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.

27b and 35b hunt same prey

both parent grug hunt HumanEval and sanitized MBPP. number below come from big parent brain, NOT squeezed GGUF rock. grug not claim rock test it never get. number show pass@1 percent. bold grug win that hunt.

hunt	grug-27b v2.1	grug-35b rebuilt
HumanEval (164)	87.2	80.5
MBPP sanitized (100)	85.0	88.0

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.

if rock act broken

single-token spam ("/" forever etc) = NOT the rock. hybrid DeltaNet brain CANNOT survive llama.cpp context-shift: old builds shift on context overflow and corrupt the recurrent state into token spam. fix:

use RECENT llama.cpp (qwen3_5 support; new builds refuse instead of shift)
agent frontends (OpenCode etc): set -c 16384 or bigger
still broken? re-download rock (verify size) + check backend grug re-test rock after every report: loads clean, zero spam at proper config.

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: 2,108

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for ProCreations/grug-27b-gguf

Base model

Qwen/Qwen3.6-27B

Finetuned

ProCreations/grug-27b

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