Instructions to use ibm-granite/granite-4.0-1b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-4.0-1b-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ibm-granite/granite-4.0-1b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ibm-granite/granite-4.0-1b-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 ibm-granite/granite-4.0-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-4.0-1b-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 ibm-granite/granite-4.0-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-4.0-1b-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 ibm-granite/granite-4.0-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ibm-granite/granite-4.0-1b-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 ibm-granite/granite-4.0-1b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ibm-granite/granite-4.0-1b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ibm-granite/granite-4.0-1b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ibm-granite/granite-4.0-1b-GGUF with Ollama:
```
ollama run hf.co/ibm-granite/granite-4.0-1b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ibm-granite/granite-4.0-1b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ibm-granite/granite-4.0-1b-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": "ibm-granite/granite-4.0-1b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ibm-granite/granite-4.0-1b-GGUF with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-4.0-1b-GGUF:Q4_K_M
```

Lemonade

How to use ibm-granite/granite-4.0-1b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ibm-granite/granite-4.0-1b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-4.0-1b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ibm-granite/granite-4.0-1b-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 ibm-granite/granite-4.0-1b-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 ibm-granite/granite-4.0-1b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ibm-granite/granite-4.0-1b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ibm-granite/granite-4.0-1b-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 "ibm-granite/granite-4.0-1b-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"

Granite 4.0 1b (GGUF)

This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model.

Please reference the base model's full model card here: https://huggingface.co/ibm-granite/granite-4.0-1b

Known Issues

This model often uses the full numerical range of a 32-bit float (f32), so variants with smaller numerical ranges may run into precision errors at inference. The F16 variant is known to fail on many hardware combinations.

The recommended full-precision variant is bf16.