Coreocr 7B 050325 Preview

🚀 coreOCR-7B-050325-preview

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.

✨ Features

• Advanced Document - Level OCR: Accurately processes and extracts structured text from complex, multi - page documents including invoices, forms, and research papers.
• 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.
• SoTA Understanding Across Image Resolutions: Achieves state - of - the - art results on visual benchmarks including MathVista, DocVQA, RealWorldQA, and MTVQA.
• Video Comprehension up to 20+ minutes: Capable of high - quality video - based question answering, dialogue generation, and content summarization from long video sequences.
• 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.

🚀 Quick Start

Quick Start with Transformers

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/coreOCR-7B-050325-preview", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/coreOCR-7B-050325-preview")

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."},
        ],
    }
]

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)

🔧 Technical Details

Training Details

⚠️ Important Note

The open dataset image - text response will be updated soon.

📚 Documentation

Intended Use

This model is intended for:

• Document analysis and OCR from scanned images, PDFs, and camera input.
• Image - based question answering (e.g., educational content, diagrams, receipts).
• Math problem solving and LaTeX text generation from handwritten or printed math content.
• Long - context vision - text applications such as multi - slide document retrieval and dense information extraction.
• Multilingual OCR workflows for cross - lingual business documents and global data digitization.
• AI agents for mobile/robotic interaction through visual context.

Limitations

• Performance may degrade on extremely noisy or low - resolution images.
• Not suitable for real - time inference on edge devices due to model size and memory demands.
• While multilingual, performance on low - resource or rare scripts may vary.
• Not optimized for high - speed processing of video streams in constrained environments.
• Contextual understanding depends on visual tokenization parameters; improper configuration may affect output quality.
• Outputs may occasionally include hallucinations or incomplete answers in long - context queries.

References

• DocVLM: Make Your VLM an Efficient Reader https://arxiv.org/pdf/2412.08746v1
• YaRN: Efficient Context Window Extension of Large Language Models
  https://arxiv.org/pdf/2309.00071
• Qwen2 - VL: Enhancing Vision - Language Model’s Perception of the World at Any Resolution
  https://arxiv.org/pdf/2409.12191
• Qwen - VL: A Versatile Vision - Language Model for Understanding, Localization, Text Reading, and Beyond
  https://arxiv.org/pdf/2308.12966
• A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy https://arxiv.org/pdf/2412.02210

📄 License

This project is licensed under the Apache - 2.0 license.