README.md

metadata

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

🤖 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

pip install transformers torch accelerate

2. Inference with Transformers

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)

vllm serve RnniaSnow/ST-Coder-14B --tensor-parallel-size 1 --max-model-len 8192

🔧 Training Details

This model was trained using 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.