Update README.md

README.md

@@ -6,9 +6,9 @@ tags: [green, llmware-rag, p1, ov]
 
 # bling-tiny-llama-ov
 
-**bling-tiny-llama-ov** is a very small, very fast fact-based question-answering model, optimized for complex business documents, and quantized and packaged in OpenVino int4 for AI PCs using Intel GPU, CPU and NPU.    
+**bling-tiny-llama-ov** is a very small, very fast fact-based question-answering model, designed for retrieval augmented generation (RAG) with complex business documents, and quantized and packaged in OpenVino int4 for AI PCs using Intel GPU, CPU and NPU.    
 
-This model is one of the smallest and fastest in the series.  For higher accuracy, look at larger models in the series, e.g., llmware/bling-phi-3-ov.  
+This model is one of the smallest and fastest in the series.  For higher accuracy, look at larger models in the BLING/DRAGON series.    
 
 ### Model Description
 
@@ -16,7 +16,7 @@ This model is one of the smallest and fastest in the series.  For higher accurac
 - **Model type:** tinyllama  
 - **Parameters:** 1.1 billion  
 - **Quantization:** int4  
-- **Model Parent:** llmware/bling-tiny-llama-v0  
+- **Model Parent:** [llmware/bling-tiny-llama-v0](https://www.huggingface.co/llmware/bling-tiny-llama-v0)    
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
 - **Uses:** Fact-based question-answering, RAG  
@@ -29,6 +29,6 @@ Looking for AI PC solutions, contact us at [llmware](https://www.llmware.ai)
 
 
 ## 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)