Tool use
Chat models are commonly trained with support for "function-calling" or "tool-use". Tools are functions supplied by the user, which the model can choose to call as part of its response. For example, models could have access to a calculator tool to perform arithmetic without having to perform the computation internally.
This guide will demonstrate how to define tools, how to pass them to a chat model, and how to handle the model's output when it calls a tool.
Passing tools
When a model supports tool-use, pass functions to the tools argument of [~PreTrainedTokenizerBase.apply_chat_template].
The tools are passed as either a JSON schema or Python functions. If you pass Python functions,
the arguments, argument types, and function docstring are parsed in order to generate the JSON schema automatically.
Although passing Python functions is very convenient, the parser can only handle Google-style docstrings. Refer to the examples below for how to format a tool-ready function.
def get_current_temperature(location: str, unit: str):
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
unit: The unit to return the temperature in. (choices: ["celsius", "fahrenheit"])
"""
return 22. # A real function should probably actually get the temperature!
def get_current_wind_speed(location: str):
"""
Get the current wind speed in km/h at a given location.
Args:
location: The location to get the wind speed for, in the format "City, Country"
"""
return 6. # A real function should probably actually get the wind speed!
tools = [get_current_temperature, get_current_wind_speed]
You can optionally add a Returns: block to the docstring and a return type to the function header, but most models won't use this information. The parser will also ignore the actual code inside the function!
What really matters is the function name, argument names, argument types, and docstring describing the function's purpose and the purpose of its arguments. These create the "signature" the model will use to decide whether to call the tool.
Tool-calling Example
Load a model and tokenizer that supports tool-use like NousResearch/Hermes-2-Pro-Llama-3-8B, but you can also consider a larger model like Command-R and Mixtral-8x22B if your hardware can support it.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "NousResearch/Hermes-2-Pro-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, dtype="auto", device_map="auto")
Create a chat history.
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries. You should reply with the unit used in the queried location."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
Next, pass messages and a list of tools to [~PreTrainedTokenizerBase.apply_chat_template]. Tokenize the chat and generate a response.
inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)
print(tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):]))
<tool_call>
{"arguments": {"location": "Paris, France", "unit": "celsius"}, "name": "get_current_temperature"}
</tool_call><|im_end|>
The chat model called the get_current_temperature tool with the correct parameters from the docstring. It inferred France as the location based on Paris, and that it should use Celsius for the units of temperature.
A model cannot actually call the tool itself. It requests a tool call, and it's your job to handle the call and append it and the result to the chat history. For
models that support response parsing, the response parsing will be handled automatically, and you can just use
[~PreTrainedTokenizer.parse_response] to extract the tool call. For other models, you'll need to manually translate the output
string into a tool call dict.
Regardless of the approach you use, the tool call should go in the tool_calls key of an assistant message. This is the recommended API, and should be supported by the chat template of most tool-using models.
[!WARNING] Although
tool_callsis similar to the OpenAI API, the OpenAI API uses a JSON string as itstool_callsformat. This may cause errors or strange model behavior if used in Transformers, which expects a dict.
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France", "unit": "celsius"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
Append the tool response to the chat history with the tool role.
messages.append({"role": "tool", "content": "22"}) # Note that the returned content is always a string!
Finally, allow the model to read the tool response and reply to the user.
inputs = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_dict=True, return_tensors="pt")
out = model.generate(**inputs.to(model.device), max_new_tokens=128)
print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))
The temperature in Paris, France right now is 22°C.<|im_end|>
[!WARNING] Although the key in the assistant message is called
tool_calls, in most cases, models only emit a single tool call at a time. Some older models emit multiple tool calls at the same time, but this is a significantly more complex process, as you need to handle multiple tool responses at once and disambiguate them, often using tool call IDs. Please refer to the model card to see exactly what format a model expects for tool calls.
JSON schemas
Another way to define tools is by passing a JSON schema.
You can also manually call the low-level functions that convert Python functions to JSON schemas, and then check or edit the generated schemas. This is usually not necessary, but is useful for understanding the underlying mechanics. It's particularly important for chat template authors who need to access the JSON schema to render the tool definitions.
The [~PreTrainedTokenizerBase.apply_chat_template] method uses the get_json_schema function to convert Python callables to a JSON schema. This includes methods: self and cls are treated as implicit receiver arguments and are ignored.
from transformers.utils import get_json_schema
def multiply(a: float, b: float):
"""
A function that multiplies two numbers
Args:
a: The first number to multiply
b: The second number to multiply
"""
return a * b
schema = get_json_schema(multiply)
print(schema)
{
"type": "function",
"function": {
"name": "multiply",
"description": "A function that multiplies two numbers",
"parameters": {
"type": "object",
"properties": {
"a": {
"type": "number",
"description": "The first number to multiply"
},
"b": {
"type": "number",
"description": "The second number to multiply"
}
},
"required": ["a", "b"]
}
}
}
We won't go into the details of JSON schema itself here, since it's already very well documented elsewhere. We will, however, mention that you can pass JSON schema dicts to the tools argument of [~PreTrainedTokenizerBase.apply_chat_template] instead of Python functions:
# A simple function that takes no arguments
current_time = {
"type": "function",
"function": {
"name": "current_time",
"description": "Get the current local time as a string.",
"parameters": {
'type': 'object',
'properties': {}
}
}
}
# A more complete function that takes two numerical arguments
multiply = {
'type': 'function',
'function': {
'name': 'multiply',
'description': 'A function that multiplies two numbers',
'parameters': {
'type': 'object',
'properties': {
'a': {
'type': 'number',
'description': 'The first number to multiply'
},
'b': {
'type': 'number', 'description': 'The second number to multiply'
}
},
'required': ['a', 'b']
}
}
}
model_input = tokenizer.apply_chat_template(
messages,
tools = [current_time, multiply]
)
Source: docs/source/en/chat_extras.md