---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
pipeline_tag: image-text-to-text
---
# SpatialLadder-3B
This repository contains the SpatialLadder-3B, introduced in [SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models]().
## Model Description
SpatialLadder-3B is a 3B-parameter multimodal model for spatial reasoning, built on top of Qwen-2.5-VL-3B. It is trained with a progressive three-stage approach: object localization for perceptual grounding, multi-dimensional spatial tasks for spatial understanding, and policy-optimized complex reasoning for advanced spatial intelligence. SpatialLadder-3B achieves strong performance and generalization across multiple spatial benchmarks, demonstrating robust hierarchical spatial reasoning capabilities.
## Usage
First, install the required dependencies:
```python
pip install transformers==4.49.0 qwen-vl-utils
```
```
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# 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(
"hongxingli/SpatialLadder-3B",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto")
processor = AutoProcessor.from_pretrained("hongxingli/SpatialLadder-3B")
image_path = ''
instruction = ''
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "image_path",
},
{"type": "text", "text": instruction},
],
}
]
# 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(model.device)
# 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)
```
## Training
The training code and usage guidelines are available in our [GitHub repository](https://github.com/ZJU-REAL/SpatialLadder). For comprehensive details, please refer to our paper and the repository documentation.
## Citation
```bibtext
@misc{li2025spatialladderprogressivetrainingspatial,
title={SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models},
author={Hongxing Li and Dingming Li and Zixuan Wang and Yuchen Yan and Hang Wu and Wenqi Zhang and Yongliang Shen and Weiming Lu and Jun Xiao and Yueting Zhuang},
year={2025},
eprint={2510.08531},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.08531},
}
```