Instructions to use ProCreations/grug-27b-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-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 Desktop

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 @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": "ProCreations/grug-27b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

grug-27b-gguf / README.md

ProCreations

Make benchmark comparison grug

9007ff6 verified about 1 month ago

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	---
	license: apache-2.0
	base_model: ProCreations/grug-27b
	tags:
	- grug
	- gguf
	- llama.cpp
	- reasoning
	- token-efficient
	language:
	- en
	---

	# 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](https://huggingface.co/ProCreations/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](https://huggingface.co/ProCreations/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](https://huggingface.co/ProCreations/grug-27b) v2.1 \| [grug-35b](https://huggingface.co/ProCreations/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](https://huggingface.co/ProCreations/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).

	```bash
	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.