Instructions to use ibm-granite/granite-3.3-8b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-3.3-8b-instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ibm-granite/granite-3.3-8b-instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ibm-granite/granite-3.3-8b-instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ibm-granite/granite-3.3-8b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-3.3-8b-instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-3.3-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M

SGLang

How to use ibm-granite/granite-3.3-8b-instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ibm-granite/granite-3.3-8b-instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-3.3-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ibm-granite/granite-3.3-8b-instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-3.3-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ibm-granite/granite-3.3-8b-instruct-GGUF with Ollama:
```
ollama run hf.co/ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-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-3.3-8b-instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ibm-granite/granite-3.3-8b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use ibm-granite/granite-3.3-8b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-3.3-8b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-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-3.3-8b-instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ibm-granite/granite-3.3-8b-instruct-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-3.3-8b-instruct-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-3.3-8b-instruct-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"

thank you for GGUF!

by jacek2024 - opened Apr 16, 2025

Discussion

jacek2024

Apr 16, 2025

It’s really nice to have GGUF available from IBM.

gabegoodhart

IBM Granite org Apr 16, 2025

You're welcome! We've heard the signal on the confusion for GGUFs, so we'll now be co-locating official GGUFs here in the ibm-granite org under this collection.

RougueSpud

May 30, 2025

Hey how do you enable thinking using Ollama, LMStudio, etc?

gabegoodhart

IBM Granite org Jun 2, 2025

Hi @RougueSpud ! There are several ways to enable thinking in Ollama and LM Studio, but only one of them works today:

Using these GGUFs which don't contain the official Ollama chat template, you would need to replicate the logic in the official chat template on the client side to enable thinking (adding the requisite system prompt section here)
If you are using the official models from Ollama, they come with a chat template that supports enabling thinking with a special element to the messages field when making an API call with the following format: {"role": "control", "content": "thinking"}. This, unfortunately, is not accessible through the CLI
Ollama just introduced a new thinking capability in in 0.9.0. This will require some special templating in the chat template to get it to work correctly for Granite. I'm actively working on this for the official Granite models, but it isn't done yet.

At the moment, there isn't a systematic way to use thinking through LM Studio without doing client-side system prompt construction (option [1] above).

gabegoodhart

IBM Granite org Jun 3, 2025

I've now got updated template versions for Ollama that allow the built-in "think" capability to work. They're pushed to my personal staging account (gabegoodhart/granite3.2, gabegoodhart/granite3.3) while we work to get them on the official library. You can try it out as follows:

ollama pull gabegoodhart/granite3.3
ollama run gabegoodhart/granite3.3 --think "What's the best way to visit all of my clients in my sales region?"

RougueSpud

Jun 9, 2025

I've now got updated template versions for Ollama that allow the built-in "think" capability to work. They're pushed to my personal staging account (gabegoodhart/granite3.2, gabegoodhart/granite3.3) while we work to get them on the official library. You can try it out as follows:
ollama pull gabegoodhart/granite3.3
ollama run gabegoodhart/granite3.3 --think "What's the best way to visit all of my clients in my sales region?"

Thank you

gabegoodhart

IBM Granite org Jun 11, 2025

One other important note related to running the GGUF models locally with Ollama: You can easily run them directly from huggingface with a command like the following:

ollama run hf.co/ibm-granite/granite-3.3-8b-instruct-GGUF:Q4_K_M

When running this way, if you want to enable thinking, you will need to do client-side template expansion (eg using apply_chat_template from transformers) and then use raw generation in Ollama.

cbarkinozer

Oct 29, 2025

Thanks for the GGUF! Any plans to share an AWQ version too, so it can run quantised on vLLM?

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