Instructions to use webAI-Official/granite-4.2-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use webAI-Official/granite-4.2-8B 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 webAI-Official/granite-4.2-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webAI-Official/granite-4.2-8B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf webAI-Official/granite-4.2-8B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webAI-Official/granite-4.2-8B: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 webAI-Official/granite-4.2-8B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf webAI-Official/granite-4.2-8B: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 webAI-Official/granite-4.2-8B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webAI-Official/granite-4.2-8B:Q4_K_M

Use Docker

docker model run hf.co/webAI-Official/granite-4.2-8B:Q4_K_M

LM Studio
Jan

vLLM

How to use webAI-Official/granite-4.2-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webAI-Official/granite-4.2-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webAI-Official/granite-4.2-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webAI-Official/granite-4.2-8B:Q4_K_M

Ollama
How to use webAI-Official/granite-4.2-8B with Ollama:
```
ollama run hf.co/webAI-Official/granite-4.2-8B:Q4_K_M
```
Unsloth Desktop

How to use webAI-Official/granite-4.2-8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webAI-Official/granite-4.2-8B: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": "webAI-Official/granite-4.2-8B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use webAI-Official/granite-4.2-8B with Docker Model Runner:
```
docker model run hf.co/webAI-Official/granite-4.2-8B:Q4_K_M
```

Lemonade

How to use webAI-Official/granite-4.2-8B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull webAI-Official/granite-4.2-8B:Q4_K_M

Run and chat with the model

lemonade run user.granite-4.2-8B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use webAI-Official/granite-4.2-8B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webAI-Official/granite-4.2-8B: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 webAI-Official/granite-4.2-8B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use webAI-Official/granite-4.2-8B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webAI-Official/granite-4.2-8B: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 "webAI-Official/granite-4.2-8B: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-4.2-8B / README.md

cutterdalton-webai

Model card: show file sizes in GB

34c0b07 verified 18 days ago

preview code

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	---
	license: apache-2.0
	base_model: ibm-granite/granite-4.2-8b
	base_model_relation: quantized
	library_name: gguf
	pipeline_tag: text-generation
	tags:
	- gguf
	- llama.cpp
	- granite
	- granite-4.2
	---

	# granite-4.2-8B GGUF

	GGUF conversions of [ibm-granite/granite-4.2-8b](https://huggingface.co/ibm-granite/granite-4.2-8b) for [llama.cpp](https://github.com/ggml-org/llama.cpp) at 16-bit, 8-bit and 4-bit precision.

	## Files

	\| File \| Precision \| Size \|
	\|---\|---\|---\|
	\| `granite-4.2-8b-BF16.gguf` \| BF16 (16-bit, lossless from source) \| 17.6 GB \|
	\| `granite-4.2-8b-Q8_0.gguf` \| Q8_0 (8-bit, 8.50 bits per weight) \| 9.3 GB \|
	\| `granite-4.2-8b-Q4_K_M.gguf` \| Q4_K_M (4-bit, 4.86 bits per weight) \| 5.3 GB \|

	## Usage

	```bash
	# Chat server with Granite's embedded chat template (tool calling and thinking)
	llama-server -hf webAI-Official/granite-4.2-8B:Q4_K_M --jinja

	# Interactive chat
	llama-cli -hf webAI-Official/granite-4.2-8B:Q8_0
	```

	Thinking is on by default in the chat template. Pass `--chat-template-kwargs '{"enable_thinking": false}'` to `llama-server` to turn it off.

	## How these files were made

	1. The source safetensors (bf16) were converted with llama.cpp `b9770`:
	`convert_hf_to_gguf.py ibm-granite/granite-4.2-8b --outtype bf16`
	2. Q8_0 and Q4_K_M were each quantized directly from the BF16 file with `llama-quantize`. No importance matrix was used.

	## Performance

	Measured with `llama-bench` (llama.cpp `b9770`, Metal) on an Apple M5 Pro with 24 GB of memory:

	\| File \| Prompt processing, 512 tokens (tok/s) \| Generation, 128 tokens (tok/s) \|
	\|---\|---\|---\|
	\| BF16 \| 834 \| 15.3 \|
	\| Q8_0 \| 1080 \| 29.1 \|
	\| Q4_K_M \| 1051 \| 47.8 \|

	On a 24 GB machine, BF16 does not fit in GPU memory with llama.cpp's default context size. Use a smaller context, such as `-c 2048`, or choose Q8_0.

	## License

	Apache 2.0, the same as the base model. See [ibm-granite/granite-4.2-8b](https://huggingface.co/ibm-granite/granite-4.2-8b) for the model's details, intended use and limitations.