Instructions to use ProCreations/grug-27b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProCreations/grug-27b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ProCreations/grug-27b-gguf",
	filename="grug-27b-Q3_K_M.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 ProCreations/grug-27b-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-27b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-27b-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-27b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-27b-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-27b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ProCreations/grug-27b-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-27b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ProCreations/grug-27b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ProCreations/grug-27b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use ProCreations/grug-27b-gguf with Ollama:
```
ollama run hf.co/ProCreations/grug-27b-gguf:Q4_K_M
```

Unsloth Studio

How to use ProCreations/grug-27b-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-27b-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-27b-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-27b-gguf to start chatting

How to use ProCreations/grug-27b-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-27b-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-27b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ProCreations/grug-27b-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-27b-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-27b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ProCreations/grug-27b-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-27b-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-27b-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-27b-gguf with Docker Model Runner:
```
docker model run hf.co/ProCreations/grug-27b-gguf:Q4_K_M
```

Lemonade

How to use ProCreations/grug-27b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ProCreations/grug-27b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.grug-27b-gguf-Q4_K_M

List all available models

lemonade list

grug-27b-gguf

2026-07-23: all rocks re-squeezed from v2.1 weights (deep think on hard problems, stuck-loop escape, stop discipline - full changelog on grug-27b card). re-download if you grab rocks before. mmproj unchanged (vision tower untouched).

grug brain squeezed into small rock. run on your cave computer with llama.cpp.

this GGUF of grug-27b: Qwen3.6-27B that think in dense grug-speak inside <think>, answer in normal english. same reasoning depth, way fewer think token. full story on main model card.

rock sizes

file	quant	size	grug opinion
grug-27b-Q8_0.gguf	Q8_0	28.6 GB	basically bf16. big rock.
grug-27b-Q6_K.gguf	Q6_K	22.1 GB	very good rock
grug-27b-Q5_K_M.gguf	Q5_K_M	19.2 GB	good rock
grug-27b-Q4_K_M.gguf	Q4_K_M	16.5 GB	best size/smart trade. grug pick this.
grug-27b-Q3_K_M.gguf	Q3_K_M	13.3 GB	small rock. smart mostly survive.
mmproj-grug-27b-f16.gguf	mmproj f16	see repo	eye rock. give grug vision back.

every rock load-tested with llama.cpp before upload. no missing-tensor sickness (grug check twice now, learn from 9b).

Q4 person? special rock exist

grug make QAT version of Q4_K_M: weights trained while feeling 4-bit rounding rock before final squish. better Q4 quality, same grug brain: grug-27b-qat-q4-gguf. rocks here best for Q8/Q6/Q5 people.

how run

need recent llama.cpp (qwen3_5 arch support).

llama-server -m grug-27b-Q4_K_M.gguf -c 16384 --temp 0.6 --top-p 0.95 --top-k 20

vision NOW work: pair any quant with mmproj-grug-27b-f16.gguf (llama-server -m grug-27b-Q4_K_M.gguf --mmproj mmproj-grug-27b-f16.gguf). MTP still not included.
context: base support 262144, pick what your RAM allow
thinking on by default, reasoning arrive inside <think>...</think>
for agent frameworks (OpenCode etc): works with think-stripped history, grug trained for exactly that world

grug made by ProCreations. base brain by Qwen team.

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ProCreations/grug-27b-gguf

Base model

Qwen/Qwen3.6-27B

Finetuned

ProCreations/grug-27b

Quantized

(7)

this model