---
license: apache-2.0
pipeline_tag: image-text-to-text
language:
- en
- zh
base_model:
- prithivMLmods/Camel-Doc-OCR-062825
library_name: transformers
tags:
- Document
- VLM
- OCR
- VL
- Camel
- Openpdf
- text-generation-inference
- Extraction
- Linking
- Markdown
- Document Digitization
- Intelligent Document Processing (IDP)
- Intelligent Word Recognition (IWR)
- Optical Mark Recognition (OMR)
---

![1.png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65bb837dbfb878f46c77de4c%2FGNsuO5cpxz73RW7xlrYCU.png)

# **Gliese-OCR-7B-Post1.0**

> The **Gliese-OCR-7B-Post1.0** model is a fine-tuned version of **[Camel-Doc-OCR-062825](https://huggingface.co/prithivMLmods/Camel-Doc-OCR-062825)**, 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.

> [!note]
This model shows significant improvements in [LaTeX rendering and Markdown rendering for OCR tasks](https://huggingface.co/prithivMLmods/Gliese-OCR-7B-Post1.0/blob/main/Gliese-OCR-7B-Post1.0(4-bit)-reportlab/Gliese_OCR_7B_Post1_0(4_bit)_reportlab.ipynb).

# 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, Q\&A, and multi-modal reasoning.

* **Visually-Grounded Device Interaction**: Enables mobile/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/Gliese-OCR-7B-Post1.0", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Gliese-OCR-7B-Post1.0")

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/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.