Upload README.md

README.md

@@ -1,53 +1,34 @@
 ---
 license: apache-2.0
 inference: false 
-tags: [green, llmware-rag, p1, onnx]
+tags: [green, llmware-rag, p1, ov]
 ---
 
-# bling-tiny-llama-onnx
+# bling-tiny-llama-ov
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-**bling-tiny-llama-onnx** is an ONNX 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.    
-
-[**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-v0) is a fact-based question-answering model, optimized for complex business documents.  
-
-Get started right away
-
-1.  Install dependencies
-
-  ```
-  pip3 install llmware
-  pip3 install onnxruntime_genai
-  ```
-
-2.  Hello World
-
-  ```python
-  from llmware.models import ModelCatalog
-  model = ModelCatalog().load_model("bling-tiny-llama-onnx")
-  response = model.inference("The stock price is $45.\nWhat is the stock price?")
-  print("response: ", response)
-  ```
-    
-Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)    
+**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 BLING/DRAGON series.    
 
 ### Model Description
 
 - **Developed by:** llmware  
 - **Model type:** tinyllama  
-- **Parameters:** 1.1 billion
-- **Model Parent:** llmware/bling-tiny-llama-v0  
+- **Parameters:** 1.1 billion  
+- **Quantization:** int4  
+- **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  
+- **Uses:** Fact-based question-answering, RAG  
 - **RAG Benchmark Accuracy Score:** 86.5  
-- **Quantization:** int4  
-  
 
-## Model Card Contact
+    
+Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)  
+
+Looking for AI PC solutions, contact us at [llmware](https://www.llmware.ai)    
 
-[llmware on hf](https://www.huggingface.co/llmware)  
 
+## 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)