Update README.md

README.md

@@ -4,9 +4,9 @@ inference: false
 tags: [green, llmware-rag, p1, ov]
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-# bling-tiny-llama-ov
+# bling-tiny-llama-onnx
 
-**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.    
+**bling-tiny-llama-onnx** 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 ONNX 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 BLING/DRAGON series.    
 
@@ -23,7 +23,7 @@ This model is one of the smallest and fastest in the series.  For higher accurac
 - **RAG Benchmark Accuracy Score:** 86.5  
 
     
-Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)  
+Get started right away with [ONNX Runtime](https://github.com/microsoft/onnxruntime)  
 
 Looking for AI PC solutions, contact us at [llmware](https://www.llmware.ai)