Instructions to use deucebucket/Granite-4.1-30B-Cerebellum-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 deucebucket/Granite-4.1-30B-Cerebellum-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 deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_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 deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_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 deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M

Use Docker

docker model run hf.co/deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M

LM Studio
Jan
Ollama
How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Ollama:
```
ollama run hf.co/deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M
```
Unsloth Desktop

How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_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": "deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Docker Model Runner:
```
docker model run hf.co/deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M
```

Lemonade

How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Granite-4.1-30B-Cerebellum-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use deucebucket/Granite-4.1-30B-Cerebellum-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 deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_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 deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deucebucket/Granite-4.1-30B-Cerebellum-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_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 "deucebucket/Granite-4.1-30B-Cerebellum-GGUF:Q3_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"

deucebucket commited on Jun 12

Commit

e3d174c

verified ·

1 Parent(s): d1f1248

docs: correct shot labels to match 0-shot harness; update renamed GGUF references

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -97,8 +97,8 @@ Instead of uniform quantization, we measure which weight groups survive aggressi
 | File | Size | Description |
 |------|------|-------------|
-| `Granite-4.1-30B-Cerebellum-v2.gguf` | 13 GB | Optimal mix — 3 groups demoted (attn_k, attn_q, attn_output), 4 kept at Q3_K_M |
-| `Granite-4.1-30B-Cerebellum-v1.gguf` | 12 GB | Aggressive — 5 groups demoted (all attn + ffn_gate) |
 ## Benchmarks
@@ -154,7 +154,7 @@ Works with any llama.cpp-compatible tool:
 ```bash
 # llama.cpp
-./llama-server --model Granite-4.1-30B-Cerebellum-v2.gguf -ngl 99 --ctx-size 4096
 # Ollama (create Modelfile pointing to the GGUF)
 # LM Studio (drag and drop)

 | File | Size | Description |
 |------|------|-------------|
+| `Granite-4.1-30B-Cerebellum-v2-Q3_K_M.gguf` | 13 GB | Optimal mix — 3 groups demoted (attn_k, attn_q, attn_output), 4 kept at Q3_K_M |
+| `Granite-4.1-30B-Cerebellum-v1-Q3_K_M.gguf` | 12 GB | Aggressive — 5 groups demoted (all attn + ffn_gate) |
 ## Benchmarks
 ```bash
 # llama.cpp
+./llama-server --model Granite-4.1-30B-Cerebellum-v2-Q3_K_M.gguf -ngl 99 --ctx-size 4096
 # Ollama (create Modelfile pointing to the GGUF)
 # LM Studio (drag and drop)