Update README.md

README.md

@@ -25,4 +25,78 @@ tags:
 - 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.