Upload README.md

README.md

@@ -4,13 +4,13 @@ inference: false
 tags: [green, p1, llmware-chat, ov]
 ---
 
-# qwen2-0.5b-chat-ov
+# qwen-2-1.5b-instruct-ov
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**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.    
+**qwen2-1.5b-instruct-ov** is an OpenVino int4 quantized version of Qwen2 Instruct 1.5B, 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.  
+[**qwen2-1.5b-instruct**](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) is a fact-based question-answering model, optimized for complex business documents.  
 
 Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino) 
 
@@ -19,14 +19,14 @@ Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmwa
 
 ### Model Description
 
-- **Developed by:** llmware  
-- **Model type:** tinyllama  
-- **Parameters:** 1.1 billion
-- **Model Parent:** llmware/bling-tiny-llama-v0  
+- **Developed by:** qwen
+- **Model type:** qwen2-1.5b
+- **Parameters:** 1.5 billion
+- **Model Parent:** Qwen/Qwen2-1.5B-Instruct
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
-- **Uses:** Fact-based question-answering  
-- **RAG Benchmark Accuracy Score:** 86.5  
+- **Uses:** General purpose  
+- **RAG Benchmark Accuracy Score:** NA 
 - **Quantization:** int4