examples for fine-tuning function calling
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#
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# # Fine-tuning Script:
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# Please start by reading the Fine-tuning section of README.md.
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#
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# # Fine-tuning Data Preparation:
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# Then, if you would like to see some examples of how to prepare training samples for function calling,
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# which is actually ReAct prompting under the hood, please read this file.
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# # Inference Script:
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# If you are interested in implementing function calling via ReAct prompting for inference,
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# please refer to openai_api.py in our repository.
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#
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# If you have any questions, please raise an issue.
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#
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import json
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def format_train_sample(messages):
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#
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# You do not need the `function` role, as Qwen's function calling is actually implemented via ReAct,
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# not by adding a `function` role or `function_call` message. See openai_api.py for details.
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#
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# If you need the `system` role, you might need to modify `finetune.py` accordingly.
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#
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assert set(m["role"] for m in messages) == {"user", "assistant"}
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sample = {
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"conversations": [
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{
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"from": m["role"],
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"value": m["content"],
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}
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for m in messages
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]
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}
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return sample
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TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters}"""
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REACT_INSTRUCTION = """Answer the following questions as best you can. You have access to the following APIs:
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{tools_text}
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [{tools_name_text}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!"""
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def build_react_instruction(functions):
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tools_text = []
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tools_name_text = []
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for func_info in functions:
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name = func_info.get("name", "")
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name_m = func_info.get("name_for_model", name)
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name_h = func_info.get("name_for_human", name)
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desc = func_info.get("description", "")
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desc_m = func_info.get("description_for_model", desc)
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tool = TOOL_DESC.format(
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name_for_model=name_m,
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name_for_human=name_h,
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description_for_model=desc_m,
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parameters=json.dumps(func_info["parameters"], ensure_ascii=False),
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)
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tools_text.append(tool)
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tools_name_text.append(name_m)
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tools_text = "\n\n".join(tools_text)
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tools_name_text = ", ".join(tools_name_text)
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instruction = REACT_INSTRUCTION.format(
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tools_text=tools_text,
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tools_name_text=tools_name_text,
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)
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return instruction
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def main():
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example_train_samples = []
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example_functions = [
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{
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"name_for_human": "Google Search",
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"name_for_model": "google_search",
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"description_for_model": "Google Search is a general search engine that can be used to access the internet,"
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+ " query encyclopedia knowledge, and stay informed about current events."
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+ " Format the arguments as a JSON object.", # If you expect `Action Input` to be a JSON.
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"parameters": [
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{
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"name": "search_query",
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"description": "Search keywords or phrases",
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"required": True, # Set to False if it is an optional parameter.
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"schema": {"type": "string"},
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},
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# You can add more parameters to this `parameters` list if you wish.
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],
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},
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{
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"name_for_human": "Code Interpreter",
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"name_for_model": "code_interpreter",
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"description_for_model": "Code interpreter that can execute Python code."
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+ "Enclose the code within triple backticks (`)"
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+ " at the beginning and end of the code.", # If you expect `Action Input` to be a Markdown code block.
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"parameters": [
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{
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"name": "code",
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"description": "Code to be executed",
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"required": True,
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"schema": {"type": "string"},
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},
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],
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},
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]
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example_instruction = build_react_instruction(example_functions)
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# This example has multiple actions in one single response.
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example_train_samples.append(
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[
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{
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"role": "user",
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"content": f"{example_instruction}\n\nQuestion: Who is jay chou? And who is his wife?",
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},
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{
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"role": "assistant",
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"content": """
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Thought: I need to google Jay Chou.
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Action: google_search
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Action Input: {"search_query": "Jay Chou"}
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Observation: Jay Chou is ...
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Thought: I need to google Jay's wife.
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Action: google_search
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Action Input: {"search_query": "Jay Chou's wife"}
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Observation: Jay Chou's wife is ...
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Thought: I now know the final answer
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Final Answer: Jay Chou is ... His wife is ...
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""".strip(),
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},
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]
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)
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# This example involves multiple rounds of conversation.
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example_train_samples.append(
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[
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# Round #1
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{
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"role": "user",
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"content": f"{example_instruction}\n\nQuestion: 123+456=?",
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},
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{
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"role": "assistant",
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"content": """
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Thought: I need to compute the result using Code Interpreter.
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Action: code_interpreter
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Action Input:
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```py
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123 + 456
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```
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Observation: 579
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Thought: I now know the final answer
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Final Answer: 579
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""".strip(),
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},
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# Round #2
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{
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"role": "user",
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"content": "Multiply the result by 2.",
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},
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{
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"role": "assistant",
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"content": """
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Thought: Code Interpreter is helpful for answering this question.
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Action: code_interpreter
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Action Input:
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```py
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579 * 2
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```
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Observation: 1158
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Thought: I now know the final answer
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Final Answer: 1158
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""".strip(),
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},
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# Round #3
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{
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"role": "user",
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"content": "You are so smart, Qwen.", # No action is needed for this question.
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},
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{
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"role": "assistant",
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"content": """
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Thought: I now know the final answer
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Final Answer: Thank you.
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""".strip(),
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},
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# Round #4
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{
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"role": "user",
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"content": "Please re-execute the code for computing my first question again.",
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},
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{
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"role": "assistant",
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"content": """
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Thought: I need to re-compute the result.
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Action: code_interpreter
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Action Input:
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```py
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123 + 456
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```
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Observation: 579
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Thought: I now know the final answer
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Final Answer: 579
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""".strip(),
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},
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]
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)
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example_train_samples = [format_train_sample(x) for x in example_train_samples]
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with open(
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"example_func_call_train_samples.json", "w"
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) as fout: # data for fine-tuning
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fout.write(json.dumps(example_train_samples, indent=2, ensure_ascii=False))
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if __name__ == "__main__":
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main()
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