---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---

# Visual Spatial Tuning: VST-7B-SFT

<p align="left">
  <a href="https://yangr116.github.io/vst_project/">
    <img
      src="https://img.shields.io/badge/VST-Project-0A66C2?logo=safari&logoColor=white" style="display: inline-block; vertical-align: middle;"
      alt="VST Project"
    />
  </a>
  <a href="https://arxiv.org/abs/2511.05491">
    <img
      src="https://img.shields.io/badge/VST-Paper-red?logo=arxiv&logoColor=red" style="display: inline-block; vertical-align: middle;"
      alt="VST Paper on arXiv"
    />
  </a>
  <a href="https://github.com/Yangr116/VST" target="_blank" style="margin: 2px;">
      <img 
        alt="Github" src="https://img.shields.io/badge/VST-Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;"
        alt="VST Codebase"
      />
  </a>
</p>

This model is described in the paper [Visual Spatial Tuning](https://huggingface.co/papers/2511.05491).

TL;DR: VST is a comprehensive framework designed to cultivate Vision-Language Models (VLMs) with human-like visuospatial abilities—from spatial perception to advanced reasoning.


## 💡 Key Highlights

✨ **VST-P**: 4.1M samples across 19 skills, spanning single images, multi-image scenarios, and videos—boosting spatial perception in VLMs.  
✨ **VST-R**: 135K curated samples that teach models to reason in space, including step-by-step reasoning and rule-based data for reinforcement learning.  
✨ **Progressive Training Pipeline**: Start with supervised fine-tuning to build foundational spatial knowledge, then reinforce spatial reasoning abilities via RL. VST achieves state-of-the-art results on spatial benchmarks (34.8% on MMSI-Bench, 61.2% on VSIBench) without compromising general capabilities.  
✨ **Vision-Language-Action Models Enhanced**: The VST paradigm significantly strengthens spatial tuning, paving the way for more physically grounded AI.



### 📈  Spatial & General Benchmarks

| Models              | CV   | 3DSR | MMSI | BLINK | VSI  | MMStar | MMB  | RealworldQA | MMMU | OCRB | AI2D |
|---------------------|------|------|------|-------|------|--------|------|-------------|------|------|------|
| VST-3B-SFT   | 84.4 | 54.1 | 30.2 | 59.1  | 57.9 | 58.0   | 80.9 | 68.4        | 45.2 | 83.7 | 82.5 |
| VST-3B-RL   | 84.2 | 56.5 | 31.3 | 57.2  | 57.7 | 58.9   | 80.5 | 68.5        | 49.8 | 80.9 | 82.4 |
| VST-7B-SFT   | 85.5 | 54.6 | 32.0 | 62.1  | 60.6 | 63.1   | 83.3 | 72.2        | 50.6 | 85.5 | 84.9 |
| VST-7B-RL| 86.5 | 60.1 | 34.8 | 62.6 | 61.2 | 63.5 | 83.0 | 68.5 | 49.4 | 86.1 | 83.5 |

### 📈  VSIBench

| Methods               | Avg. | Obj. Count | Abs. Dist. | Obj. Size | Room Size | Rel. Dist | Rel. Dir. | Route Plan | Appr. Order |
|-----------------------|------|------------|------------|-----------|-----------|-----------|-----------|------------|-------------|
| VST-3B-SFT      | 57.9 | 69.3       | 45.4       | 71.8      | 62.4      | 59.0      | 46.0      | 38.7       | 70.2    |
| VST-3B-RL      | 57.7 | 66.6       | 45.0       | 72.8      | 60.9      | 59.9      | 47.6      | 40.7       | 68.3        |
| VST-7B-SFT      | 60.6 | 72.0   | 44.4       | 74.3      | 68.3      | 59.7      | 55.8      | 44.9       | 65.2        |
| VST-7B-RL      | 61.2 | 71.6   | 43.8       | 75.5  | 69.2  | 60.0      | 55.6      | 44.3       | 69.2        |


## Sample Usage

To get started, install the necessary libraries:

```bash
pip install transformers
# It's highly recommanded to use `[decord]` feature for faster video loading.
pip install qwen-vl-utils
```

### Using 🤗  Transformers to Chat

Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:

```python
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model_path="rayruiyang/VST-7B-SFT"

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

# default processer
processor = AutoProcessor.from_pretrained(model_path, min_pixels = 256*28*28, max_pixels=1280*28*28)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "http://images.cocodataset.org/train2017/000000039685.jpg",
            },
            {"type": "text", "text": "Consider the real-world 3D locations of the objects. Is the flag directly underneath the airplane?"},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
```


## Citation

If you find our work helpful, feel free to give us a cite.

```
@article{vst,
  title={Visual Spatial Tuning},
  author={Rui Yang, Ziyu Zhu, Yanwei Li, Jingjia Huang, Shen Yan, Siyuan Zhou, Zhe Liu, Xiangtai Li, Shuangye Li, Wenqian Wang, Yi Lin, Hengshuang Zhao},
  journal={arXiv preprint arXiv:2511.05491},
  year={2025}
}
```