Update README.md

README.md

@@ -6,9 +6,13 @@ 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, and the model will generate a python dictionary consisting of the extract key and a list of the values found in the text, including an 'empty list' if the text does not provide an answer for the value of the selected key.  
+**slim-extract-tiny-ov** is a specialized function calling model that implements a generative 'question' (e.g., 'q-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of one key:
 
-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.    
+ `{'question': ['What was the amount of revenue in the quarter?']} `  
+
+The model has been designed to accept one of three different parameters to guide the type of question-answer created: 'question' (generates a standard question), 'boolean' (generates a 'yes-no' question), and 'multiple choice' (generates a multiple choice question).
+
+This is an OpenVino int4 quantized version of slim-q-gen, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
 
 
 ### Model Description
@@ -16,23 +20,12 @@ This is an OpenVino int4 quantized version of slim-extract-tiny, providing a ver
 - **Developed by:** llmware  
 - **Model type:** tinyllama  
 - **Parameters:** 1.1 billion
-- **Model Parent:** llmware/slim-extract-tiny
+- **Model Parent:** llmware/slim-q-gen
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
-- **Uses:** Extraction of values from complex business documents    
+- **Uses:** Question generation from a context passage     
 - **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