---
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).