Update README.md

README.md

@@ -1,29 +1,34 @@
 ---
-license: apache-2.0
-inference: false 
-tags: [green, llmware-rag, p1, ov]
+license: llama2
+inference: false
+tags:
+- green
+- llmware-rag
+- p7
+- ov
+- emerald
 ---
 
-# bling-tiny-llama-ov
+# dragon-llama2-ov
 
-**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, quantized and packaged in OpenVino int4 for AI PCs using Intel GPU, CPU and NPU.    
+**dragon-llama2-ov** is a high-quality, 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 smallest and fastest in the series.  For higher accuracy, look at larger models in the BLING/DRAGON series.    
+This model provides a good combination of accuracy and inference performance.      
 
 ### Model Description
 
 - **Developed by:** llmware  
-- **Model type:** tinyllama  
-- **Parameters:** 1.1 billion  
+- **Model type:** llama2  
+- **Parameters:** 7 billion  
 - **Quantization:** int4  
-- **Model Parent:** [llmware/bling-tiny-llama-v0](https://www.huggingface.co/llmware/bling-tiny-llama-v0)    
+- **Model Parent:** [llmware/dragon-llama-7b-v0](https://www.huggingface.co/llmware/dragon-llama-7b-v0)      
 - **Language(s) (NLP):** English  
-- **License:** Apache 2.0  
-- **Uses:** Fact-based question-answering, RAG  
-- **RAG Benchmark Accuracy Score:** 86.5  
+- **License:** Llama2 Community License  
+- **Uses:** Fact-based question-answering, RAG    
+- **RAG Benchmark Accuracy Score:** 97.25  
 
 
 ## 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)  
+[llmware website](https://www.llmware.ai)