143 lines
6.1 KiB
Markdown
143 lines
6.1 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- allenai/olmOCR-mix-0225
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- prithivMLmods/Opendoc1-Analysis-Recognition
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- prithivMLmods/Opendoc2-Analysis-Recognition
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- prithivMLmods/Openpdf-Analysis-Recognition
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pipeline_tag: image-text-to-text
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tags:
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- OCR
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- Pdf
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- Doc
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- Image
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- text-generation-inference
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- KIE-Key Information Extraction
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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library_name: transformers
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---
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# **docscopeOCR-7B-050425-exp**
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> The **docscopeOCR-7B-050425-exp** model is a fine-tuned version of **Qwen/Qwen2.5-VL-7B-Instruct**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats.
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# Key Enhancements
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* **Advanced Document-Level OCR**: Capable of extracting structured content from complex, multi-page documents such as invoices, academic papers, forms, and scanned reports.
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* **Enhanced Long-Context Vision-Language Understanding**: Designed to handle dense document layouts, long sequences of embedded text, tables, and diagrams with coherent cross-reference understanding.
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* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.
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* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning.
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* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic.
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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/docscopeOCR-7B-050425-exp", torch_dtype="auto", device_map="auto"
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)
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processor = AutoProcessor.from_pretrained("prithivMLmods/docscopeOCR-7B-050425-exp")
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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."},
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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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## Training Details
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| Parameter | Value |
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|-------------------------|-----------------------------------------------------|
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| **Dataset Size** | 274,209 samples (Modular Combination of Datasets) |
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| **Model Architecture** | `Qwen2_5_VLForConditionalGeneration` |
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| **Hardware** | 2 × NVIDIA A100 SXM (32 vCPUs) |
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| **Total Disk** | 170,000 MB |
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| **Training Time** | 9,020 seconds (~2.51 hours) |
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| **Learning Rate** | 1e-5 |
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| **Scheduler** | Linear Decay |
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| **Warmup Steps** | 750 |
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| **Precision** | bfloat16 |
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> [!note]
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> The open dataset image-text response will be updated soon.
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# Intended Use
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This model is intended for:
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* High-fidelity OCR from documents, forms, receipts, and printed or scanned materials.
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* Image and document-based question answering for educational and enterprise applications.
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* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content.
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* Retrieval and summarization from long documents, slides, and multi-modal inputs.
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* Multilingual OCR and structured content extraction for global use cases.
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* Robotic or mobile automation with vision-guided contextual interaction.
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# Limitations
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* May show degraded performance on extremely low-quality or occluded 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 uncommon or low-resource languages/scripts.
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* Long video processing may require substantial memory and is not optimized for streaming applications.
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* Visual token settings affect performance; suboptimal configurations can impact results.
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* In rare cases, outputs may contain hallucinated or contextually misaligned information.
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## References
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- **DocVLM: Make Your VLM an Efficient Reader**
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[https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)
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- **YaRN: Efficient Context Window Extension of Large Language Models**
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[https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
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- **Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution**
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[https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)
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- **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**
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[https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)
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- **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
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[https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210) |