Instructions to use inclusionAI/ZwZ2-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/ZwZ2-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="inclusionAI/ZwZ2-9B")
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/ZwZ2-9B")
model = AutoModelForMultimodalLM.from_pretrained("inclusionAI/ZwZ2-9B", 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/ZwZ2-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/ZwZ2-9B"
# 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/ZwZ2-9B",
		"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/ZwZ2-9B

SGLang

How to use inclusionAI/ZwZ2-9B 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/ZwZ2-9B" \
    --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/ZwZ2-9B",
		"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/ZwZ2-9B" \
        --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/ZwZ2-9B",
		"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/ZwZ2-9B with Docker Model Runner:
```
docker model run hf.co/inclusionAI/ZwZ2-9B
```

ZwZ2-9B / README.md

WaltonFuture

Update README.md

84ca0ba verified about 22 hours ago

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---
library_name: transformers
pipeline_tag: image-text-to-text
datasets:
- inclusionAI/ZwZ2-RL-VQA
base_model: Qwen/Qwen3.5-9B
language:
- en
tags:
- zwz2
- vision-language
- visual-reasoning
- reinforcement-learning
- grpo
---
# ZwZ2-9B

<!-- Replace PAPER_URL with the final v2 paper URL. -->
<p align="center">
  📃 <a href="https://arxiv.org/pdf/2610.37037">Paper</a> | 🏠 <a href="https://github.com/inclusionAI/Zooming-without-Zooming-v2">Project</a> | 🤗 <a href="https://huggingface.co/collections/inclusionAI/zooming-without-zooming-v2">Collection</a>
</p>
## ✨ Model Summary
**ZwZ2-9B** is the **Qwen3.5-9B**-based model in **Zooming-without-Zooming-v2 (ZwZ2)**. It learns to connect visual evidence across regions through comparison, aggregation, matching, and multi-step reasoning. Training uses full images; inference requires no zooming tools.
On **25 benchmarks across six domains**, it improves over its base model on every benchmark, increasing overall accuracy from **51.63 to 56.59 (+4.96 points)**. Gains extend from fine-grained perception to spatial reasoning, long-context understanding, multimodal agents, and general visual reasoning.
## ⚙️ Method and Data
**EvidenceConnect** builds verified multi-region questions from SA-1B images:
1. Group 3–7 related regions and generate questions using privileged zoomed views.
2. Check answers through cross-model agreement and drop-one region tests.
3. Convert verified questions to four-option tasks and train with **GRPO**, without an additional supervised fine-tuning stage.
All three model sizes use approximately **20K training examples**. The policy sees full images with colored region outlines; privileged crop layouts are used only for data construction. See [ZwZ2-RL-VQA](https://huggingface.co/datasets/inclusionAI/ZwZ2-RL-VQA) for the data schema and synthesis process.
## 📊 Performance
![ZwZ2 performance on eight representative benchmarks](assets/zwz2_performance_highlights.png)
[Editable vector PDF](assets/zwz2_performance_highlights_editable.pdf). The figure shows eight representative benchmarks; the complete results follow.
![All 25 benchmark results](assets/zwz2_9b_results.png)
[Full-resolution results](assets/zwz2_9b_results.png). Scores are accuracies (%); domain and overall averages weight the corresponding benchmarks equally. **MMLongBench\*** uses the visual core: V-RAG, Image MM-NIAH, and VH-Multi, comprising **25 non-Counting tasks**. Advanced-model image resolution is constrained by their serving platforms.
## 🚀 Quickstart
### Installation
Install a PyTorch build compatible with your hardware, then:
```bash
pip install "transformers==5.13.1" accelerate pillow huggingface_hub
```
### Inference
```python
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "inclusionAI/ZwZ2-9B"
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id)
processor.chat_template = Path(
    hf_hub_download(model_id, "chat_template.jinja")
).read_text(encoding="utf-8")
messages = [{"role": "user", "content": [
    {"type": "image", "url": "/path/to/image.jpg"},
    {"type": "text", "text": "Describe this image."},
]}]
inputs = processor.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_dict=True, return_tensors="pt", enable_thinking=False,
).to(model.device)
with torch.inference_mode():
    generated_ids = model.generate(
        **inputs, max_new_tokens=2048, do_sample=False
    )
output_text = processor.batch_decode(
    generated_ids[:, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True, clean_up_tokenization_spaces=False,
)[0]
print(output_text)
```
Replace `/path/to/image.jpg` with an existing image. All three sizes use `Qwen3_5ForConditionalGeneration`, including the Qwen3.6-27B-based model. The example preserves the released chat template and uses **non-thinking mode**. The template requests final answers inside `<answer>...</answer>`; a custom system message replaces that default instruction.

## 🤗 Models and Data

| Model | Base model |
|---|---|
| [ZwZ2-4B](https://huggingface.co/inclusionAI/ZwZ2-4B) | Qwen3.5-4B |
| [ZwZ2-9B](https://huggingface.co/inclusionAI/ZwZ2-9B) | Qwen3.5-9B |
| [ZwZ2-27B](https://huggingface.co/inclusionAI/ZwZ2-27B) | Qwen3.6-27B |

[🤗 ZwZ2-RL-VQA training dataset](https://huggingface.co/datasets/inclusionAI/ZwZ2-RL-VQA)

## 📄 Limitations and License

The model can hallucinate details, misread text, and make counting or spatial errors. Performance depends on image resolution, context length, and prompt format. Refer to the license accompanying the released weights and the applicable backbone license for usage terms.

