Add demo notebook

demo_notebook.ipynb

@@ -0,0 +1,101 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MultiModalHackVAE Demo\n\n",
+        "This notebook demonstrates how to use the MultiModalHackVAE model from CatkinChen/nethack-vae.\n\n",
+        "## Installation\n\n",
+        "```bash\n",
+        "pip install torch transformers huggingface_hub\n",
+        "# For NetHack environment (optional):\n",
+        "pip install nle\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "source": [
+        "import torch\n",
+        "import numpy as np\n",
+        "from huggingface_hub import hf_hub_download\n",
+        "import json\n",
+        "\n",
+        "# Load model config\n",
+        "config_path = hf_hub_download(repo_id='CatkinChen/nethack-vae', filename='config.json')\n",
+        "with open(config_path, 'r') as f:\n",
+        "    config = json.load(f)\n",
+        "\n",
+        "print('Model Configuration:')\n",
+        "for key, value in config.items():\n",
+        "    print(f'  {key}: {value}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "source": [
+        "# Load the model (you'll need to import your model class)\n",
+        "# from your_package import MultiModalHackVAE\n",
+        "# model = load_model_from_huggingface('{repo_name}')\n",
+        "\n",
+        "# Example synthetic data\n",
+        "batch_size = 1\n",
+        "game_chars = torch.randint(32, 127, (batch_size, 21, 79))\n",
+        "game_colors = torch.randint(0, 16, (batch_size, 21, 79))\n",
+        "blstats = torch.randn(batch_size, 27)\n",
+        "msg_tokens = torch.randint(0, 128, (batch_size, 256))\n",
+        "hero_info = torch.randint(0, 10, (batch_size, 4))\n",
+        "\n",
+        "print('Synthetic data shapes:')\n",
+        "print(f'  game_chars: {game_chars.shape}')\n",
+        "print(f'  game_colors: {game_colors.shape}')\n",
+        "print(f'  blstats: {blstats.shape}')\n",
+        "print(f'  msg_tokens: {msg_tokens.shape}')\n",
+        "print(f'  hero_info: {hero_info.shape}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "source": [
+        "# Encode to latent space\n",
+        "# with torch.no_grad():\n",
+        "#     output = model(\n",
+        "#         glyph_chars=game_chars,\n",
+        "#         glyph_colors=game_colors,\n",
+        "#         blstats=blstats,\n",
+        "#         msg_tokens=msg_tokens,\n",
+        "#         hero_info=hero_info\n",
+        "#     )\n",
+        "#     \n",
+        "#     latent_mean = output['latent_mean']\n",
+        "#     latent_logvar = output['latent_logvar']\n",
+        "#     reconstructions = output['reconstructions']\n",
+        "#     \n",
+        "#     print(f'Latent representation shape: {latent_mean.shape}')\n",
+        "#     print(f'Latent mean: {latent_mean[0][:5].tolist()}')\n",
+        "\n",
+        "print('Model inference example (uncomment when model is available)')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.8+"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}