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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- image-captioning |
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- optical-character-recognition |
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- intelligent-character-recognition |
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- caption |
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- ocr |
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- visual-understanding |
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- art |
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- icr |
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- image-to-text |
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- vlm |
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base_model: |
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- prithivMLmods/VIREX-062225-exp |
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--- |
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# **WR30a-Deep-7B-0711** |
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> 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. |
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# Key Enhancements |
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* **Superior Image Captioning**: Advanced capability for generating detailed, contextually accurate captions for diverse image types and content. |
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* **Enhanced Visual Analysis**: Designed to efficiently analyze and interpret complex visual information across different image categories and formats. |
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* **Advanced Image Reasoning**: Optimized for logical reasoning about visual content, understanding relationships, and making inferences from images. |
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* **Multi-Category Image Support**: Specialized in handling all categories of images with variational dimensions, from simple objects to complex scenes. |
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* **State-of-the-Art Performance**: Achieves competitive results on visual understanding benchmarks and real-world image analysis tasks. |
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* **Dimensional Flexibility**: Supports images of various resolutions and aspect ratios for comprehensive visual processing. |
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* **Cross-Domain Visual Understanding**: Enables robust performance across different visual domains and content types. |
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# Quick Start with Transformers |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/WR30a-Deep-7B-0711", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/WR30a-Deep-7B-0711") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Describe this image in detail."}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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# Intended Use |
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This model is intended for: |
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* High-quality image captioning across diverse visual content and categories. |
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* Comprehensive visual analysis and interpretation of complex imagery. |
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* Advanced image reasoning for educational, research, and commercial applications. |
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* Multi-dimensional image understanding regardless of resolution or aspect ratio. |
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* Visual question answering and image-based dialogue systems. |
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* Content moderation and automated image classification tasks. |
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* Creative applications requiring detailed visual understanding. |
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* Accessibility tools for image description and visual assistance. |
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## Training Details |
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| Parameter | Value | |
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|-------------------------|-----------------------------------------------------| |
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| **Dataset Size** | 1,500K image pairs | |
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| **Model Architecture** | `Qwen2_5_VLForConditionalGeneration` | |
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| **Total Disk Volume** | 400,000 MB | |
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| **Training Time** | approx. 9,612 seconds (~2.67 hours) | |
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| **Model Stage** | Experimental | |
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| **Hardware** | 2 × NVIDIA A40 (19 vCPUs) | |
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| **Precision** | bfloat16 | |
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# Limitations |
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* May show degraded performance on extremely low-quality or heavily corrupted images. |
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* Not optimized for real-time applications on low-resource or edge devices due to computational demands. |
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* Variable accuracy on highly specialized or domain-specific visual content. |
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* Performance may vary with unusual image compositions or artistic styles. |
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* Being in experimental stage, outputs should be validated for critical applications. |
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* May require fine-tuning for specific niche use cases or domains. |
