import os os.environ['SWIFT_DEBUG'] = '1' system = 'You are a helpful assistant.' tools = [{ 'type': 'function', 'function': { 'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': { 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA' }, 'unit': { 'type': 'string', 'enum': ['celsius', 'fahrenheit'] } }, 'required': ['location'] } } }, { 'name_for_model': 'tool2', 'name_for_human': '工具2', 'description': 'Tool2的描述', }] glm4_tools = [{ 'type': 'function', 'function': { 'name': 'realtime_aqi', 'description': '天气预报。获取实时空气质量。当前空气质量，PM2.5，PM10信息', 'parameters': { 'type': 'object', 'properties': { 'city': { 'description': '城市名' } }, 'required': ['city'] } } }] glm4_tool_messasges = [ { 'role': 'tool', 'content': '{"city": "北京", "aqi": "10", "unit": "celsius"}' }, { 'role': 'tool', 'content': '{"city": "上海", "aqi": "72", "unit": "fahrenheit"}' }, ] glm4_query = '北京和上海今天的天气情况' def _infer(engine, num_tools: int = 1, agent_tools=None, tool_messages=None, query=None): if agent_tools is None: agent_tools = tools if tool_messages is None: tool_messages = [] for _ in range(num_tools): tool_messages.append({ 'role': 'tool', 'content': '{"temperature": 32, "condition": "Sunny", "humidity": 50}' }) stop = [engine.default_template.agent_template.keyword.observation] query = query or "How's the weather in Beijing today?" infer_request = InferRequest([{'role': 'user', 'content': query}], tools=agent_tools) request_config = RequestConfig(max_tokens=512, stop=stop, temperature=0) resp_list = engine.infer([infer_request], request_config=request_config) response = resp_list[0].choices[0].message.content toolcall = resp_list[0].choices[0].message.tool_calls[0].function print(f'response: {response}') print(f'toolcall: {toolcall}') assert toolcall is not None infer_request.messages.append({'role': 'assistant', 'content': response}) infer_request.messages += tool_messages resp_list = engine.infer([infer_request], request_config=request_config) response2 = resp_list[0].choices[0].message.content print(f'response2: {response2}') infer_request.messages.append({'role': 'assistant', 'content': response2}) return infer_request.messages def test_react_en(): agent_template = agent_templates['react_en']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 1144 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template messages = _infer(engine) assert messages[-1]['content'] == ( 'Thought: The current temperature in Beijing is 32 degrees Celsius, and the condition is sunny ' 'with a humidity of 50%.\nFinal Answer: The current temperature in Beijing is 32 degrees Celsius,' ' and the condition is sunny with a humidity of 50%.') template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0] data = dataset[6] data['messages'].insert(1, data['messages'][1]) data['messages'].insert(3, data['messages'][3]) template.template_backend = 'swift' encoded = template.encode(data) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') def test_react_zh(): agent_template = agent_templates['react_zh']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 712 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template _infer(engine) def test_qwen_en(): agent_template = agent_templates['qwen_en']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 879 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template messages = _infer(engine) assert messages[-1]['content'] == ( '✿RETURN✿: Today in Beijing, the temperature is 32°C with sunny conditions and the humidity ' 'is at 50%. Enjoy the nice weather!') template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0] data = dataset[6] data['messages'].insert(1, data['messages'][1]) data['messages'].insert(3, data['messages'][3]) template.template_backend = 'swift' encoded = template.encode(data) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') def test_qwen_zh(): agent_template = agent_templates['qwen_zh']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 577 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template _infer(engine) def test_qwen_en_parallel(): agent_template = agent_templates['qwen_en_parallel']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 1012 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template messages = _infer(engine, num_tools=2) assert messages[-1]['content'] == ( '✿RETURN✿: Today in Beijing, the temperature is 32 degrees Celsius with sunny conditions ' 'and the humidity is at 50%. Enjoy the nice weather!') template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0] data = dataset[6] data['messages'].insert(1, data['messages'][1]) data['messages'].insert(3, data['messages'][3]) template.template_backend = 'swift' encoded = template.encode(data) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') def test_qwen_zh_parallel(): agent_template = agent_templates['qwen_zh_parallel']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 688 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template _infer(engine, num_tools=2) def test_hermes(): agent_template = agent_templates['hermes']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 875 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template messages = _infer(engine, num_tools=2) template.template_backend = 'jinja' messages2 = _infer(engine, num_tools=2) assert messages[-1]['content'] == messages2[-1]['content'] == ( 'Today in Beijing, the temperature is 32 degrees Celsius with sunny conditions ' 'and the humidity is at 50%. Enjoy the nice weather!') template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0] data = dataset[6] data['messages'].insert(1, data['messages'][1]) data['messages'].insert(3, data['messages'][3]) template.template_backend = 'swift' encoded = template.encode(data) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') template.template_backend = 'jinja' encoded2 = template.encode(data) print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}') print(f'labels: {template.safe_decode(encoded2["labels"])}') assert encoded['input_ids'] == encoded2['input_ids'][:-1] def test_toolbench(): agent_template = agent_templates['toolbench']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 1833 engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') template = engine.default_template template.agent_template = agent_template _infer(engine) def test_glm4(): agent_template = agent_templates['glm4']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 846 engine = PtEngine('ZhipuAI/glm-4-9b-chat') template = engine.default_template template.agent_template = agent_template _infer(engine, agent_tools=glm4_tools, tool_messages=glm4_tool_messasges, query=glm4_query) def test_glm4_0414(): agent_template = agent_templates['glm4_0414']() new_system = agent_template._format_tools(tools, system) assert len(new_system) == 769 engine = PtEngine('ZhipuAI/GLM-4-9B-0414') template = engine.default_template template.agent_template = agent_template messages = _infer(engine, agent_tools=glm4_tools, tool_messages=glm4_tool_messasges, query=glm4_query) assert messages[-1]['content'] == '根据天气预报工具，北京今天的空气质量指数为10，属于良好水平；上海今天的空气质量指数为72，属于轻度污染水平。' template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0] data = dataset[6] data['messages'].insert(1, data['messages'][1]) data['messages'].insert(3, data['messages'][3]) template.template_backend = 'swift' encoded = template.encode(data) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') def test_llama3(): agent_template = agent_templates['llama3']() engine = PtEngine('LLM-Research/Llama-3.2-3B-Instruct') template = engine.default_template template.agent_template = agent_template messages = _infer(engine) template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0] data = dataset[6] data['messages'].insert(1, data['messages'][1]) data['messages'].insert(3, data['messages'][3]) template.template_backend = 'swift' encoded = template.encode(data) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') def test_llama4(): agent_template = agent_templates['llama4']() engine = PtEngine('LLM-Research/Llama-4-Scout-17B-16E-Instruct') template = engine.default_template template.agent_template = agent_template messages = _infer(engine) template.set_mode('train') encoded = template.encode({'messages': messages}) print(f'input_ids: {template.safe_decode(encoded["input_ids"])}') print(f'labels: {template.safe_decode(encoded["labels"])}') if __name__ == '__main__': from swift.plugin import agent_templates from swift.llm import PtEngine, InferRequest, RequestConfig, load_dataset # test_react_en() # test_react_zh() # test_qwen_en() # test_qwen_zh() # test_qwen_en_parallel() # test_qwen_zh_parallel() test_hermes() # test_toolbench() # test_glm4() # test_glm4_0414() # test_llama3() # test_llama4()