Upload Run_EPlus_LLM.ipynb

Run_EPlus_LLM.ipynb

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+{
+  "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": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cwihsgMJw-V2",
+        "outputId": "a893f7c8-2f8d-496d-9965-25b611511ef5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Building Energy Model Auto-Generated: v1_final.idf\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from transformers import (\n",
+        "    AutoModelForSeq2SeqLM,\n",
+        "    AutoTokenizer,\n",
+        ")\n",
+        "\n",
+        "# Input the rest port of IDF file.\n",
+        "file_path = \"v1_nextpart.idf\"\n",
+        "output_path = \"v1_final.idf\"\n",
+        "\n",
+        "# Input the EPlus-LLM model\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-large\")\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(\"EPlus-LLM/EPlus-LLMv1\")\n",
+        "\n",
+        "# Generation config\n",
+        "generation_config = model.generation_config\n",
+        "generation_config.max_new_tokens = 2000\n",
+        "generation_config.temperature = 0.1\n",
+        "generation_config.top_p = 0.1\n",
+        "generation_config.num_return_sequences = 1\n",
+        "generation_config.pad_token_id = tokenizer.eos_token_id\n",
+        "generation_config.eos_token_id = tokenizer.eos_token_id\n",
+        "\n",
+        "# Please provide your input here — a description of the desired building\n",
+        "# For more details, please refer to the paper\n",
+        "input=\"Simulate a building that is 30.00 meters long, 15.00 meters wide, and 3.50 meters high. The window-to-wall ratio is 0.28. The occupancy rate is 8.00 m2/people, the lighting level is 6.00 W/m2, and the equipment power consumption is 8.80 W/m2.\"\n",
+        "input_ids = tokenizer(input, return_tensors=\"pt\", truncation=False)\n",
+        "generated_ids = model.generate(input_ids = input_ids.input_ids,\n",
+        "                           attention_mask = input_ids.attention_mask,\n",
+        "                           generation_config = generation_config)\n",
+        "generated_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
+        "generated_output = generated_output.replace(\"_\", \" \")\n",
+        "generated_output = generated_output.replace(\"|\", \"\\n\")\n",
+        "\n",
+        "with open(file_path, 'r', encoding='utf-8') as file:\n",
+        "    nextpart = file.read()\n",
+        "final_text = nextpart + \"\\n\\n\" + generated_output\n",
+        "with open(output_path, 'w', encoding='utf-8') as f:\n",
+        "    f.write(final_text)\n",
+        "\n",
+        "# Output the building energy model in IDF file\n",
+        "print(f\"Building Energy Model Auto-Generated: {output_path}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "dDakbuWH5TLt"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}