Instructions to use ibm-granite/granite-vision-3.3-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-vision-3.3-2b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ibm-granite/granite-vision-3.3-2b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ibm-granite/granite-vision-3.3-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ibm-granite/granite-vision-3.3-2b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ibm-granite/granite-vision-3.3-2b-GGUF with Ollama:
```
ollama run hf.co/ibm-granite/granite-vision-3.3-2b-GGUF:Q4_K_M
```

Unsloth Studio

How to use ibm-granite/granite-vision-3.3-2b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ibm-granite/granite-vision-3.3-2b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ibm-granite/granite-vision-3.3-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ibm-granite/granite-vision-3.3-2b-GGUF to start chatting

How to use ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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-vision-3.3-2b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-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-vision-3.3-2b-GGUF:Q4_K_M

Run Hermes

hermes

OpenClaw new

How to use ibm-granite/granite-vision-3.3-2b-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-vision-3.3-2b-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-vision-3.3-2b-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"

Docker Model Runner
How to use ibm-granite/granite-vision-3.3-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-vision-3.3-2b-GGUF:Q4_K_M
```

Lemonade

How to use ibm-granite/granite-vision-3.3-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ibm-granite/granite-vision-3.3-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-vision-3.3-2b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

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-vision-3.3-2b

Model Summary: Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as image segmentation, doctags generation, and multi-page support (see Experimental Capabilities for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image tasks. Granite-vision-3.3-2b was trained by fine-tuning a Granite large language model with both image and text modalities.

Paper: Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence. Note that the paper describes Granite Vision 3.2. Granite Vision 3.3 shares most of the technical underpinnings with Granite 3.2. However, there are several enhancements in terms of new and improved vision encoder, many new high quality datasets for training, and several new experimental capabilities.
Release Date: Jun 11th, 2025
License: Apache 2.0

Supported Input Format: Currently the model supports English instructions and images (png, jpeg) as input format.

Intended Use: The model is intended to be used in enterprise applications that involve processing visual and text data. In particular, the model is well-suited for a range of visual document understanding tasks, such as analyzing tables and charts, performing optical character recognition (OCR), and answering questions based on document content. Additionally, its capabilities extend to general image understanding, enabling it to be applied to a broader range of business applications. For tasks that exclusively involve text-based input, we suggest using our Granite large language models, which are optimized for text-only processing and offer superior performance compared to this model.

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GGUF

Model size

3B params

Architecture

granite

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Granite Quantized Models

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Quantized versions of IBM Granite models. • 50 items • Updated Jun 30 • 36

Paper for ibm-granite/granite-vision-3.3-2b-GGUF

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

Paper • 2502.09927 • Published Feb 14, 2025 • 1