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

Libraries

How to use RnniaSnow/ST-Coder-14B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RnniaSnow/ST-Coder-14B-GGUF",
	filename="ST-Coder-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RnniaSnow/ST-Coder-14B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RnniaSnow/ST-Coder-14B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf RnniaSnow/ST-Coder-14B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RnniaSnow/ST-Coder-14B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf RnniaSnow/ST-Coder-14B-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf RnniaSnow/ST-Coder-14B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf RnniaSnow/ST-Coder-14B-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf RnniaSnow/ST-Coder-14B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RnniaSnow/ST-Coder-14B-GGUF:F16

Use Docker

docker model run hf.co/RnniaSnow/ST-Coder-14B-GGUF:F16

LM Studio
Jan
Ollama
How to use RnniaSnow/ST-Coder-14B-GGUF with Ollama:
```
ollama run hf.co/RnniaSnow/ST-Coder-14B-GGUF:F16
```

Unsloth Studio new

How to use RnniaSnow/ST-Coder-14B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RnniaSnow/ST-Coder-14B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RnniaSnow/ST-Coder-14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RnniaSnow/ST-Coder-14B-GGUF to start chatting

Pi new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RnniaSnow/ST-Coder-14B-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RnniaSnow/ST-Coder-14B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RnniaSnow/ST-Coder-14B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RnniaSnow/ST-Coder-14B-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default RnniaSnow/ST-Coder-14B-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use RnniaSnow/ST-Coder-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/RnniaSnow/ST-Coder-14B-GGUF:F16
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RnniaSnow/ST-Coder-14B-GGUF:F16

Run and chat with the model

lemonade run user.ST-Coder-14B-GGUF-F16

List all available models

lemonade list

ST-Coder-14B (GGUF)

This repository contains the GGUF quantized versions of RnniaSnow/ST-Coder-14B.

ST-Coder-14B is an industrial-grade Large Language Model optimized for Programmable Logic Controller (PLC) programming, specifically focused on the IEC 61131-3 Structured Text (ST) language.

By providing GGUF formats, automation engineers can run this model locally and entirely offline on standard laptops or IPCs (Industrial PCs) using CPU or edge GPUs, which is crucial for secure and air-gapped shop-floor environments.

💾 Available Quantization Formats

We provide several quantization levels. Choose the one that best fits your hardware (RAM/VRAM):

File Name	Bit	Size	RAM Required	Recommended For
`st-coder-14b-q4_k_m.gguf`	4-bit	~8.5 GB	12 GB+	Recommended. Best balance of speed, size, and code logic preservation.
`st-coder-14b-q6_k.gguf`	6-bit	~11.5 GB	16 GB+	Very high quality, minimal precision loss.
`st-coder-14b-q8_0.gguf`	8-bit	~15.2 GB	20 GB+	Near-lossless precision for complex engineering math/logic.

Note: Code generation models are sensitive to heavy quantization. We do not recommend using anything below Q4 for ST code generation, as syntax accuracy may degrade.

🚀 How to Use

Since this model is based on the Qwen2.5 architecture, it uses the ChatML prompt format. Most modern tools will auto-detect this from the GGUF metadata.

Method 1: LM Studio (Easiest GUI)

This is the recommended method for Windows/macOS users who prefer a graphical interface.

Download and install LM Studio.
Search for RnniaSnow/ST-Coder-14B-GGUF in the top search bar.
Download the q4_k_m or q6_k file.
Load the model, set the system prompt to: "You are an expert industrial automation engineer specializing in IEC 61131-3 Structured Text." and start chatting.

Method 2: Ollama (CLI & API)

You can easily serve this model using Ollama.

Download your preferred .gguf file to your local machine.
Create a file named Modelfile in the same directory with the following content:

FROM ./st-coder-14b-q4_k_m.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
SYSTEM """You are an expert industrial automation engineer specializing in IEC 61131-3 Structured Text."""
PARAMETER temperature 0.2
PARAMETER top_p 0.9

Create and run the model in your terminal:

ollama create st-coder -f Modelfile
ollama run st-coder "Write a Function Block for a PID controller."

Method 3: llama.cpp (Command Line)

For advanced users deploying via llama.cpp:

./llama-cli -m st-coder-14b-q4_k_m.gguf -p "<|im_start|>system\nYou are an expert PLC programmer.<|im_end|>\n<|im_start|>user\nWrite an ST program for a conveyor belt motor.<|im_end|>\n<|im_start|>assistant\n" -n 1024 -c 8192 --temp 0.2

⚠️ Disclaimer & Industrial Safety

Industrial Control Systems (ICS) carry significant physical risks. * This AI model generates code based on statistical probabilities and does not guarantee logical correctness, real-time safety, or hardware compatibility.

Always verify, simulate, and strictly test the generated code in a safe environment before deploying it to physical hardware (PLCs, drives, robotics).
The creators of this model assume absolutely no liability for any damage, injury, or production downtime resulting from the use of this code.