Upload Math_Add_Sub_Mul_Div.ipynb

Math_Add_Sub_Mul_Div.ipynb

@@ -0,0 +1,376 @@
+{
+  "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": 55,
+      "metadata": {
+        "id": "SHsGymlK6Q5d"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install torch safetensors huggingface_hub"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "\n",
+        "from safetensors.torch import save_file"
+      ],
+      "metadata": {
+        "id": "-hrp9a078H5x"
+      },
+      "execution_count": 56,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random\n",
+        "\n",
+        "X = []\n",
+        "Y = []\n",
+        "\n",
+        "for _ in range(5000):\n",
+        "\n",
+        "    a = random.uniform(1, 10)\n",
+        "    b = random.uniform(1, 10)\n",
+        "\n",
+        "    inputs = [a, b]\n",
+        "\n",
+        "    outputs = [\n",
+        "        a + b,\n",
+        "        a - b,\n",
+        "        a * b,\n",
+        "        a / b\n",
+        "    ]\n",
+        "\n",
+        "    X.append(inputs)\n",
+        "    Y.append(outputs)\n",
+        "\n",
+        "X = torch.tensor(X, dtype=torch.float32)\n",
+        "Y = torch.tensor(Y, dtype=torch.float32)\n",
+        "\n",
+        "print(X.shape)\n",
+        "print(Y.shape)"
+      ],
+      "metadata": {
+        "id": "WYAJTDJI8Kqm"
+      },
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class MathModel(nn.Module):\n",
+        "\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "\n",
+        "        self.net = nn.Sequential(\n",
+        "\n",
+        "            nn.Linear(2, 32),\n",
+        "            nn.ReLU(),\n",
+        "\n",
+        "            nn.Linear(32, 64),\n",
+        "            nn.ReLU(),\n",
+        "\n",
+        "            nn.Linear(64, 32),\n",
+        "            nn.ReLU(),\n",
+        "\n",
+        "            nn.Linear(32, 4)\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.net(x)"
+      ],
+      "metadata": {
+        "id": "a9T_97D_8NAc"
+      },
+      "execution_count": 58,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = MathModel()\n",
+        "\n",
+        "print(model)"
+      ],
+      "metadata": {
+        "id": "1jxqfO6v8PGD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "criterion = nn.MSELoss()\n",
+        "\n",
+        "optimizer = optim.Adam(\n",
+        "    model.parameters(),\n",
+        "    lr=0.001\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "BhcNFeMZ8Qti"
+      },
+      "execution_count": 60,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "epochs = 500\n",
+        "\n",
+        "for epoch in range(epochs):\n",
+        "\n",
+        "    predictions = model(X)\n",
+        "\n",
+        "    loss = criterion(predictions, Y)\n",
+        "\n",
+        "    optimizer.zero_grad()\n",
+        "\n",
+        "    loss.backward()\n",
+        "\n",
+        "    optimizer.step()\n",
+        "\n",
+        "    if epoch % 50 == 0:\n",
+        "        print(f\"Epoch {epoch} Loss: {loss.item():.4f}\")"
+      ],
+      "metadata": {
+        "id": "ZdNlDDp48lVE"
+      },
+      "execution_count": 61,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_input = torch.tensor([[6.0, 2.0]])\n",
+        "\n",
+        "prediction = model(test_input)\n",
+        "\n",
+        "print(prediction)"
+      ],
+      "metadata": {
+        "id": "bwfPd2e_8nkb"
+      },
+      "execution_count": 62,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a = 6\n",
+        "b = 2\n",
+        "\n",
+        "test_input = torch.tensor([[a, b]], dtype=torch.float32)\n",
+        "\n",
+        "prediction = model(test_input).detach().numpy()[0]\n",
+        "\n",
+        "print(\"INPUT\")\n",
+        "print(a, b)\n",
+        "\n",
+        "print(\"\\nPREDICTIONS\")\n",
+        "\n",
+        "print(\"ADD :\", prediction[0])\n",
