Instructions to use RnniaSnow/ST-Coder-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RnniaSnow/ST-Coder-14B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RnniaSnow/ST-Coder-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RnniaSnow/ST-Coder-14B")
model = AutoModelForCausalLM.from_pretrained("RnniaSnow/ST-Coder-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RnniaSnow/ST-Coder-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RnniaSnow/ST-Coder-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RnniaSnow/ST-Coder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RnniaSnow/ST-Coder-14B

SGLang

How to use RnniaSnow/ST-Coder-14B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RnniaSnow/ST-Coder-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RnniaSnow/ST-Coder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RnniaSnow/ST-Coder-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RnniaSnow/ST-Coder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RnniaSnow/ST-Coder-14B with Docker Model Runner:
```
docker model run hf.co/RnniaSnow/ST-Coder-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

Golden Samples: Verified ST code from real-world engineering projects.
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.