Update README.md

README.md

@@ -21,26 +21,63 @@ A prototype project exploring the use of fine-tuned large language models to aut
 [Paper here](https://doi.org/10.1016/j.apenergy.2024.123431).
 
 ## 🚀 Key Features
-- Scalability: Auto-generates complex EnergyPlus models, including varying geometries, materials, thermal zones, hourly schedules, and more.
-- Accuracy & Efficiency: Achieves 100% modeling accuracy while reducing manual modeling time by over 98%.
+- Scalability: Auto-generates EnergyPlus models, including varying geometry sizes and internal loads.
+- Accuracy & Efficiency: Achieves 100% modeling accuracy while reducing manual modeling time by over 95%.
 - Interaction & Automation: A user-friendly human-AI interface for seamless model creation and customization.
 
-- Flexible Design Scenarios:
-
-  ✅ Geometry: square, L-, T-, U-, and hollow-square-shaped buildings  
-  ✅ Roof types: flat, gable, hip – customizable attic/ridge height  
-  ✅ Orientation & windows: custom WWR, window placement, facade-specific controls  
-  ✅ Walls & materials: thermal properties, insulation types  
-  ✅ Internal loads: lighting, equipment, occupancy, infiltration/ventilation, schedules, heating/cooling setpoints  
-  ✅ Thermal zoning: configurable multi-zone layouts with core & perimeter zones  
-
 ## 🏗️ Target Users
 This current platform is designed for engineers, architects, and researchers working in building performance, sustainability, and resilience. It is especially useful during early-stage conceptual design when modeling decisions have the greatest impact.
 
 ## 🚀 Quick Start
 
-This repository contains v2 and v1 of EPlus-LLM, along with implementation details for the ABEM reference.
+Here provides a code snippet to show you how to load the EPlus-LLM and auto-generate building energy models.
+
+```python
+
+
+generation_config = model.generation_config
+
+generation_config.max_new_tokens = 1300
+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
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Qwen/Qwen2.5-32B-Instruct"
 
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
 
 📂 Repository Structure
 
@@ -67,4 +104,32 @@ This repository contains v2 and v1 of EPlus-LLM, along with implementation detai
 ```
     cd v2
     python EPlus-LLM/v2/Inference.py
+```
+
+## 📝 Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@article{jiang2025prompt,
+  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}
+}
+
+@article{jiang2025prompt,
+  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}
+}
 ```