Update README.md

README.md

@@ -1,37 +1,33 @@
 ---
 license: apache-2.0
 inference: false 
-tags: [llmware-rag, p7, ov]
+tags: [green, llmware-rag, p7, ov]
 ---
 
 # dragon-mistral-0.3-ov
 
-<!-- Provide a quick summary of what the model is/does. -->
+**dragon-mistral-0.3-ov** is an OpenVino int4 quantized version of dragon-mistral-0.3, providing a fast inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
 
-**bling-tiny-llama-ov** is an OpenVino int4 quantized version of BLING Tiny-Llama 1B, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
-
-[**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-v0) is a fact-based question-answering model, optimized for complex business documents.  
-
-Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino) 
-
-Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)    
+[**dragon-mistral-0.3**](https://huggingface.co/llmware/dragon-mistral-0.3) is a fact-based question-answering model, optimized for complex business documents.  
 
 
 ### Model Description
 
 - **Developed by:** llmware  
-- **Model type:** tinyllama  
-- **Parameters:** 1.1 billion
-- **Model Parent:** llmware/bling-tiny-llama-v0  
+- **Model type:** mistral-0.3  
+- **Parameters:** 7 billion
+- **Model Parent:** llmware/dragon-mistral-0.3  
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
 - **Uses:** Fact-based question-answering  
-- **RAG Benchmark Accuracy Score:** 86.5  
+- **RAG Benchmark Accuracy Score:** 99.5
 - **Quantization:** int4  
   
 
 ## Model Card Contact
 
+[llmware on github](https://www.github.com/llmware-ai/llmware)  
+
 [llmware on hf](https://www.huggingface.co/llmware)  
 
 [llmware website](https://www.llmware.ai)