Instructions to use ProCreations/grug-35b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProCreations/grug-35b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ProCreations/grug-35b-gguf",
	filename="grug-35b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ProCreations/grug-35b-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-35b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-35b-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-35b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ProCreations/grug-35b-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-35b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ProCreations/grug-35b-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-35b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ProCreations/grug-35b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ProCreations/grug-35b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ProCreations/grug-35b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProCreations/grug-35b-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-35b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ProCreations/grug-35b-gguf:Q4_K_M

Ollama
How to use ProCreations/grug-35b-gguf with Ollama:
```
ollama run hf.co/ProCreations/grug-35b-gguf:Q4_K_M
```

Unsloth Studio

How to use ProCreations/grug-35b-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-35b-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-35b-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-35b-gguf to start chatting

How to use ProCreations/grug-35b-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-35b-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-35b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ProCreations/grug-35b-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-35b-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-35b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ProCreations/grug-35b-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-35b-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-35b-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-35b-gguf with Docker Model Runner:
```
docker model run hf.co/ProCreations/grug-35b-gguf:Q4_K_M
```

Lemonade

How to use ProCreations/grug-35b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ProCreations/grug-35b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.grug-35b-gguf-Q4_K_M

List all available models

lemonade list

grug-35b GGUF

grug brain inside rock. no custom Grug instruction required.

July 15, 2026 GGUF replacement: same public repo, all popular quants rebuilt from the corrected parent. Old files preserved on pre-intrinsic-style-fix-2026-07-14.

These files were rebuilt from the intrinsic-style-gated ProCreations/grug-35b checkpoint. The parent and embedded template contain no hidden Grug/style prompt; ordinary chat and tool-enabled agent scaffolds rely on the tuned weights.

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

file	size
`grug-35b-Q4_K_M.gguf`	19.71 GiB
`grug-35b-Q5_K_M.gguf`	23.03 GiB
`grug-35b-Q6_K.gguf`	26.56 GiB
`grug-35b-Q8_0.gguf`	34.37 GiB

Q4_K_M: recommended local default
Q5_K_M: more accuracy meat
Q6_K: high-quality rock
Q8_0: closest popular quantized weight

All four loaded and generated successfully with llama.cpp f955e394bf94e01e5e36186d13c985727e5ef5b5 before upload. Hashes live in SHA256SUMS; smoke evidence lives in smoke-results.json.

Full BF16 parent: HumanEval 80.5%, MBPP 88.0%, broad valid/strict/right 100.0/100.0/95.0%, unprompted dialect-clean 100.0%.

Quant-specific full benchmark not claimed. Runtime must honor the embedded Qwen3.5 template. Reasoning stays in <think>...</think>; native XML tool calls stay intact.

Downloads last month: 837

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for ProCreations/grug-35b-gguf

Base model

deepreinforce-ai/Ornith-1.0-35B

Finetuned

ProCreations/grug-35b

Quantized

(2)

this model