---
license: apache-2.0
language:
- en
- zh
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- image-captioning
- optical-character-recognition
- intelligent-character-recognition
- caption
- ocr
- visual-understanding
- art
- icr
- image-to-text
- vlm
base_model:
- prithivMLmods/VIREX-062225-exp
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/YBTwin1Fqn2NUBP5oKos2.png)

# **WR30a-Deep-7B-0711**

> The **WR30a-Deep-7B-0711** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, optimized for **Image Captioning**, **Visual Analysis**, and **Image Reasoning**. Built on top of the Qwen2.5-VL architecture, this experimental model enhances visual comprehension capabilities with focused training on 1,500K image pairs for superior image understanding and reasoning tasks across all categories of images with variational dimensions.

# Key Enhancements

* **Superior Image Captioning**: Advanced capability for generating detailed, contextually accurate captions for diverse image types and content.

* **Enhanced Visual Analysis**: Designed to efficiently analyze and interpret complex visual information across different image categories and formats.

* **Advanced Image Reasoning**: Optimized for logical reasoning about visual content, understanding relationships, and making inferences from images.

* **Multi-Category Image Support**: Specialized in handling all categories of images with variational dimensions, from simple objects to complex scenes.

* **State-of-the-Art Performance**: Achieves competitive results on visual understanding benchmarks and real-world image analysis tasks.

* **Dimensional Flexibility**: Supports images of various resolutions and aspect ratios for comprehensive visual processing.

* **Cross-Domain Visual Understanding**: Enables robust performance across different visual domains and content types.

# 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/WR30a-Deep-7B-0711", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/WR30a-Deep-7B-0711")

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

This model is intended for:

* High-quality image captioning across diverse visual content and categories.
* Comprehensive visual analysis and interpretation of complex imagery.
* Advanced image reasoning for educational, research, and commercial applications.
* Multi-dimensional image understanding regardless of resolution or aspect ratio.
* Visual question answering and image-based dialogue systems.
* Content moderation and automated image classification tasks.
* Creative applications requiring detailed visual understanding.
* Accessibility tools for image description and visual assistance.

## Training Details

| Parameter               | Value                                               |
|-------------------------|-----------------------------------------------------|
| **Dataset Size**        | 1,500K image pairs                                  |
| **Model Architecture**  | `Qwen2_5_VLForConditionalGeneration`                |
| **Total Disk Volume**   | 400,000 MB                                          |
| **Training Time**       | approx. 9,612 seconds (~2.67 hours)                |
| **Model Stage**         | Experimental                                        |
| **Hardware**            | 2 × NVIDIA A40 (19 vCPUs)                          |
| **Precision**           | bfloat16                                            |

# Limitations

* May show degraded performance on extremely low-quality or heavily corrupted images.
* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
* Variable accuracy on highly specialized or domain-specific visual content.
* Performance may vary with unusual image compositions or artistic styles.
* Being in experimental stage, outputs should be validated for critical applications.
* May require fine-tuning for specific niche use cases or domains.