Upload README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+inference: false 
+base_model: llmware/bling-tiny-llama-v0
+base_model_relation: quantized 
+tags: [green, llmware-rag, p1, ov]
+---
+
+# bling-tiny-llama-npu-ov
+
+**bling-tiny-llama-npu-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 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  
+- **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, RAG  
+- **RAG Benchmark Accuracy Score:** 86.5  
+
+
+## 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)