Update README.md

README.md

@@ -1,15 +1,90 @@
 ---
 license: apache-2.0
 datasets:
-  - allenai/olmOCR-mix-0225
-  - prithivMLmods/Opendoc1-Analysis-Recognition
-  - prithivMLmods/Opendoc2-Analysis-Recognition
-  - prithivMLmods/Openpdf-Analysis-Recognition
+- allenai/olmOCR-mix-0225
+- prithivMLmods/Opendoc1-Analysis-Recognition
+- prithivMLmods/Opendoc2-Analysis-Recognition
+- prithivMLmods/Openpdf-Analysis-Recognition
 pipeline_tag: image-text-to-text
+tags:
+- OCR
+- Pdf
+- Doc
+- Image
+- text-generation-inference
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+library_name: transformers
 ---
 
 ![22.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/_L1v41LZYfOQCLLwHtAEy.png)
 
+# **docscopeOCR-7B-050425-exp**
+
+> The **docscopeOCR-7B-050425-exp** model is a fine-tuned version of **Qwen/Qwen2.5-VL-7B-Instruct**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats.
+
+# Key Enhancements
+
+* **Advanced Document-Level OCR**: Capable of extracting structured content from complex, multi-page documents such as invoices, academic papers, forms, and scanned reports.
+
+* **Enhanced Long-Context Vision-Language Understanding**: Designed to handle dense document layouts, long sequences of embedded text, tables, and diagrams with coherent cross-reference understanding.
+
+* **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/docscopeOCR-7B-050425-exp", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("prithivMLmods/docscopeOCR-7B-050425-exp")
+
+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)
+```
+
 ## Training Details
 
 | Parameter               | Value                                               |
@@ -27,6 +102,27 @@ pipeline_tag: image-text-to-text
 > [!note]
 > The open dataset image-text response will be updated soon.
 
+# Intended Use
+
+This model is intended for:
+
+* High-fidelity OCR from documents, forms, receipts, and printed or scanned materials.
+* Image and 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 OCR 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.
+
+
 ## References
 
 - **DocVLM: Make Your VLM an Efficient Reader** 
@@ -42,4 +138,4 @@ pipeline_tag: image-text-to-text
   [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) 
+  [https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)