Qwen3：思深，行速

from modelscope import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-30B-A3B"
# load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(    model_name,    torch_dtype="auto",    device_map="auto")
# prepare the model inputprompt = "Give me a short introduction to large language model."messages = [    {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(    messages,    tokenize=False,    add_generation_prompt=True,    enable_thinking=True # Switch between thinking and non-thinking modes. Default is True.)model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completiongenerated_ids = model.generate(    **model_inputs,    max_new_tokens=32768)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
# parsing thinking contenttry:    # rindex finding 151668 (</think>)    index = len(output_ids) - output_ids[::-1].index(151668)except ValueError:    index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)print("content:", content)

text = tokenizer.apply_chat_template(    messages,    tokenize=False,    add_generation_prompt=True,    enable_thinking=False  # True is the default value for enable_thinking.)

    python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B --reasoning-parser qwen3

    vllm serve Qwen/Qwen3-30B-A3B --enable-reasoning --reasoning-parser deepseek_r1

from transformers import AutoModelForCausalLM, AutoTokenizer
classQwenChatbot:    def __init__(self, model_name="Qwen3-30B-A3B/Qwen3-30B-A3B"):        self.tokenizer = AutoTokenizer.from_pretrained(model_name)        self.model = AutoModelForCausalLM.from_pretrained(model_name)        self.history = []
    def generate_response(self, user_input):        messages = self.history + [{"role": "user", "content": user_input}]
        text = self.tokenizer.apply_chat_template(            messages,            tokenize=False,            add_generation_prompt=True        )
        inputs = self.tokenizer(text, return_tensors="pt")        response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()        response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
        # Update history        self.history.append({"role": "user", "content": user_input})        self.history.append({"role": "assistant", "content": response})
        return response
# Example Usageif __name__ == "__main__":    chatbot = QwenChatbot()
    # First input (without /think or /no_think tags, thinking mode is enabled by default)    user_input_1 = "How many r's in strawberries?"    print(f"User: {user_input_1}")    response_1 = chatbot.generate_response(user_input_1)    print(f"Bot: {response_1}")    print("----------------------")
    # Second input with /no_think    user_input_2 = "Then, how many r's in blueberries? /no_think"    print(f"User: {user_input_2}")    response_2 = chatbot.generate_response(user_input_2)    print(f"Bot: {response_2}")     print("----------------------")
    # Third input with /think    user_input_3 = "Really? /think"    print(f"User: {user_input_3}")    response_3 = chatbot.generate_response(user_input_3)    print(f"Bot: {response_3}")

from qwen_agent.agents import Assistant
# Define LLMllm_cfg = {    'model': 'Qwen3-30B-A3B',
    # Use the endpoint provided by Alibaba Model Studio:    # 'model_type': 'qwen_dashscope',    # 'api_key': os.getenv('DASHSCOPE_API_KEY'),
    # Use a custom endpoint compatible with OpenAI API:    'model_server': 'http://localhost:8000/v1',  # api_base    'api_key': 'EMPTY',
    # Other parameters:    # 'generate_cfg': {    #         # Add: When the response content is `<think>this is the thought</think>this is the answer;    #         # Do not add: When the response has been separated by reasoning_content and content.    #         'thought_in_content': True,    #     },}
# Define Toolstools = [    {'mcpServers': {  # You can specify the MCP configuration file            'time': {                'command': 'uvx',                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']            },            "fetch": {                "command": "uvx",                "args": ["mcp-server-fetch"]            }        }    },  'code_interpreter',  # Built-in tools]
# Define Agentbot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generationmessages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]for responses in bot.run(messages=messages):    passprint(responses)