feat: 添加MPS支持示例并重置执行计数

添加新的mps.ipynb文件展示如何在Apple Silicon GPU上使用PyTorch的MPS加速
重置conversation.ipynb中的执行计数为null

conversation.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "e0022b61",
    "metadata": {},
    "outputs": [

mps.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3e83459e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MPS available: True\n",
+      "Tensor created on device: mps:0\n",
+      "tensor([[1., 1., 1.],\n",
+      "        [1., 1., 1.]], device='mps:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# 检查 MPS 是否可用\n",
+    "print(f\"MPS available: {torch.backends.mps.is_available()}\")\n",
+    "\n",
+    "# 如果可用，可以进行一个简单的测试\n",
+    "if torch.backends.mps.is_available():\n",
+    "    device = torch.device(\"mps\")\n",
+    "    x = torch.ones(2, 3, device=device)\n",
+    "    print(f\"Tensor created on device: {x.device}\")\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1b11398e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ 检测到 Apple Silicon GPU (MPS)，加速已就绪！\n",
+      "\n",
+      "👇 创建的张量内容如下：\n",
+      "tensor([[-1.1299,  0.7376,  0.9677],\n",
+      "        [ 1.1327,  0.1566, -1.4688]], device='mps:0')\n",
+      "\n",
+      "📍 当前张量所在的设备是: mps:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "# 1. 自动检测设备：如果支持 MPS 则使用 mps，否则使用 cpu\n",
+    "if torch.backends.mps.is_available():\n",
+    "    device = torch.device(\"mps\")\n",
+    "    print(\"✅ 检测到 Apple Silicon GPU (MPS)，加速已就绪！\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "    print(\"⚠️ MPS 不可用，当前使用 CPU 运行。\")\n",
+    "\n",
+    "# 2. 在指定设备上创建一个随机张量 (形状为 2x3)\n",
+    "# 这里的 torch.randn 会生成符合标准正态分布的随机数\n",
+    "x = torch.randn(2, 3, device=device)\n",
+    "\n",
+    "# 3. 打印张量\n",
+    "print(\"\\n👇 创建的张量内容如下：\")\n",
+    "print(x)\n",
+    "\n",
+    "# 4. 额外验证：打印设备信息，确保它真的在 mps 上\n",
+    "print(f\"\\n📍 当前张量所在的设备是: {x.device}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3a7269bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 0.2961,  0.5715,  0.3610],\n",
+      "        [ 0.6116,  2.6332, -0.5906]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = torch.randn(2, 3)\n",
+    "print(x)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hgface",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}