Delete Math_Add_Sub_Mul_Div.ipynb

Math_Add_Sub_Mul_Div.ipynb

@@ -1,796 +0,0 @@
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-      "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": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1jxqfO6v8PGD",
-        "outputId": "26c43cae-f6ee-4125-833a-ec2006807836"
-      },
-      "execution_count": 59,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "MathModel(\n",
-            "  (net): Sequential(\n",
-            "    (0): Linear(in_features=2, out_features=32, bias=True)\n",
-            "    (1): ReLU()\n",
-            "    (2): Linear(in_features=32, out_features=64, bias=True)\n",
-            "    (3): ReLU()\n",
-            "    (4): Linear(in_features=64, out_features=32, bias=True)\n",
-            "    (5): ReLU()\n",
-            "    (6): Linear(in_features=32, out_features=4, bias=True)\n",
-            "  )\n",
-            ")\n"
-          ]
-        }
-      ]
-    },
-    {
-      "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": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8vhzfzAe8tKW",
-        "outputId": "3855f92f-5f56-47e5-e7e9-8ad5e93b16b8"
-      },
-      "execution_count": 65,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "['.config', 'README.md', 'math_add_sub_mul_div_model.safetensors', 'sample_data']\n"
-          ]
-        }
-      ]
-    },
-    {
-      "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": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 67,
-          "referenced_widgets": [
-            "db1b009654884679b423b3f2957e0026",
-            "30ff234646864b6bacca542c2f5dc147",
-            "4d53e007798745eb9c3eef9894930e0e",
-            "a2260d3710504d2cb84c3f7a31375d30",
-            "101177b8e08c4515af26396ce934b3cd",
-            "35f5d90507ca4b62a9e6eff1d7671a8f",
-            "6531fba09cd04fafb6a60d6bf8da89ca",
-            "350bd326bc01450c95311521650b1250",
-            "1daf9c25d50c430a90d50807ae51f02c",
-            "f92cf32c524d46ad9af9aaf3c7f3333f",
-            "a4a84ca7f8194670bc377cd2547c2656"
-          ]
-        },
-        "id": "lbVdBthn-RIv",
-        "outputId": "e7e00a00-7e40-40c2-ca44-8913adb70128"
-      },
-      "execution_count": 72,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "math_add_sub_mul_div_model.safetensors:   0%|          | 0.00/18.3k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "db1b009654884679b423b3f2957e0026"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "tensor([[14.7397,  5.0453, 48.6241,  2.7476]], grad_fn=<AddmmBackward0>)\n"
-          ]
-        }
-      ]
-    }
-  ]
-}