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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- trl
- spatial-reasoning
- vision-understanding
---
# **Spatial-VU**
> The **Spatial-VU** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, tailored for **Spatial Reasoning and Vision Understanding**. This variant is designed to generate highly detailed and descriptive captions across a broad range of visual categories, including images with complex, sensitive, or nuanced content—across varying aspect ratios and resolutions.
# Key Highlights
* **Spatial Reasoning & Vision Understanding**: Fine-tuned to provide accurate and descriptive visual interpretations, enabling deeper understanding of spatial relationships, structures, and context.
* **High-Fidelity Descriptions**: Generates comprehensive captions for general, artistic, technical, abstract, and low-context images.
* **Robust Across Aspect Ratios**: Capable of accurately captioning images with wide, tall, square, and irregular dimensions.
* **Variational Detail Control**: Produces outputs with both high-level summaries and fine-grained descriptions as needed.
* **Foundation on Qwen2.5-VL Architecture**: Leverages the strengths of the Qwen2.5-VL-7B multimodal model for visual reasoning, comprehension, and instruction-following.
* **Multilingual Output Capability**: Can support multilingual descriptions (English as default), adaptable via prompt engineering.
# Quick Start with Transformers
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"prithivMLmods/Spatial-VU", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("prithivMLmods/Spatial-VU")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image in detail."},
],
}
]
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")
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)
```
# Intended Use
* Generating detailed and unfiltered image captions for general-purpose or artistic datasets.
* Spatial reasoning and vision understanding research.
* Content moderation research, red-teaming, and generative safety evaluations.
* Enabling descriptive captioning for visual datasets typically excluded from mainstream models.
* Use in creative applications (e.g., storytelling, art generation) that benefit from rich descriptive captions.
* Captioning for non-standard aspect ratios and stylized visual content.
# Limitations
* May produce explicit, sensitive, or offensive descriptions depending on image content and prompts.
* Not suitable for deployment in production systems requiring content filtering or moderation.
* Can exhibit variability in caption tone or style depending on input prompt phrasing.
* Accuracy for unfamiliar or synthetic visual styles may vary. |