#
# # Fine-tuning Script:
# Please start by reading the Fine-tuning section of README.md.
#
# # Fine-tuning Data Preparation:
# Then, if you would like to see some examples of how to prepare training samples for function calling,
# which is actually ReAct prompting under the hood, please read this file.
# # Inference Script:
# If you are interested in implementing function calling via ReAct prompting for inference,
# please refer to openai_api.py in our repository.
#
# If you have any questions, please raise an issue.
#
import json
def format_train_sample ( messages ) :
#
# You do not need the `function` role, as Qwen's function calling is actually implemented via ReAct,
# not by adding a `function` role or `function_call` message. See openai_api.py for details.
#
# If you need the `system` role, you might need to modify `finetune.py` accordingly.
#
assert set ( m [ " role " ] for m in messages ) == { " user " , " assistant " }
sample = {
" conversations " : [
{
" from " : m [ " role " ] ,
" value " : m [ " content " ] ,
}
for m in messages
]
}
return sample
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} """
REACT_INSTRUCTION = """ Answer the following questions as best you can. You have access to the following APIs:
{ tools_text }
Use the following format :
Question : the input question you must answer
Thought : you should always think about what to do
Action : the action to take , should be one of [ { tools_name_text } ]
Action Input : the input to the action
Observation : the result of the action
. . . ( this Thought / Action / Action Input / Observation can be repeated zero or more times )
Thought : I now know the final answer
Final Answer : the final answer to the original input question
Begin ! """
def build_react_instruction ( functions ) :
tools_text = [ ]
tools_name_text = [ ]
for func_info in functions :
name = func_info . get ( " name " , " " )
name_m = func_info . get ( " name_for_model " , name )
name_h = func_info . get ( " name_for_human " , name )
desc = func_info . get ( " description " , " " )
desc_m = func_info . get ( " description_for_model " , desc )
tool = TOOL_DESC . format (
name_for_model = name_m ,
name_for_human = name_h ,
description_for_model = desc_m ,
parameters = json . dumps ( func_info [ " parameters " ] , ensure_ascii = False ) ,
)
tools_text . append ( tool )
tools_name_text . append ( name_m )
tools_text = " \n \n " . join ( tools_text )
tools_name_text = " , " . join ( tools_name_text )
instruction = REACT_INSTRUCTION . format (
tools_text = tools_text ,
tools_name_text = tools_name_text ,
)
return instruction
def main ( ) :
example_train_samples = [ ]
example_functions = [
{
" name_for_human " : " 在线搜索 " ,
" name_for_model " : " search " ,
" description_for_model " : " 使用此工具可以搜索群内成员的信息， " , # If you expect `Action Input` to be a JSON.
" parameters " : [
{
" name " : " keywords " ,
" description " : " 需要搜索的关键词。 " ,
" required " : True ,
" schema " : { " type " : " string " } ,
} ,
] ,
} ,
{
" name_for_human " : " 天气信息 " ,
" name_for_model " : " get_weather " ,
" description_for_model " : " 查询某个地点的天气信息。Format the arguments as a JSON object. " ,
" parameters " : [
{
" name " : " position " ,
" description " : " 需要查询天气的地点。 " ,
" required " : True ,
" schema " : { " type " : " string " } ,
} ,
] ,
} ,
{
" name_for_human " : " 封禁用户 " ,
" name_for_model " : " ban_user " ,
" description_for_model " : " 在用户多次触犯道德规范时，使用此工具可以封禁用户。 " ,
" parameters " : [ ] ,
}
]
example_instruction = build_react_instruction ( example_functions )
# This example has multiple actions in one single response.
example_train_samples . append (
[
{
" role " : " user " ,
" content " : f " { example_instruction } \n \n Question: Who is jay chou? And who is his wife? " ,
} ,
{
" role " : " assistant " ,
" content " : """
Thought : I need to google Jay Chou .
Action : google_search
Action Input : { " search_query " : " Jay Chou " }
Observation : Jay Chou is . . .
Thought : I need to google Jay ' s wife.
Action : google_search
Action Input : { " search_query " : " Jay Chou ' s wife " }
Observation : Jay Chou ' s wife is ...
Thought : I now know the final answer
Final Answer : Jay Chou is . . . His wife is . . .
""" .strip(),
} ,
]
)
# This example involves multiple rounds of conversation.
example_train_samples . append (
[
# Round #1
{
" role " : " user " ,
" content " : f " { example_instruction } \n \n Question: 123+456=? " ,
} ,
{
" role " : " assistant " ,
" content " : """
Thought : I need to compute the result using Code Interpreter .
Action : code_interpreter
Action Input :
` ` ` py
123 + 456
` ` `
Observation : 579
Thought : I now know the final answer
Final Answer : 579
""" .strip(),
} ,
# Round #2
{
" role " : " user " ,
" content " : " Multiply the result by 2. " ,
} ,
{
" role " : " assistant " ,
" content " : """
Thought : Code Interpreter is helpful for answering this question .
Action : code_interpreter
Action Input :
` ` ` py
579 * 2
` ` `
Observation : 1158
Thought : I now know the final answer
Final Answer : 1158
""" .strip(),
} ,
# Round #3
{
" role " : " user " ,
" content " : " You are so smart, Qwen. " , # No action is needed for this question.
} ,
{
" role " : " assistant " ,
" content " : """
Thought : I now know the final answer
Final Answer : Thank you .
""" .strip(),
} ,
# Round #4
{
" role " : " user " ,
" content " : " Please re-execute the code for computing my first question again. " ,
} ,
{
" role " : " assistant " ,
" content " : """
Thought : I need to re - compute the result .
Action : code_interpreter
Action Input :
` ` ` py
123 + 456
` ` `
Observation : 579
Thought : I now know the final answer
Final Answer : 579
""" .strip(),
} ,
]
)
example_train_samples = [ format_train_sample ( x ) for x in example_train_samples ]
with open (
" example_func_call_train_samples.json " , " w "
) as fout : # data for fine-tuning
fout . write ( json . dumps ( example_train_samples , indent = 2 , ensure_ascii = False ) )
if __name__ == " __main__ " :
main ( )
