Update README.md

README.md

@@ -150,6 +150,50 @@ The stock fundamentals data for Tesla (TSLA) are as follows:
 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
 ```
 
+## Chat Templates for function calling
+
+You can also use chat templates for function calling. For more information, please see the relevant section of the [chat template documentation](https://huggingface.co/docs/transformers/en/chat_templating#advanced-tool-use--function-calling).
+
+Here is a brief example of this approach:
+
+```python
+def multiply(a: int, b: int):
+    """
+    A function that multiplies two numbers
+    
+    Args:
+        a: The first number to multiply
+        b: The second number to multiply
+    """
+    return int(a) * int(b)
+
+tools = [multiply]  # Only one tool in this example, but you probably want multiple!
+
+model_input = tokenizer.apply_chat_template(
+    messages,
+    tools=tools
+)
+```
+
+The docstrings and type hints of the functions will be used to generate a function schema that will be read by the chat template and passed to the model.
+Please make sure you include a docstring in the same format as this example!
+
+If the model makes a tool call, you can append the tool call to the conversation like so:
+
+```python
+tool_call = {"name": "multiply", "arguments": {"a": "6", "b": "7"}}
+messages.append({"role": "assistant", "tool_calls": [{type": "function", "function": tool_call}]})
+```
+
+Next, call the tool function and append the tool result:
+
+```python
+messages.append({"role": "tool", "name": "multiply", "content": "42"})
+```
+
+And finally apply the chat template to the updated `messages` list and `generate()` text once again to continue the conversation.
+
+
 ## Prompt Format for JSON Mode / Structured Outputs
 
 Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.