{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hlBugIxD1mM4"
      },
      "outputs": [],
      "source": [
        "!pip install torch safetensors huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "from safetensors.torch import save_file"
      ],
      "metadata": {
        "id": "Va1tYSgo1wdP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TinyModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "        self.layer1 = nn.Linear(4, 8)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.layer2 = nn.Linear(8, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.layer1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.layer2(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "WIW3Ay8k1zb_"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = TinyModel()\n",
        "\n",
        "print(model)"
      ],
      "metadata": {
        "id": "9YeQA-G811UQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_file(model.state_dict(), \"tiny_empty_model.safetensors\")"
      ],
      "metadata": {
        "id": "n9BZ2UGW14Sb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "print(os.listdir())"
      ],
      "metadata": {
        "id": "6fkroAm0164Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login()"
      ],
      "metadata": {
        "id": "pjpGmemW2Tvb"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"tiny_empty_model.safetensors\",\n",
        "    path_in_repo=\"tiny_empty_model.safetensors\",\n",
        "    repo_id=\"harishforaiandml/tiny-empty-safetensor-model\",\n",
        "    repo_type=\"model\",\n",
        ")"
      ],
      "metadata": {
        "id": "nrh5F23-2WGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "readme = \"\"\"\n",
        "# Tiny SafeTensor Model\n",
        "\n",
        "This is my first empty SafeTensor model uploaded from Google Colab.\n",
        "\n",
        "- Framework: PyTorch\n",
        "- Format: SafeTensor\n",
        "\"\"\"\n",
        "\n",
        "with open(\"README.md\", \"w\") as f:\n",
        "    f.write(readme)"
      ],
      "metadata": {
        "id": "2q_KBzNx2hTY"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "api.upload_file(\n",
        "    path_or_fileobj=\"README.md\",\n",
        "    path_in_repo=\"README.md\",\n",
        "    repo_id=\"harishforaiandml/tiny-empty-safetensor-model\",\n",
        "    repo_type=\"model\",\n",
        ")"
      ],
      "metadata": {
        "id": "p-v0zbFP2m99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from safetensors.torch import load_file\n",
        "\n",
        "state_dict = load_file(\"tiny_empty_model.safetensors\")\n",
        "\n",
        "new_model = TinyModel()\n",
        "new_model.load_state_dict(state_dict)\n",
        "\n",
        "print(\"Model loaded successfully!\")"
      ],
      "metadata": {
        "id": "KqJRvQea2rNG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}