Update README.md

README.md

@@ -13,12 +13,17 @@ 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:  
 
-slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping.   Please also see slim-qa-gen-phi-3-tool, which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.  
+  -- '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).  
+  
 
+slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping.   Please also see [slim-qa-gen-phi-3-tool](https://huggingface.co/llmware/slim-qa-gen-phi-3-tool), which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.  
 
-[**slim-qa-gen-tiny*](https://huggingface.co/llmware/slim-qa-gen-tiny) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
+
+[**slim-qa-gen-tiny**](https://huggingface.co/llmware/slim-qa-gen-tiny) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
 
 
 To pull the model via API:  
@@ -32,14 +37,14 @@ 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")
+    model = ModelCatalog().load_model("slim-qa-gen-tiny-tool", temperature=0.5, sample=True)
     response = model.function_call(text_sample)  
 
     # this one line will download the model and run a series of tests
     ModelCatalog().tool_test_run("slim-qa-gen-tiny-tool", verbose=True)  
 
 
-Note: please review [**config.json**](https://huggingface.co/llmware/slim-xsum-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-qa-gen-tiny-tool/blob/main/config.json) in the repository for prompt template information, details on the model, and full test set.  
 
 
 ## Model Card Contact