Instructions to use super-inference/super-12B-ste-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use super-inference/super-12B-ste-coding with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf super-inference/super-12B-ste-coding:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf super-inference/super-12B-ste-coding:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf super-inference/super-12B-ste-coding:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf super-inference/super-12B-ste-coding:Q4_K_M

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 super-inference/super-12B-ste-coding:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf super-inference/super-12B-ste-coding:Q4_K_M

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 super-inference/super-12B-ste-coding:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf super-inference/super-12B-ste-coding:Q4_K_M

Use Docker

docker model run hf.co/super-inference/super-12B-ste-coding:Q4_K_M

LM Studio
Jan
Ollama
How to use super-inference/super-12B-ste-coding with Ollama:
```
ollama run hf.co/super-inference/super-12B-ste-coding:Q4_K_M
```

Unsloth Studio

How to use super-inference/super-12B-ste-coding 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 super-inference/super-12B-ste-coding 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 super-inference/super-12B-ste-coding to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for super-inference/super-12B-ste-coding to start chatting

How to use super-inference/super-12B-ste-coding with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf super-inference/super-12B-ste-coding:Q4_K_M

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": "super-inference/super-12B-ste-coding:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use super-inference/super-12B-ste-coding with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf super-inference/super-12B-ste-coding:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "super-inference/super-12B-ste-coding:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use super-inference/super-12B-ste-coding with Docker Model Runner:
```
docker model run hf.co/super-inference/super-12B-ste-coding:Q4_K_M
```

Lemonade

How to use super-inference/super-12B-ste-coding with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull super-inference/super-12B-ste-coding:Q4_K_M

Run and chat with the model

lemonade run user.super-12B-ste-coding-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use super-inference/super-12B-ste-coding with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf super-inference/super-12B-ste-coding:Q4_K_M

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 super-inference/super-12B-ste-coding:Q4_K_M

Run Hermes

hermes

Atomic Chat

super-12B-ste-coding : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf super-inference/super-12B-ste-coding --jinja
For multimodal models: llama-mtmd-cli -hf super-inference/super-12B-ste-coding --jinja

Available Model files:

gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2.Q6_K.gguf
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2.BF16.gguf
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2.Q4_K_M.gguf
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2.F16.gguf
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2.Q8_0.gguf
gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2.BF16-mmproj.gguf This was trained 2x faster with Unsloth

Downloads last month: -

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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