Update README.md

README.md

@@ -33,3 +33,72 @@ It is specifically designed for **Industrial Automation** and **PLC Programming*
 ### Requirements
 ```bash
 pip install transformers peft torch accelerate
+```
+
+### Inference Code (Python)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# 1. Load Base Model
+base_model_path = "Qwen/Qwen2.5-Coder-14B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_path,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
+
+# 2. Load LoRA Adapter
+lora_path = "RnniaSnow/ST-Coder-14B-LoRA"
+model = PeftModel.from_pretrained(model, lora_path)
+
+# 3. Generate Code
+prompt = "Write a Function Block (ST) for a PID controller with anti-windup mechanism."
+messages = [
+    {"role": "system", "content": "You are an expert IEC 61131-3 PLC programmer."},
+    {"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=1024,
+    temperature=0.2,  # Low temperature for code precision
+    top_p=0.9
+)
+
+output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(output_text)
+
+```
+
+## 🔧 Training Details
+
+This model was trained using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) with the following configuration:
+
+* **Base Model**: Qwen/Qwen2.5-Coder-14B-Instruct
+* **Training Method**: LoRA (Low-Rank Adaptation)
+* **Target Modules**: `all` (Applied to all linear layers for maximum expressivity)
+* **LoRA Rank**: 64
+* **LoRA Alpha**: 128
+* **Cutoff Length**: 8192 tokens
+* **Flash Attention**: Enabled (FA2)
+* **Precision**: BF16
+
+## 📂 Dataset
+
+The training dataset (`RnniaSnow/st-code-dataset`) consists of:
+
+1. **Golden Samples**: High-quality, verified ST code snippets from real-world engineering projects.
+2. **Synthetic Distillation**: Generated using DeepSeek-V3 with strict syntax constraints and self-correction pipelines to ensure logical correctness.
+
+## ⚠️ Disclaimer
+
+While this model is optimized for industrial programming, **LLM-generated code must always be verified and tested** on a simulation environment before deployment to physical hardware. The author assumes no liability for damages caused by the use of this code in production environments.
+