Update README.md

README.md

@@ -21,7 +21,73 @@ tags:
 
 ![11.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/06COvqws8RSPQVm51EQgh.png)
 
-## Training Details
+# **coreOCR-7B-050325-preview**
+
+> The **coreOCR-7B-050325-preview** model is a fine-tuned version of **Qwen/Qwen2-VL-7B**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Designed with a focus on high-fidelity visual-textual comprehension, this model enhances document parsing, structured data extraction, and complex visual reasoning.
+
+# Key Enhancements
+
+* **Advanced Document-Level OCR**: Accurately processes and extracts structured text from complex, multi-page documents including invoices, forms, and research papers.
+
+* **Enhanced Long-Context Vision-Language Understanding**: Supports long-text retrieval and reasoning from documents and multimedia inputs, including dense text blocks, diagrams, and math content.
+
+* **SoTA Understanding Across Image Resolutions**: Achieves state-of-the-art results on visual benchmarks including MathVista, DocVQA, RealWorldQA, and MTVQA.
+
+* **Video Comprehension up to 20+ minutes**: Capable of high-quality video-based question answering, dialogue generation, and content summarization from long video sequences.
+
+* **Device Control via Visual Commands**: With complex reasoning and perception capabilities, it can be integrated with devices like mobile phones or robots for visually grounded automation.
+
+* **Multilingual OCR Support**: Recognizes and extracts text from images in multiple languages including English, Chinese, Arabic, Japanese, Korean, Vietnamese, and most European languages.
+
+# Quick Start with Transformers
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/coreOCR-7B-050325-preview", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("prithivMLmods/coreOCR-7B-050325-preview")
+
+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                                              |
 |-------------------------|----------------------------------------------------|
@@ -38,7 +104,27 @@ tags:
 > [!note]
 > The open dataset image-text response will be updated soon.
 
-## References
+# Intended Use
+
+This model is intended for:
+
+* Document analysis and OCR from scanned images, PDFs, and camera input.
+* Image-based question answering (e.g., educational content, diagrams, receipts).
+* Math problem solving and LaTeX text generation from handwritten or printed math content.
+* Long-context vision-text applications such as multi-slide document retrieval and dense information extraction.
+* Multilingual OCR workflows for cross-lingual business documents and global data digitization.
+* AI agents for mobile/robotic interaction through visual context.
+
+# Limitations
+
+* Performance may degrade on extremely noisy or low-resolution images.
+* Not suitable for real-time inference on edge devices due to model size and memory demands.
+* While multilingual, performance on low-resource or rare scripts may vary.
+* Not optimized for high-speed processing of video streams in constrained environments.
+* Contextual understanding depends on visual tokenization parameters; improper configuration may affect output quality.
+* Outputs may occasionally include hallucinations or incomplete answers in long-context queries.
+
+# References
 
 - **DocVLM: Make Your VLM an Efficient Reader** 
   [https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)
@@ -54,4 +140,4 @@ tags:
 
 - **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)
- 
+