Update README.md

README.md

@@ -34,7 +34,7 @@ Inference speed and loading time is much faster with the 'tool' versions of the
 The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls, and to provide a natural language flexible tool that can be used as decision gates and processing steps in a complex LLM-based automation workflow.  
 
 <details>
-<summary></summary><b>Getting Started: </b> </summary>
+<summary><b>Getting Started: </b> </summary>
 
     model = AutoModelForCausalLM.from_pretrained("llmware/slim-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sentiment")
@@ -42,7 +42,6 @@ The intended use of SLIM models is to re-imagine traditional 'hard-coded' classi
     function = "classify"
     params = "sentiment"
 
-    text = "That was the worst earnings call of the year.  The CEO should be fired."
     text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."  
     
     prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
@@ -69,7 +68,7 @@ Sample output:
     {"sentiment": ["negative"]}
 
 
-All of the SLIM models use a novel prompt instruction structured as follows:
+## Prompt Instruction format: all of the SLIM models use a novel prompt instruction structured as follows:
 
     "<human> " + {text} + "\n" +