Update README.md

README.md

@@ -19,6 +19,60 @@ tags:
 This model was converted to GGUF format from [`prithivMLmods/LatexMind-2B-Codec`](https://huggingface.co/prithivMLmods/LatexMind-2B-Codec) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/LatexMind-2B-Codec) for more details on the model.
 
+---
+The LatexMind-2B-Codec model is a fine-tuned version of Qwen2-VL-2B-Instruct, optimized for Optical Character Recognition (OCR), image-to-text conversion, and mathematical expression extraction with LaTeX formatting.
+ This model integrates a conversational approach with visual and textual
+ understanding to handle multi-modal tasks effectively.  
+
+
+
+	
+		
+	
+
+		Key Enhancements:
+	
+
+
+
+SoTA understanding of images with various resolutions & aspect ratios:
+ LatexMind-2B-Codec achieves state-of-the-art performance on visual 
+understanding benchmarks, including MathVista, DocVQA, RealWorldQA, 
+MTVQA, etc.  
+
+
+Advanced LaTeX extraction: The model specializes
+ in extracting structured mathematical expressions from images and 
+documents, converting them into LaTeX format for precise rendering and 
+further computation.  
+
+
+Understanding long-duration videos (20min+): 
+LatexMind-2B-Codec can process videos over 20 minutes long, enabling 
+high-quality video-based question answering, mathematical solution 
+explanation, and educational content creation.  
+
+
+Agent capabilities for automated operations: 
+With complex reasoning and decision-making abilities, the model can be 
+integrated with mobile devices, robots, and assistive technologies to 
+automate tasks based on visual and textual inputs.  
+
+
+Multilingual Support: To serve global users, in 
+addition to English and Chinese, the model supports text recognition 
+inside images across multiple languages, including European languages, 
+Japanese, Korean, Arabic, Vietnamese, etc.
+
+
+
+
+This model is particularly effective in retrieving mathematical notations and equations
+ from scanned documents, whiteboard images, and handwritten notes, 
+ensuring accurate conversion to LaTeX code for further academic and 
+computational applications.  
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)