyTorchModelHubMixin\n",
-        "\n",
-        "class QFCModel(nn.Module,\n",
-        "               PyTorchModelHubMixin,\n",
-        "               tags=[\"torchquantum\"]):\n",
-        "\n",
-        "  def __init__(self, n_wires ):\n",
-        "    super().__init__()\n",
-        "\n",
-        "    self.n_wires = n_wires # an init parameter\n",
-        "\n",
-        "    self.measure = tq.MeasureAll(tq.PauliZ)\n",
-        "\n",
-        "    self.encoder_gates = [tqf.rx] * 4 + [tqf.ry] * 4 + \\\n",
-        "                         [tqf.rz] * 4 + [tqf.rx] * 4\n",
-        "    self.rx0 = tq.RX(has_params=True, trainable=True)\n",
-        "    self.ry0 = tq.RY(has_params=True, trainable=True)\n",
-        "    self.rz0 = tq.RZ(has_params=True, trainable=True)\n",
-        "    self.crx0 = tq.CRX(has_params=True, trainable=True)\n",
-        "\n",
-        "  def forward(self, x):\n",
-        "    bsz = x.shape[0]\n",
-        "    # down-sample the image\n",
-        "    x = F.avg_pool2d(x, 6).view(bsz, 16)\n",
-        "\n",
-        "    # create a quantum device to run the gates\n",
-        "    qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=bsz, device=x.device)\n",
-        "\n",
-        "    # encode the classical image to quantum domain\n",
-        "    for k, gate in enumerate(self.encoder_gates):\n",
-        "      gate(qdev, wires=k % self.n_wires, params=x[:, k])\n",
-        "\n",
-        "    # add some trainable gates (need to instantiate ahead of time)\n",
-        "    self.rx0(qdev, wires=0)\n",
-        "    self.ry0(qdev, wires=1)\n",
-        "    self.rz0(qdev, wires=3)\n",
-        "    self.crx0(qdev, wires=[0, 2])\n",
-        "\n",
-        "    # add some more non-parameterized gates (add on-the-fly)\n",
-        "    qdev.h(wires=3)\n",
-        "    qdev.sx(wires=2)\n",
-        "    qdev.cnot(wires=[3, 0])\n",
-        "    qdev.qubitunitary(wires=[1, 2], params=[[1, 0, 0, 0],\n",
-        "                                            [0, 1, 0, 0],\n",
-        "                                            [0, 0, 0, 1j],\n",
-        "                                            [0, 0, -1j, 0]])\n",
-        "\n",
-        "    # perform measurement to get expectations (back to classical domain)\n",
-        "    x = self.measure(qdev).reshape(bsz, 2, 2)\n",
-        "\n",
-        "    # classification\n",
-        "    x = x.sum(-1).squeeze()\n",
-        "    x = F.log_softmax(x, dim=1)\n",
-        "\n",
-        "    return x\n",
-        "\n",
-        "model = QFCModel(n_wires = 4)"
-      ],
-      "metadata": {
-        "id": "6s9D_oxmRaYY"
-      },
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# push your weights to huggingface"
-      ],
-      "metadata": {
-        "id": "083_CavNXjnf"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "now you can push your weights to Hugginface or you can use `model.save_pretrained(\"path\")`\n",
-        "to save your model locally"
-      ],
-      "metadata": {
-        "id": "eAVkBtd1XLqj"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.push_to_hub(\"qfc-model\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 84,
-          "referenced_widgets": [
-            "33d6236ffcb449768358e100ecadff57",
-            "d45adb4c73334cef9e999f1e528d29e8",
-            "ef982f04dcfd459bb3fc18cb5e5e810b",
-            "ef69f4eeba00445fac55f89be3923a82",
-            "97154dd426ec4a11a7439d77121a96f1",
-            "9e9ffcded9fc42c89bd931c9f9cd3f5c",
-            "50247b1d2efd4d968590f02fa23bffdb",
-            "c7d1058e221b433785ae13759d6d8a4c",
-            "29fd42a52e694c7bb217b226175ca3c2",
-            "33985956758947f2b6e19049350194de",
-            "dbf965d4653f4d4a880102c90c63ac61"
-          ]
-        },
-        "id": "bJGfwC5ERpLl",
-        "outputId": "1389ac4f-e55f-4f5f-fb84-6e5d52ca0049"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/288 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "33d6236ffcb449768358e100ecadff57"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "CommitInfo(commit_url='https://huggingface.co/not-lain/qfc-model/commit/c9ff191c3506ac488ec42b75584119dfaab5a250', commit_message='Push model using huggingface_hub.', commit_description='', oid='c9ff191c3506ac488ec42b75584119dfaab5a250', pr_url=None, pr_revision=None, pr_num=None)"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            }
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "my model can be found here https://huggingface.co/not-lain/qfc-model"
-      ],
-      "metadata": {
-        "id": "kpNNU_O4XaIG"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# load weights from huggingface"
-      ],
-      "metadata": {
-        "id": "VLMzVmXEXeR1"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# use the raw class to load the model\n",
-        "# no need to reinitialize the model or manually load the weights\n",
-        "# from pretrained can also be used to load a local model\n",
-        "new_model = QFCModel.from_pretrained(\"not-lain/qfc-model\",force_download=True)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 81,
-          "referenced_widgets": [
-            "4c2da5cf80a94267b9faea748f82c0e0",
-            "0ee88ce783994f38b0db6a738d2a8a48",
-            "60fadc09913d42b796ef418f24618576",
-            "acdfabd115ea4ef482af5820abc15b20",
-            "1322fd447b6a4db8a7966e992c2cc948",
-            "aba3fe0da5594ddd9226b35bd6f19e54",
-            "23e70ca894394cf891247e897c75ae7c",
-            "bda0e8e5053f4615b4bab3833ee29d3e",
-            "85c3bb489d894c57b9ad1c16e34b56c7",
-            "b5e89469873049adbe2f9bf3cf5d2709",
-            "a83b0347eef547abb5dac64d7456ee22",
-            "9cf1d08201df41a8b06c3e5956a96611",
-            "11e99e2f558049dd8ff9353b8e7ed0c9",
-            "f72b9265ba8e4a45bf1049dfd8c711b9",
-            "c644a223e2cb4696963d9421a39fd3d6",
-            "d7250c64d86a4316b9b1fb6ab5d95030",
-            "a2867777e3d248d0ab1b3c49fa18463e",
-            "bdbf6d0e2b0d4ddd802a2787c2927285",
-            "327b087493c44bc1bcac526a486fc7f1",
-            "0b99b13bc14e4565b546d9485de0ecf7",
-            "13922082f4d04cbbb613c3b45689c121",
-            "95d55bb766ef413598a48b9a3478948a"
-          ]
-        },
-        "id": "r8K1DsjPRqEG",
-        "outputId": "21027571-9d67-4bd6-b610-3c667cd8fd9e"
-      },
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "config.json:   0%|          | 0.00/18.0 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4c2da5cf80a94267b9faea748f82c0e0"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/288 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "9cf1d08201df41a8b06c3e5956a96611"
-            }
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
+  "nbformat": 4,
+  "nbformat_minor": 0
+}