Update README.md

README.md

@@ -42,9 +42,17 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype='auto', device_map="auto")
 
 # Example usage (adapt as needed for your specific tool usage scenario)
-system_prompt = "You are an AI assistant for function calling. 
-For politically sensitive questions, security and privacy issues, you will refuse to answer.\n"
-user_prompt = "Please find the weather in London and then book a table at a restaurant nearby."
+"""You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
+If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out.
+You should only return the function call in tools call sections.
+
+If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]
+You SHOULD NOT include any other text in the response.
+Here is a list of functions in JSON format that you can invoke.\n{functions}\n
+"""
+# User query
+query = "Find me the sales growth rate for company XYZ for the last 3 years and also the interest coverage ratio for the same duration."
+
 tools = [
     {
         "name": "financial_ratios.interest_coverage", "description": "Calculate a company's interest coverage ratio given the company name and duration",