Update README.md

README.md

@@ -53,32 +53,112 @@ This current platform is designed for engineers, architects, and researchers wor
 
 ## 🚀 Quick Start
 
-This repository contains v2 and v1 of EPlus-LLM, along with implementation details for the ABEM reference.
-
-
-📂 Repository Structure
-
-```
-    ── README.md                           # Project documentation
-    ── v2                                  # V2 model for complex ABEM scenarios in real-world
-    ── v1                                  # V1 model for simple ABEM scenarios
-    ── requirements.txt                    # Dependencies for this project
+Here provides a code snippet to show you how to load the EPlus-LLM and auto-generate building energy models.  
+  
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/EPlus-LLM/EPlus-LLMv1/blob/main/Run_EPlus-LLM.ipynb)
+
+```python
+# ⚠️ Please make sure you have adequate GPU memory.
+# ! pip install -U bitsandbytes -q
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+from peft import PeftModel, PeftConfig
+
+# Load the rest port of IDF file.
+file_path = "v2_nextpart.idf"
+output_path = "v2_final.idf"
+
+# Load the EPlus-LLMv2 config. 
+peft_model_id = "EPlus-LLM/EPlus-LLMv2"
+config = PeftConfig.from_pretrained(peft_model_id)
+
+# Load the base LLM, flan-t5-xxl, and tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-xxl", load_in_8bit=True)
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xxl")
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+# Generation config
+generation_config = model.generation_config
+generation_config.max_new_tokens = 5000
+generation_config.temperature = 0.1
+generation_config.top_p = 0.1
+generation_config.num_return_sequences = 1
+generation_config.pad_token_id = tokenizer.eos_token_id
+generation_config.eos_token_id = tokenizer.eos_token_id
+
+# Please provide your input here — a description of the desired building
+# For more details, please refer to the paper: https://doi.org/10.1016/j.autcon.2025.106223
+input=f"""
+Simulate a U-shaped building that is 99.73 meters high, with a gable roof.
+The horizontal segment is 732.31 meters long and 17.54 meters wide.
+The left vertical segment is 256.31 meters long and 206.96 meters wide.
+The right vertical segment is 431.54 meters long and 62 meters wide.
+The roof ridge is 8.77 meters to the length side of the horizontal segment, and 128.16 meters, 215.77 meters to the width side of the vertical segments, respectively.
+The attic height is 139.71 meters. The building orientation is 62 degrees to the north.
+The building has 3 thermal zones with each segment as one thermal zone.
+The window-to-wall ratio is 0.32. The window sill height is 33.91 meters, the window height is 65.82 meters, and the window jamb width is 0.01 meters.
+The window U-factor is 6.36 W/m2K and the SHGC is 0.89. The wall is made of wood, with a thickness of 0.48 meters and the wall insulation is RSI 1.6 m2K/W, U-factor 0.63 W/m2K.
+The roof is made of metal, with a thickness of 0.09 meters and the roof insulation is RSI 5.4 m2K/W, U-factor 0.19 W/m2K.
+The floor is made of concrete, covered with carpet. The ventilation rate is 2.32 ach. The infiltration rate is 0.55 ach.
+The people density is 16.61 m2/person, the light density is 4.48 W/m2, and the electric equipment density is 22.63 W/m2.
+Occupancy starts at 7:00 and ends at 18:00. The occupancy rate is 1. The unoccupancy rate is 0.3.
+The heating setpoint is 21.54 Celsius in occupancy period and 15.86 Celsius in unoccupancy period.
+The cooling setpoint is 22.6 Celsius in occupancy period and 26.72 Celsius in unoccupancy period.
+"""
+input_ids = tokenizer(input, return_tensors="pt", truncation=False)
+generated_ids = model.generate(input_ids = input_ids.input_ids,
+                           attention_mask = input_ids.attention_mask,
+                           generation_config = generation_config)
+generated_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+# print(generated_output)
+
+# Output the building energy model in IDF file
+with open(file_path, 'r', encoding='utf-8') as file:
+    nextpart = file.read()
+final_text = nextpart + "\n\n" + generated_output
+with open(output_path, 'w', encoding='utf-8') as f:
+    f.write(final_text)
+print(f"Building Energy Model Auto-Generated: {output_path}")
 ```
 
-🔧 Installation
+## 📝 Citation
 
-- Clone the repository:
-```
-    git clone https://github.com/Gangjiang1/EPlus-LLM.git
-    cd EPlus-LLM
-```
-- Install required dependencies:
-```
-    pip install -r requirements.txt
-```
+If you find our work helpful, feel free to give us a cite.
 
-▶️ Running Auto-Building Energy Modeling via EPlus-LLM
 ```
-    cd v2
-    python EPlus-LLM/v2/Inference.py
+@article{jiang2025EPlus-LLMv2,
+  author    = {Gang Jiang and Jianli Chen},
+  title     = {Efficient fine-tuning of large language models for automated building energy modeling in complex cases},
+  journal   = {Automation in Construction},
+  volume    = {175},
+  pages     = {106223},
+  year      = {2025},
+  month     = {July},
+  doi       = {https://doi.org/10.1016/j.autcon.2025.106223}
+}
+
+@article{jiang2025prompting,
+  author    = {Gang Jiang and Zhihao Ma and Liang Zhang and Jianli Chen},
+  title     = {Prompt engineering to inform large language models in automated building energy modeling},
+  journal   = {Energy},
+  volume    = {316},
+  pages     = {134548},
+  year      = {2025},
+  month     = {Feb},
+  doi       = {https://doi.org/10.1016/j.energy.2025.134548}
+}
+
+@article{jiang2025EPlus-LLM,
+  author    = {Gang Jiang and Zhihao Ma and Liang Zhang and Jianli Chen},
+  title     = {Prompt engineering to inform large language models in automated building energy modeling},
+  journal   = {Applied Energy},
+  volume    = {367},
+  pages     = {123431},
+  year      = {2024},
+  month     = {Aug},
+  doi       = {https://doi.org/10.1016/j.apenergy.2024.123431}
+}
 ```