+        "print(\"SUB :\", prediction[1])\n",
+        "print(\"MUL :\", prediction[2])\n",
+        "print(\"DIV :\", prediction[3])"
+      ],
+      "metadata": {
+        "id": "2yRAy_ek8peN"
+      },
+      "execution_count": 63,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "save_file(\n",
+        "    model.state_dict(),\n",
+        "    \"math_add_sub_mul_div_model.safetensors\"\n",
+        ")\n",
+        "\n",
+        "print(\"Saved!\")"
+      ],
+      "metadata": {
+        "id": "FcPuskx58rkF"
+      },
+      "execution_count": 64,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "print(os.listdir())"
+      ],
+      "metadata": {
+        "id": "8vhzfzAe8tKW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import login\n",
+        "\n",
+        "login()"
+      ],
+      "metadata": {
+        "id": "U5qADMmz8u65"
+      },
+      "execution_count": 66,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import HfApi\n",
+        "\n",
+        "api = HfApi()\n",
+        "\n",
+        "api.upload_file(\n",
+        "    path_or_fileobj=\"math_add_sub_mul_div_model.safetensors\",\n",
+        "    path_in_repo=\"math_add_sub_mul_div_model.safetensors\",\n",
+        "    repo_id=\"harishforaiandml/math-add-sub-mul-div-model\",\n",
+        "    repo_type=\"model\",\n",
+        ")\n",
+        "\n",
+        "print(\"Uploaded!\")"
+      ],
+      "metadata": {
+        "id": "5o1XmEOX8ycJ"
+      },
+      "execution_count": 67,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "readme = \"\"\"\n",
+        "# Basic Math AI\n",
+        "\n",
+        "Tiny neural network trained to perform:\n",
+        "\n",
+        "- Addition\n",
+        "- Subtraction\n",
+        "- Multiplication\n",
+        "- Division\n",
+        "\n",
+        "Inputs:\n",
+        "[a, b]\n",
+        "\n",
+        "Outputs:\n",
+        "[a+b, a-b, a*b, a/b]\n",
+        "\n",
+        "Saved using SafeTensors.\n",
+        "\"\"\"\n",
+        "\n",
+        "with open(\"README.md\", \"w\") as f:\n",
+        "    f.write(readme)"
+      ],
+      "metadata": {
+        "id": "SjlZCtl69H8R"
+      },
+      "execution_count": 68,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "api.upload_file(\n",
+        "    path_or_fileobj=\"README.md\",\n",
+        "    path_in_repo=\"README.md\",\n",
+        "    repo_id=\"harishforaiandml/math-add-sub-mul-div-model\",\n",
+        "    repo_type=\"model\",\n",
+        ")\n",
+        "\n",
+        "print(\"README uploaded!\")"
+      ],
+      "metadata": {
+        "id": "N3ALvS4n9hKe"
+      },
+      "execution_count": 69,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import hf_hub_download\n",
+        "from safetensors.torch import load_file"
+      ],
+      "metadata": {
+        "id": "r5gb1XAO-7bh"
+      },
+      "execution_count": 70,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_math_model():\n",
+        "\n",
+        "    model_path = hf_hub_download(\n",
+        "        repo_id=\"harishforaiandml/math-add-sub-mul-div-model\",\n",
+        "        filename=\"math_add_sub_mul_div_model.safetensors\"\n",
+        "    )\n",
+        "\n",
+        "    state_dict = load_file(model_path)\n",
+        "\n",
+        "    model = MathModel()\n",
+        "\n",
+        "    model.load_state_dict(state_dict)\n",
+        "\n",
+        "    model.eval()\n",
+        "\n",
+        "    return model"
+      ],
+      "metadata": {
+        "id": "JEnr8lUS-PVr"
+      },
+      "execution_count": 71,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = load_math_model()\n",
+        "\n",
+        "x = torch.tensor([[10.0, 5.0]])\n",
+        "\n",
+        "result = model(x)\n",
+        "\n",
+        "print(result)"
+      ],
+      "metadata": {
+        "id": "lbVdBthn-RIv"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}