README.md

---
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- Document
- KIE
- OCR
- VL
- Openpdf
- Camel
- text-generation-inference
- Extraction
- Linking
- Markdown
- .Md
- OpenPDF
- OCRmix
- trl
datasets:
- prithivMLmods/OpenDoc-Pdf-Preview
- prithivMLmods/Opendoc1-Analysis-Recognition
- allenai/olmOCR-mix-0225
- prithivMLmods/Openpdf-Analysis-Recognition
license: apache-2.0
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CZM7u91ww9SJPFQiY7YlI.png)

# **Camel-Doc-OCR-080125(v2-preview)**

> The **Camel-Doc-OCR-080125** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, optimized for **Document Retrieval**, **Content Extraction**, and **Analysis Recognition**. Built on top of the Qwen2.5-VL architecture, this model enhances document comprehension capabilities with focused training on the Opendoc2-Analysis-Recognition dataset for superior document analysis and information extraction tasks.

## Key Enhancements

* **Context-Aware Multimodal Extraction and Linking for Documents**: Advanced capability for understanding document context and establishing connections between multimodal elements within documents.

* **Enhanced Document Retrieval**: Designed to efficiently locate and extract relevant information from complex document structures and layouts.

* **Superior Content Extraction**: Optimized for precise extraction of structured and unstructured content from diverse document formats.

* **Analysis Recognition**: Specialized in recognizing and interpreting analytical content, charts, tables, and visual data representations.

* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.

* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, question answering, and multi-modal reasoning.

* **Visually-Grounded Device Interaction**: Enables mobile or robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic.

## Quick Start with Transformers

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Camel-Doc-OCR-080125", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Camel-Doc-OCR-080125")

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)
```

## Intended Use

This model is intended for:

* Context-aware multimodal extraction and linking for complex document structures.
* High-fidelity document retrieval and content extraction from various document formats.
* Analysis recognition of charts, graphs, tables, and visual data representations.
* Document-based question answering for educational and enterprise applications.
* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content.
* Retrieval and summarization from long documents, slides, and multi-modal inputs.
* Multilingual document analysis and structured content extraction for global use cases.
* Robotic or mobile automation with vision-guided contextual interaction.

## Limitations

* May show degraded performance on extremely low-quality or occluded images.
* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
* Variable accuracy on uncommon or low-resource languages or scripts.
* Long video processing may require substantial memory and is not optimized for streaming applications.
* Visual token settings affect performance; suboptimal configurations can impact results.
* In rare cases, outputs may contain hallucinated or contextually misaligned information.

---

## Training Details

| Parameter              | Value                                         |
| ---------------------- | --------------------------------------------- |
| **Dataset Size**       | 230K samples (Modular Combustion of Datasets) |
| **Model Architecture** | `Qwen2_5_VLForConditionalGeneration`          |
| **Total Disk Volume**  | 400,000 MB                                    |
| **Training Time**      | approx. 9,360(±120) seconds (\~2.60 hours)         |
| **Warmup Steps**       | 750                                           |
| **Precision**          | bfloat16                                      |

---

## References

* **DocVLM: Make Your VLM an Efficient Reader**
  [https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)

* **YaRN: Efficient Context Window Extension of Large Language Models**
  [https://arxiv.org/pdf/2309.00071](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](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](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](https://arxiv.org/pdf/2412.02210)