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 GGUF

GGUF conversions of ibm-granite/granite-4.2-8b for 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

# 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

The source safetensors (bf16) were converted with llama.cpp b9770: convert_hf_to_gguf.py ibm-granite/granite-4.2-8b --outtype bf16
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 for the model's details, intended use and limitations.

Downloads last month: 139

GGUF

Model size

9B params

Architecture

granite

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for webAI-Official/granite-4.2-8B

Base model

ibm-granite/granite-4.1-8b-base

Finetuned

ibm-granite/granite-4.2-8b

Quantized

(47)

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