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README.md

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----
-license: mit
----
+---
+license: mit
+library_name: transformers
+base_model: Qwen/Qwen2.5-Coder-14B-Instruct
+datasets:
+- RnniaSnow/st-code-dataset
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- code
+- plc
+- iec-61131-3
+- structured-text
+- industrial-automation
+- qwen
+- llama-factory
+---
+
+# ST-Coder-14B
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/>
+</div>
+
+## 🤖 Model Description
+
+**ST-Coder-14B** is a specialized code generation model fine-tuned on **Qwen2.5-Coder-14B-Instruct**. It is specifically optimized for **Industrial Automation** tasks, with a primary focus on the **IEC 61131-3 Structured Text (ST)** programming language.
+
+Unlike general-purpose coding models, ST-Coder-14B has been trained on high-quality, domain-specific data to understand:
+* **PLC Logic**: PID control, Motion Control, Safety logic, State Machines.
+* **IEC 61131-3 Syntax**: Correct usage of `FUNCTION_BLOCK`, `VAR_INPUT`, `VAR_OUTPUT`, and strict typing rules.
+* **Industrial Protocols**: Modbus, TCP/IP socket handling in ST, and vendor-specific nuances (Codesys, TwinCAT, Siemens SCL).
+
+## 💻 Quick Start
+
+### 1. Installation
+
+```bash
+pip install transformers torch accelerate
+```
+### 2. Inference with Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model
+model_id = "RnniaSnow/ST-Coder-14B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Prepare the prompt
+system_prompt = "You are an expert industrial automation engineer specializing in IEC 61131-3 Structured Text."
+user_prompt = "Write a Function Block for a 3-axis motion control system with error handling."
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Generate
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=2048,
+    temperature=0.2, # Low temperature is recommended for code generation
+    top_p=0.9
+)
+
+# Decode output
+output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(output)
+
+```
+
+### 3. Usage with vLLM (Recommended for Production)
+
+```bash
+vllm serve RnniaSnow/ST-Coder-14B --tensor-parallel-size 1 --max-model-len 8192
+
+```
+
+## 🔧 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
+* **Finetuning Method**: Full LoRA Merge (Target modules: `all`)
+* **Precision**: BF16
+* **Context Window**: 8192 tokens
+* **Optimizer**: AdamW (Paged)
+* **Learning Rate Strategy**: Cosine with warmup
+
+The training data includes a mix of:
+
+1. **Golden Samples**: Verified ST code from real-world engineering projects.
+2. **Synthetic Data**: High-quality instruction-response pairs generated via DeepSeek-V3 distillation, focusing on edge cases and complex logic.
+
+## ⚠️ Disclaimer & Safety
+
+**Industrial Control Systems (ICS) carry significant physical risks.** * This model generates code based on statistical probabilities and does **not** guarantee functional correctness or safety.
+
+* **Always** verify, simulate, and test generated code in a safe environment before deploying to physical hardware (PLCs, robots, drives).
+* The authors assume no liability for any damage or injury resulting from the use of this model.
+
+## 📜 License
+
+This model is licensed under the [MIT License](https://opensource.org/licenses/MIT).