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--- |
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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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language: |
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- en |
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base_model: |
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- Qwen/Qwen2-VL-7B-Instruct |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- OCR |
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- Pdf |
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- Doc |
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- Image |
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--- |
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# **coreOCR-7B-050325-preview** |
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> The **coreOCR-7B-050325-preview** model is a fine-tuned version of **Qwen/Qwen2-VL-7B**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Designed with a focus on high-fidelity visual-textual comprehension, this model enhances document parsing, structured data extraction, and complex visual reasoning. |
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# Key Enhancements |
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* **Advanced Document-Level OCR**: Accurately processes and extracts structured text from complex, multi-page documents including invoices, forms, and research papers. |
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* **Enhanced Long-Context Vision-Language Understanding**: Supports long-text retrieval and reasoning from documents and multimedia inputs, including dense text blocks, diagrams, and math content. |
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* **SoTA Understanding Across Image Resolutions**: Achieves state-of-the-art results on visual benchmarks including MathVista, DocVQA, RealWorldQA, and MTVQA. |
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* **Video Comprehension up to 20+ minutes**: Capable of high-quality video-based question answering, dialogue generation, and content summarization from long video sequences. |
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* **Device Control via Visual Commands**: With complex reasoning and perception capabilities, it can be integrated with devices like mobile phones or robots for visually grounded automation. |
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# Quick Start with Transformers |
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```python |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/coreOCR-7B-050325-preview", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/coreOCR-7B-050325-preview") |
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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** | `Qwen2VLForConditionalGeneration` | |
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| **Hardware** | 2 × NVIDIA A100 SXM (with 32 vCPUs) | |
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| **Total Disk** | 160,000 MB | |
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| **Training Time** | 10,390 seconds (~2.88 hours) | |
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| **Learning Rate** | 1e-5 | |
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| **Scheduler** | Linear Decay | |
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| **Warmup Steps** | 700 | |
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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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* Document analysis and OCR from scanned images, PDFs, and camera input. |
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* Image-based question answering (e.g., educational content, diagrams, receipts). |
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* Math problem solving and LaTeX text generation from handwritten or printed math content. |
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* Long-context vision-text applications such as multi-slide document retrieval and dense information extraction. |
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* Multilingual OCR workflows for cross-lingual business documents and global data digitization. |
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* AI agents for mobile/robotic interaction through visual context. |
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# Limitations |
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* Performance may degrade on extremely noisy or low-resolution images. |
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* Not suitable for real-time inference on edge devices due to model size and memory demands. |
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* While multilingual, performance on low-resource or rare scripts may vary. |
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* Not optimized for high-speed processing of video streams in constrained environments. |
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* Contextual understanding depends on visual tokenization parameters; improper configuration may affect output quality. |
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* Outputs may occasionally include hallucinations or incomplete answers in long-context queries. |
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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) |
