Update README.md

README.md

@@ -13,7 +13,12 @@ This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') func
 
      `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} `  
 
-The model has been designed to accept one of three different parameters to guide the type of question-answer created:  'question, answer' (generates a standard question and answer), 'boolean' (generates a 'yes-no' question and answer), and 'multiple choice' (generates a multiple choice question and answer).  
+The model has been designed to accept one of three different parameters to guide the type of question-answer created:   
+  -- 'question, answer' (generates a standard question and answer),  
+  -- 'boolean' (generates a 'yes-no' question and answer), and  
+  -- 'multiple choice' (generates a multiple choice question and answer).  
+
+Note: we would generally recommend using sampling and temperature(0.5+) for varied generations, although if using 'multiple choice' mode, then we have seen the best results with temperature in the 0.2-0.3 range.  
 
 [**slim-qa-gen-phi-3**](https://huggingface.co/llmware/slim-qa-gen-phi-3) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
 
@@ -29,7 +34,7 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
     from llmware.models import ModelCatalog  
     
     # to load the model and make a basic inference
-    model = ModelCatalog().load_model("slim-qa-gen-tiny-tool", temperature=0.7, sample=True)  
+    model = ModelCatalog().load_model("slim-qa-gen-phi-3-tool", temperature=0.5, sample=True)  
     response = model.function_call(text_sample, params=["boolean"])  
 
     # this one line will download the model and run a series of tests