diff --git a/examples/function_call_finetune_examples.py b/examples/function_call_finetune_examples.py
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+#
+# # 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": "Google Search",
+            "name_for_model": "google_search",
+            "description_for_model": "Google Search is a general search engine that can be used to access the internet,"
+            + " query encyclopedia knowledge, and stay informed about current events."
+            + " Format the arguments as a JSON object.",  # If you expect `Action Input` to be a JSON.
+            "parameters": [
+                {
+                    "name": "search_query",
+                    "description": "Search keywords or phrases",
+                    "required": True,  # Set to False if it is an optional parameter.
+                    "schema": {"type": "string"},
+                },
+                # You can add more parameters to this `parameters` list if you wish.
+            ],
+        },
+        {
+            "name_for_human": "Code Interpreter",
+            "name_for_model": "code_interpreter",
+            "description_for_model": "Code interpreter that can execute Python code."
+            + "Enclose the code within triple backticks (`)"
+            + " at the beginning and end of the code.",  # If you expect `Action Input` to be a Markdown code block.
+            "parameters": [
+                {
+                    "name": "code",
+                    "description": "Code to be executed",
+                    "required": True,
+                    "schema": {"type": "string"},
+                },
+            ],
+        },
+    ]
+    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\nQuestion: 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\nQuestion: 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()