Instructions to use inclusionAI/ZwZ-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/ZwZ-2B-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="inclusionAI/ZwZ-2B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("inclusionAI/ZwZ-2B-GGUF", dtype="auto")

llama-cpp-python

How to use inclusionAI/ZwZ-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="inclusionAI/ZwZ-2B-GGUF",
	filename="ZwZ-2B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use inclusionAI/ZwZ-2B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf inclusionAI/ZwZ-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf inclusionAI/ZwZ-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf inclusionAI/ZwZ-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf inclusionAI/ZwZ-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 inclusionAI/ZwZ-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf inclusionAI/ZwZ-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 inclusionAI/ZwZ-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf inclusionAI/ZwZ-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/inclusionAI/ZwZ-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use inclusionAI/ZwZ-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/ZwZ-2B-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": "inclusionAI/ZwZ-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/ZwZ-2B-GGUF:Q4_K_M

SGLang

How to use inclusionAI/ZwZ-2B-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 "inclusionAI/ZwZ-2B-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": "inclusionAI/ZwZ-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "inclusionAI/ZwZ-2B-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": "inclusionAI/ZwZ-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use inclusionAI/ZwZ-2B-GGUF with Ollama:
```
ollama run hf.co/inclusionAI/ZwZ-2B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use inclusionAI/ZwZ-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 inclusionAI/ZwZ-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 inclusionAI/ZwZ-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 inclusionAI/ZwZ-2B-GGUF to start chatting

Pi new

How to use inclusionAI/ZwZ-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf inclusionAI/ZwZ-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": "inclusionAI/ZwZ-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use inclusionAI/ZwZ-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf inclusionAI/ZwZ-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 inclusionAI/ZwZ-2B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use inclusionAI/ZwZ-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/inclusionAI/ZwZ-2B-GGUF:Q4_K_M
```

Lemonade

How to use inclusionAI/ZwZ-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull inclusionAI/ZwZ-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ZwZ-2B-GGUF-Q4_K_M

List all available models

lemonade list

ZwZ-2B-GGUF

This repository provides GGUF-format weights for ZwZ-2B, split into two components:

Language model (LLM): FP16, Q8_0, Q4_K_M
Vision encoder (mmproj): FP16, Q8_0, Q4_K_M

These files are compatible with llama.cpp, Ollama, and other GGUF-based tools, supporting inference on CPU, NVIDIA GPU (CUDA), Apple Silicon (Metal), Intel GPUs (SYCL), and more. You can mix precision levels for the language and vision components based on your hardware and performance needs, and even perform custom quantization starting from the FP16 weights.

Enjoy running this multimodal model on your personal device! 🚀

How to Use

To use these models with llama.cpp, please ensure you are using the latest version—either by building from source or downloading the most recent release according to the devices.

You can run inference via the command line or through a web-based chat interface.

CLI Inference (`llama-mtmd-cli`)

For example, to run ZwZ-2B with an Q8_0 vision encoder and Q8_0 quantized LLM:

llama-mtmd-cli \
  -m path/to/ZwZ-2B-Q8_0.gguf \
  --mmproj mmproj-ZwZ-2B-Q8_0.gguf\
  --image test.jpeg \
  -p "What is the publisher name of the newspaper?" \
  --temp 1.0 --top-k 20 --top-p 0.95 -n 1024

Web Chat (using `llama-server`)

To serve ZwZ-2B via an OpenAI-compatible API with a web UI:

llama-server \
  -m path/to/ZwZ-2B-Q8_0.gguf  \
  --mmproj mmproj-ZwZ-2B-Q8_0.gguf

Citation

@article{wei2026zooming,
  title={Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception},
  author={Wei, Lai and He, Liangbo and Lan, Jun and Dong, Lingzhong and Cai, Yutong and Li, Siyuan and Zhu, Huijia and Wang, Weiqiang and Kong, Linghe and Wang, Yue and Zhang, Zhuosheng and Huang, Weiran},
  journal={arXiv preprint arXiv:2602.11858},
  year={2026}
}

License

This model follows the license of Apache 2.0 License.

Downloads last month: 204

GGUF

Model size

2B params

Architecture

qwen3vl

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for inclusionAI/ZwZ-2B-GGUF

Base model

Qwen/Qwen3-VL-2B-Instruct

Finetuned

inclusionAI/ZwZ-2B

Quantized

(1)

this model

Datasets used to train inclusionAI/ZwZ-2B-GGUF

Collection including inclusionAI/ZwZ-2B-GGUF

Zooming-without-Zooming

Collection

9 items • Updated Mar 25 • 8

Paper for inclusionAI/ZwZ-2B-GGUF

Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Paper • 2602.11858 • Published Feb 12 • 63