129 lines
5.1 KiB
Markdown
129 lines
5.1 KiB
Markdown
---
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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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- trl
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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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- math
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- stem
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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. |