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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kai-os/Grug-12B-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 kai-os/Grug-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kai-os/Grug-12B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kai-os/Grug-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kai-os/Grug-12B-GGUF: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 kai-os/Grug-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kai-os/Grug-12B-GGUF: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 kai-os/Grug-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kai-os/Grug-12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kai-os/Grug-12B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use kai-os/Grug-12B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kai-os/Grug-12B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kai-os/Grug-12B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kai-os/Grug-12B-GGUF:Q4_K_M

Ollama
How to use kai-os/Grug-12B-GGUF with Ollama:
```
ollama run hf.co/kai-os/Grug-12B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use kai-os/Grug-12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kai-os/Grug-12B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kai-os/Grug-12B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kai-os/Grug-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/kai-os/Grug-12B-GGUF:Q4_K_M
```

Lemonade

How to use kai-os/Grug-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kai-os/Grug-12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Grug-12B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kai-os/Grug-12B-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 kai-os/Grug-12B-GGUF: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 kai-os/Grug-12B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kai-os/Grug-12B-GGUF with OpenClaw:

Start the llama.cpp server

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

kai-os commited on Jul 2

Commit

ea672ec

verified ·

1 Parent(s): 35efd6e

Upload Grug 12B GGUF Q4_K_M

Browse files

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Grug-12B-Q4_K_M.gguf +3 -0
README.md +57 -0
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+---
+base_model: kai-os/Grug-12B
+library_name: gguf
+pipeline_tag: text-generation
+license: other
+tags:
+- gguf
+- llama.cpp
+- q4_k_m
+- quantized
+- reasoning
+- compact-reasoning
+- grugthink
+- gemma-4
+---
+![Grug 12B banner](banner.png)
+# Grug 12B GGUF
+This is the adjacent GGUF release for [`kai-os/Grug-12B`](https://huggingface.co/kai-os/Grug-12B).
+The main repo contains the merged Transformers/safetensors fine-tune; this repo contains a llama.cpp quantized file for local inference.
+## Files
+- `Grug-12B-Q4_K_M.gguf` - practical mixed-precision 4-bit GGUF quant, about 7.0 GB.
+- `banner.png` - model banner.
+## Conversion
+Converted from the merged full model release, then quantized with llama.cpp.
+- Source model: `kai-os/Grug-12B`
+- llama.cpp commit: `4fc4ec5`
+- Conversion outtype: `BF16`
+- Uploaded quant: `Q4_K_M`
+- Quantized size: `7024.34 MiB`
+- Bits per weight: `4.95 BPW`
+- SHA256: `3928e9af604369c111ec7098660781f26e3dc350080e3786ef9dd69881967348`
+## Usage
+Use a recent llama.cpp build with Gemma 4 / Gemma4 Unified GGUF support.
+```bash
+hf download kai-os/Grug-12B-GGUF Grug-12B-Q4_K_M.gguf
+llama-cli -m Grug-12B-Q4_K_M.gguf -p "What is 2+2? Answer briefly." -n 64
+```
+## Training Summary
+Grug 12B is a compact-reasoning fine-tune of `google/gemma-4-12B-it` trained with QLoRA, then merged into the base model for release.
+The training target is terse, high-density reasoning that preserves constraints, branching decisions, invariants, edge cases, and final-answer checks while reducing unnecessary reasoning-token verbosity.
+See the full model card at [`kai-os/Grug-12B`](https://huggingface.co/kai-os/Grug-12B) for the technique details, dataset provenance, filtering notes, and benchmark notes.
+Thanks to [Lambda](https://lambda.com/) for the compute credits used for this work.

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Git LFS Details

SHA256: 739061a25acac7d2558dedefda8a0aba8027eaf4f40239690d8d516de7a66fcf
Pointer size: 132 Bytes
Size of remote file: 3.19 MB