**First-generation ZwZ:** [Paper](https://huggingface.co/papers/2602.11858) · [Code](https://github.com/inclusionAI/Zooming-without-Zooming).

	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	datasets:
	- inclusionAI/ZwZ2-RL-VQA
	base_model: Qwen/Qwen3.5-9B
	language:
	- en
	tags:
	- zwz2
	- vision-language
	- visual-reasoning
	- reinforcement-learning
	- grpo
	---

	# ZwZ2-9B

	<!-- Replace PAPER_URL with the final v2 paper URL. -->
	<p align="center">
	📃 <a href="https://arxiv.org/pdf/2610.37037">Paper</a> \| 🏠 <a href="https://github.com/inclusionAI/Zooming-without-Zooming-v2">Project</a> \| 🤗 <a href="https://huggingface.co/collections/inclusionAI/zooming-without-zooming-v2">Collection</a>
	</p>

	## ✨ Model Summary

	ZwZ2-9B is the Qwen3.5-9B-based model in Zooming-without-Zooming-v2 (ZwZ2). It learns to connect visual evidence across regions through comparison, aggregation, matching, and multi-step reasoning. Training uses full images; inference requires no zooming tools.

	On 25 benchmarks across six domains, it improves over its base model on every benchmark, increasing overall accuracy from 51.63 to 56.59 (+4.96 points). Gains extend from fine-grained perception to spatial reasoning, long-context understanding, multimodal agents, and general visual reasoning.

	## ⚙️ Method and Data

	EvidenceConnect builds verified multi-region questions from SA-1B images:

	1. Group 3–7 related regions and generate questions using privileged zoomed views.
	2. Check answers through cross-model agreement and drop-one region tests.
	3. Convert verified questions to four-option tasks and train with GRPO, without an additional supervised fine-tuning stage.

	All three model sizes use approximately 20K training examples. The policy sees full images with colored region outlines; privileged crop layouts are used only for data construction. See [ZwZ2-RL-VQA](https://huggingface.co/datasets/inclusionAI/ZwZ2-RL-VQA) for the data schema and synthesis process.

	## 📊 Performance

	![ZwZ2 performance on eight representative benchmarks](assets/zwz2_performance_highlights.png)

	[Editable vector PDF](assets/zwz2_performance_highlights_editable.pdf). The figure shows eight representative benchmarks; the complete results follow.

	![All 25 benchmark results](assets/zwz2_9b_results.png)

	[Full-resolution results](assets/zwz2_9b_results.png). Scores are accuracies (%); domain and overall averages weight the corresponding benchmarks equally. MMLongBench\* uses the visual core: V-RAG, Image MM-NIAH, and VH-Multi, comprising 25 non-Counting tasks. Advanced-model image resolution is constrained by their serving platforms.

	## 🚀 Quickstart

	### Installation

	Install a PyTorch build compatible with your hardware, then:

	```bash
	pip install "transformers==5.13.1" accelerate pillow huggingface_hub
	```

	### Inference

	```python
	from pathlib import Path
	import torch
	from huggingface_hub import hf_hub_download
	from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

	model_id = "inclusionAI/ZwZ2-9B"
	model = Qwen3_5ForConditionalGeneration.from_pretrained(
	model_id, dtype=torch.bfloat16, device_map="auto"
	).eval()
	processor = AutoProcessor.from_pretrained(model_id)
	processor.chat_template = Path(
	hf_hub_download(model_id, "chat_template.jinja")
	).read_text(encoding="utf-8")

	messages = [{"role": "user", "content": [
	{"type": "image", "url": "/path/to/image.jpg"},
	{"type": "text", "text": "Describe this image."},
	]}]
	inputs = processor.apply_chat_template(
	messages, tokenize=True, add_generation_prompt=True,
	return_dict=True, return_tensors="pt", enable_thinking=False,
	).to(model.device)

	with torch.inference_mode():
	generated_ids = model.generate(
	**inputs, max_new_tokens=2048, do_sample=False
	)
	output_text = processor.batch_decode(
	generated_ids[:, inputs["input_ids"].shape[1]:],
	skip_special_tokens=True, clean_up_tokenization_spaces=False,
	)[0]
	print(output_text)
	```

	Replace `/path/to/image.jpg` with an existing image. All three sizes use `Qwen3_5ForConditionalGeneration`, including the Qwen3.6-27B-based model. The example preserves the released chat template and uses non-thinking mode. The template requests final answers inside `<answer>...</answer>`; a custom system message replaces that default instruction.

	## 🤗 Models and Data

	\| Model \| Base model \|
	\|---\|---\|
	\| [ZwZ2-4B](https://huggingface.co/inclusionAI/ZwZ2-4B) \| Qwen3.5-4B \|
	\| [ZwZ2-9B](https://huggingface.co/inclusionAI/ZwZ2-9B) \| Qwen3.5-9B \|
	\| [ZwZ2-27B](https://huggingface.co/inclusionAI/ZwZ2-27B) \| Qwen3.6-27B \|

	[🤗 ZwZ2-RL-VQA training dataset](https://huggingface.co/datasets/inclusionAI/ZwZ2-RL-VQA)

	## 📄 Limitations and License

	The model can hallucinate details, misread text, and make counting or spatial errors. Performance depends on image resolution, context length, and prompt format. Refer to the license accompanying the released weights and the applicable backbone license for usage terms.

	First-generation ZwZ: [Paper](https://huggingface.co/papers/2602.11858) · [Code](https://github.com/inclusionAI/Zooming-without-Zooming).