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---
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
---
# **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. |