Serie Qwen

from openai import OpenAI

client = OpenAI(
    api_key="sk-***", # 🔑 Replace it by your AiHubMix Key
    base_url="https://aihubmix.com/v1",
)

completion = client.chat.completions.create(
    model="Qwen/Qwen3-30B-A3B",
    messages=[
        {
            "role": "user",
            "content": "Explain the Occam's Razor concept and provide everyday examples of it"
        }
    ],
    stream=True
)

for chunk in completion:
    if hasattr(chunk.choices, '__len__') and len(chunk.choices) > 0:
        if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].delta.content, end="")

from openai import OpenAI

client = OpenAI(
    api_key="sk-***", # 🔑 Replace it by your AiHubMix Key
    base_url="https://aihubmix.com/v1",
)

# Define Tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather of a specified location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City name, e.g., Beijing, Shanghai, etc."
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Create chat completion request with tool definitions
completion = client.chat.completions.create(
    model="Qwen/Qwen3-30B-A3B",  # Supported by both 2.5 and 3, not supported by QwQ
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Beijing today?"
        }
    ],
    tools=tools,
    tool_choice="auto",  # Let the model decide whether to use a tool
    stream=True
)

# Dictionary for collecting tool call info
tool_calls = {}

# Handle streaming responses
for chunk in completion:
    if not hasattr(chunk.choices, '__len__') or len(chunk.choices) == 0:
        continue
        
    delta = chunk.choices[0].delta
    
    # Handle textual content
    if hasattr(delta, 'content') and delta.content:
        print(delta.content, end="")
    
    # Handle tool calls
    if hasattr(delta, 'tool_calls') and delta.tool_calls:
        for tool_call in delta.tool_calls:
            if not hasattr(tool_call, 'index'):
                continue
                
            idx = tool_call.index
            if idx not in tool_calls:
                tool_calls[idx] = {"name": "", "arguments": ""}
                
            if hasattr(tool_call, 'function'):
                if hasattr(tool_call.function, 'name') and tool_call.function.name:
                    tool_calls[idx]["name"] = tool_call.function.name
                if hasattr(tool_call.function, 'arguments') and tool_call.function.arguments:
                    tool_calls[idx]["arguments"] += tool_call.function.arguments

# After completion, print collected tool call info
for idx, info in tool_calls.items():
    if info["name"]:
        print(f"\nTool call: {info['name']}")
    if info["arguments"]:
        print(f"Arguments: {info['arguments']}")

from qwen_agent.agents import Assistant
import os

# Define LLM
llm_cfg = {
    'model': 'Qwen/Qwen3-30B-A3B',

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'https://aihubmix.com/v1',
    'api_key': os.getenv('AIHUBMIX_API_KEY'),

    # 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 Tools
tools = [
    {'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 Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

from openai import OpenAI
import base64
import os

client = OpenAI(
    api_key="sk-***", # 🔑 Replace it by your AiHubMix Key
    base_url="https://aihubmix.com/v1",
)

image_path = "yourpath/file.png"

def encode_image(image_path):
    if not os.path.exists(image_path):
        raise FileNotFoundError(f"Image file does not exist: {image_path}")
    
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# Get the base64 encoding of the image
base64_image = encode_image(image_path)

completion = client.chat.completions.create(
    model="qwen2.5-vl-72b-instruct", #qwen2.5-vl-72b-instruct OR Qwen/QVQ-72B-Preview
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Please describe this image in detail"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{base64_image}"
                    }
                }
            ]
        }
    ],
    stream=True
)

for chunk in completion:
    if hasattr(chunk.choices, '__len__') and len(chunk.choices) > 0:
        if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].delta.content, end="")

from openai import OpenAI

client = OpenAI(
    api_key="sk-***", # 🔑 Replace it by your AiHubMix Key
    base_url="https://aihubmix.com/v1",
)

completion = client.chat.completions.create(
    model="Qwen/QwQ-32B",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is the meta rule that dominates the universe?"}
            ]
        }
    ],
    stream=True
)

for chunk in completion:
    if hasattr(chunk.choices, '__len__') and len(chunk.choices) > 0:
        if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].delta.content, end="")