---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/Gliese-OCR-7B-Post1.0
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- trl
- Document
- VLM
- KIE
- OCR
- VL
- Camel
- Openpdf
- text-generation-inference
- Extraction
- Linking
- Markdown
- .Md
- Document Digitization
- Intelligent Document Processing (IDP)
- Intelligent Word Recognition (IWR)
- Optical Mark Recognition (OMR)
---

# **Gliese-OCR-7B-Post2.0-final**

> The **Gliese-OCR-7B-Post2.0-final** model is a refined and optimized version of **[Gliese-OCR-7B-Post1.0](https://huggingface.co/prithivMLmods/Gliese-OCR-7B-Post1.0)**, built upon the **Qwen2.5-VL** architecture. It represents the final iteration in the **Gliese-OCR** series, offering enhanced efficiency, precision, and visualization capabilities for **document OCR**, **visual analysis**, and **information extraction**.
>
> Fine-tuned with extended document visualization data and OCR-focused objectives, this model delivers superior accuracy across a wide range of document types, including scanned PDFs, handwritten pages, structured forms, and analytical reports.

## Key Enhancements

* **Optimized Document Visualization and OCR Pipeline**: Significantly improved recognition of text, layout, and embedded visuals for structured document understanding.
* **Context-Aware Multimodal Linking**: Enhanced understanding of document context with stronger alignment between text, images, and layout components.
* **Refined Document Retrieval**: Improved retrieval accuracy from complex layouts and multi-page documents.
* **High-Fidelity Content Extraction**: Precise extraction of structured, semi-structured, and unstructured information with advanced text normalization.
* **Analytical Recognition**: Superior reasoning over charts, graphs, tables, and mathematical equations.
* **Improved Visual Reasoning and Layout Awareness**: Trained on document visualization datasets for advanced spatial and semantic comprehension.
* **State-of-the-Art Performance Across Resolutions**: Achieves top results on benchmarks such as DocVQA, InfographicVQA, MathVista, and RealWorldQA.
* **Extended Multimodal Duration Support**: Handles long document sequences and extended videos (20+ minutes).
* **Final Release Stability**: Consolidates all prior improvements for stable and reliable performance.

## 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-Post2.0-final", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Gliese-OCR-7B-Post2.0-final")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
            {"type": "text", "text": "Describe the document structure and extract key text content."},
        ],
    }
]

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",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=256)
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

* Document visualization and OCR extraction tasks.
* Context-aware document retrieval and multimodal linking.
* Extraction and LaTeX formatting of equations and structured content.
* Analytical document interpretation (charts, tables, graphs, and figures).
* Multilingual OCR for enterprise, academic, and research use cases.
* Summarization, question answering, and cross-modal reasoning over long documents.
* Intelligent robotic or mobile automation guided by visual document input.

## Limitations

* Reduced accuracy on heavily degraded or occluded documents.
* High computational requirements for large-scale or real-time applications.
* Limited optimization for low-resource or edge devices.
* Occasional misalignment in text layout or minor hallucinations in outputs.
* Performance may vary depending on visual token configuration and context length settings.