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"

ProCreations commited on Jul 23

Commit

31ffbfb

verified ·

1 Parent(s): 22d2e1e

Upload README.md with huggingface_hub

Browse files

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README.md +4 -1

README.md CHANGED Viewed

@@ -29,6 +29,7 @@ model card.
 | 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. |
 every rock load-tested with llama.cpp before upload. no missing-tensor
 sickness (grug check twice now, learn from 9b).
@@ -48,7 +49,9 @@ 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
 ```
-- text-only GGUF (vision + MTP not included; use safetensors repo for vision)
 - 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-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).
 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,