Update README.md

README.md

@@ -6,14 +6,7 @@ tags: [green, p1, llmware-fx, ov, emerald]
 
 # slim-extract-tiny-ov  
 
-**slim-extract-tiny-ov** is a specialized function calling model with a single mission to look for values in a text, based on an "extract" key that is passed as a parameter.  No other instructions are required except to pass the context passage, and the target key, e.g., 
-
-    text_passage = "The company announced that for the current quarter the total revenue increased by 9% to $125 million."
-
-    llm_response = model.function_call(text_passage, function="extract", params=["revenue"])
-
-    Output: `llm_response = {"revenue": [$125 million"]}`
-  
+**slim-extract-tiny-ov** is a specialized function calling model with a single mission to look for values in a text, based on an "extract" key that is passed as a parameter.  No other instructions are required except to pass the context passage, and the target key, and the model returns a python dictionary consisting of the extract key and a list of the values found in the text.  
 
 This is an OpenVino int4 quantized version of slim-extract-tiny, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
 
@@ -30,6 +23,16 @@ This is an OpenVino int4 quantized version of slim-extract-tiny, providing a ver
 - **RAG Benchmark Accuracy Score:** NA  
 - **Quantization:** int4  
   
+### Example Usage  
+
+    from llmware.models import ModelCatalog
+
+    text_passage = "The company announced that for the current quarter the total revenue increased by 9% to $125 million."  
+    model = ModelCatalog().load_model("slim-extract-tiny-ov")   
+    llm_response = model.function_call(text_passage, function="extract", params=["revenue"])  
+
+    Output: `llm_response = {"revenue": [$125 million"]}`  
+  
 
 ## Model Card Contact