Update ReadMe_Surgical_Vocab_Reduction_for_QWEN2B_DotOCRv1.5_V1.0untested_py.md

ReadMe_Surgical_Vocab_Reduction_for_QWEN2B_DotOCRv1.5_V1.0untested_py.md

@@ -6,6 +6,14 @@ By surgically removing non-target Unicode blocks (such as Chinese, Cyrillic, Ara
 
 This implementation is inspired by and built upon the methodology outlined in [Method for Dynamically Reducing Logit Computation in LLMs](https://huggingface.co/MartialTerran/Method_for_Dynamically_Reducing_Logit_Computation_in_LLMs).
 
+Because I came upon this https://modelscope.cn/models/rednote-hilab/dots.ocr-1.5 
+"We present dots.ocr-1.5, a 3B-parameter multimodal model composed of a 1.2B vision encoder and a 1.7B language model. 
+Designed for universal accessibility, it possesses the capability to recognize virtually any human script. 
+Beyond achieving state-of-the-art (SOTA) performance in standard multilingual document parsing among models 
+of comparable size, dots.ocr-1.5 excels at converting structured graphics (e.g., charts and diagrams) directly into SVG code, 
+parsing web screens and spotting scene text. Furthermore, the model demonstrates competitive performance in general OCR, 
+object grounding & counting tasks."   
+
 ---
 
 ## 1. The Core Problem