Instructions to use ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ProCreations/grug-v2-9b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ProCreations/grug-v2-9b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ProCreations/grug-v2-9b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProCreations/grug-v2-9b-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": "ProCreations/grug-v2-9b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ProCreations/grug-v2-9b-gguf:Q4_K_M

Ollama
How to use ProCreations/grug-v2-9b-gguf with Ollama:
```
ollama run hf.co/ProCreations/grug-v2-9b-gguf:Q4_K_M
```

Unsloth Studio

How to use ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ProCreations/grug-v2-9b-gguf to start chatting

How to use ProCreations/grug-v2-9b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ProCreations/grug-v2-9b-gguf: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": "ProCreations/grug-v2-9b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ProCreations/grug-v2-9b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ProCreations/grug-v2-9b-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 "ProCreations/grug-v2-9b-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"

Docker Model Runner
How to use ProCreations/grug-v2-9b-gguf with Docker Model Runner:
```
docker model run hf.co/ProCreations/grug-v2-9b-gguf:Q4_K_M
```

Lemonade

How to use ProCreations/grug-v2-9b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ProCreations/grug-v2-9b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.grug-v2-9b-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-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 ProCreations/grug-v2-9b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

grug-v2-9b GGUF

July 15, 2026 default-brain audit: parent BF16 passed 33/33 fresh neutral reasoning probes and 33/33 Grug-clean probes, including tool-enabled agent systems, with original instruction-free template. Existing rocks already derive from that intrinsic checkpoint. grug not churn identical rock; filenames, weights, and hashes below remain same. This note records verification.

grug honest note: old rocks replaced after dialect repair. same filenames, new weights and new hashes. old rock knew club skill but sometimes thought polished tiny English. new rock comes from corrected merged bird. redownload file.

full bird: ProCreations/grug-v2-9b. pre-repair GGUF repo state preserved on branch pre-dialect-fix-2026-07-13.

choose rock

file	size
`grug-v2-9b-Q4_K_M.gguf`	5.24 GiB
`grug-v2-9b-Q5_K_M.gguf`	6.02 GiB
`grug-v2-9b-Q6_K.gguf`	6.85 GiB
`grug-v2-9b-Q8_0.gguf`	8.87 GiB

Q4_K_M: best default small rock
Q5_K_M: more accuracy meat, still compact
Q6_K: high-quality local rock
Q8_0: biggest popular rock, closest quantized weight

grug verify

all four rock converted fresh from same gated BF16 candidate. no quant-of-quant. all loaded and generated on GPU with llama.cpp commit 14d3ba45f3369e75a308212399cfada5d349883b before upload. exact hashes in SHA256SUMS; smoke logs summarized in smoke-results.json.

full merged bird exact greedy harness:

test	score %
HumanEval pass@1	82.9
MBPP pass@1	77.0
card valid / strict / right tool	100.0 / 100.0 / 100.0
broad valid / strict / right tool	100.0 / 100.0 / 94.1
held-out dialect-clean trace	100.0

quant-specific full benchmark not claimed. grug not invent number.

run

llama-cli -hf ProCreations/grug-v2-9b-gguf:Q4_K_M -n 512 -c 8192

need recent llama.cpp Qwen3.5 support. runtime must honor embedded chat template. grug reasoning stays in <think>...</think>. native XML tool club stays sacred.

Downloads last month: 1,893

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for ProCreations/grug-v2-9b-gguf

Base model

ornith-ai/Ornith-1.0-9B

Finetuned

ProCreations/grug-9b

Finetuned

ProCreations/grug-v2-9b

Quantized

(1)

this model