Instructions to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF",
	filename="Grug-12B-ROCmFP4_FAST.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF 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 maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF
# Run inference directly in the terminal:
llama cli -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF
# Run inference directly in the terminal:
llama cli -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

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 maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF
# Run inference directly in the terminal:
./llama-cli -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

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 maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

Use Docker

docker model run hf.co/maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

LM Studio
Jan
Ollama
How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with Ollama:
```
ollama run hf.co/maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF
```

Unsloth Studio

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-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 maczzinatui/Grug-12B-ROCmFP4_FAST-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 maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF to start chatting

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

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": "maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

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 maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

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 "maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF" \
  --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 maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with Docker Model Runner:
```
docker model run hf.co/maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF
```

Lemonade

How to use maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maczzinatui/Grug-12B-ROCmFP4_FAST-GGUF

Run and chat with the model

lemonade run user.Grug-12B-ROCmFP4_FAST-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

mac commited on 18 days ago

Commit

253fffa

verified ·

1 Parent(s): 38a7821

docs: add BENCH-aeon-full-suite.md (consolidated)

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BENCH-aeon-full-suite.md +31 -0

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+# AEON Bench Full Suite — Grug-12B-ROCmFP4_FAST (150 cases, 120 scored)
+**Date:** 2026-07-07
+**Framework:** AEON-Bench-Pod v1 (self-reported mode)
+**Hardware:** AMD RX 9060 XT 16 GB (charlie/ROCmFPX, ROCmFP4_FAST)
+**Suite:** 150 cases (math + instruction + reasoning + coding + prose)
+**Scored:** 120 of 150 (30 prose cases unscored — no judge endpoint configured)
+## Results
+**Overall mean: 0.558** across 120 scored cases
+| Category | Mean | N |
+|---|---|---|
+| coding | 0.800 | 30 |
+| reasoning | 0.767 | 30 |
+| instruction | 0.433 | 30 |
+| math | 0.233 | 30 |
+| Difficulty | Mean | N |
+|---|---|---|
+| easy | 1.000 | 8 |
+| medium | 0.833 | 12 |
+| hard | 0.650 | 20 |
+| expert | 0.531 | 32 |
+| frontier | 0.396 | 48 |
+## Notes
+- 30 prose cases required a frontier judge endpoint — excluded from the mean.
+- Bench run in self-reported mode. Mothership returned HTTP 403 (expected).