Upload README.md

README.md

@@ -1,37 +1,29 @@
 ---
 license: apache-2.0
 inference: false 
-tags: [green, llmware-rag, p3,onnx]
+tags: [green, llmware-rag, p3, ov]
 ---
 
-# bling-phi-3-onnx
+# bling-phi-3-ov
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-**bling-phi-3-ov** is an ONNX int4 quantized version of BLING Phi-3, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
-
-[**bling-phi-3**](https://huggingface.co/llmware/bling-phi-3) 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)    
+**bling-phi-3-ov** is a fast and accurate fact-based question-answering model, designed for retrieval augmented generation (RAG) with complex business documents, quantized and packaged in OpenVino int4 for AI PCs using Intel GPU, CPU and NPU.    
 
+This model is one of the most accurate in the BLING/DRAGON model series, which is especially notable given the relatively small size and is ideal for use on AI PCs and local inferencing.  
 
 ### Model Description
 
 - **Developed by:** llmware  
-- **Model type:** phi3  
-- **Parameters:** 3.8 billion
-- **Model Parent:** llmware/bling-phi-3  
+- **Model type:** phi-3
+- **Parameters:** 3.8 billion  
+- **Quantization:** int4  
+- **Model Parent:** [llmware/bling-phi-3](https://www.huggingface.co/llmware/bling-phi-3)    
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
-- **Uses:** Fact-based question-answering  
-- **RAG Benchmark Accuracy Score:** 99.5  
-- **Quantization:** int4  
-  
-
-## Model Card Contact
+- **Uses:** Fact-based question-answering, RAG  
+- **RAG Benchmark Accuracy Score:** 99.5
 
+    
+## 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)