Instructions to use inclusionAI/ZwZ2-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/ZwZ2-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="inclusionAI/ZwZ2-27B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("inclusionAI/ZwZ2-27B")
model = AutoModelForMultimodalLM.from_pretrained("inclusionAI/ZwZ2-27B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/ZwZ2-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/ZwZ2-27B"
# 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/ZwZ2-27B",
		"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/ZwZ2-27B

SGLang

How to use inclusionAI/ZwZ2-27B 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/ZwZ2-27B" \
    --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/ZwZ2-27B",
		"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/ZwZ2-27B" \
        --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/ZwZ2-27B",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/ZwZ2-27B with Docker Model Runner:
```
docker model run hf.co/inclusionAI/ZwZ2-27B
```

zenithli commited on 3 days ago

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@@ -32,11 +32,13 @@ Although trained on single images, **ZwZ2-27B** improves over **Qwen3.6-27B** on
 Individual benchmark scores are accuracies (%) from the second-generation manuscript; domain means and overall scores follow the latest 25-benchmark results table; Δ denotes absolute percentage-point improvement over the corresponding backbone. Domain averages are unweighted within each domain. Overall follows the latest 25-benchmark results table; it is not a mean of the six domain scores.
-### Overall Performance
-![Overall performance of the Zooming-without-Zooming-v2 family on 25 benchmarks](assets/zwz2_family_firstgen_style.png)
-Mean accuracy over the same 25 benchmarks across six domains in the latest Table 1. ZwZ2-4B / 9B / 27B score 54.79 / 56.59 / 60.51, respectively. The chart's vertical axis starts at 40%.
 <!-- RESULTS_IMAGE_START -->
 ### Benchmark Results

 Individual benchmark scores are accuracies (%) from the second-generation manuscript; domain means and overall scores follow the latest 25-benchmark results table; Δ denotes absolute percentage-point improvement over the corresponding backbone. Domain averages are unweighted within each domain. Overall follows the latest 25-benchmark results table; it is not a mean of the six domain scores.
+### Performance Highlights
+![ZwZ2-4B, ZwZ2-9B and ZwZ2-27B on eight representative benchmarks](assets/zwz2_performance_highlights.png)
+Accuracy (%) on eight representative benchmarks: ZoomBench, Perception-Bench, MMSI-Bench, SPAR-Bench, MileBench, ERQA, ViewSpatial-Bench, and MMStar. Full 25-benchmark results are below; overall scores for ZwZ2-4B / 9B / 27B are 54.79 / 56.59 / 60.51.
+[View vector PDF](assets/zwz2_performance_highlights_editable.pdf)
 <!-- RESULTS_IMAGE_START -->
 ### Benchmark Results