| import os | |
| from smolagents import InferenceClientModel, LiteLLMModel, ToolCallingAgent, MCPClient, CodeAgent | |
| from huggingface_hub import InferenceClient | |
| # Create the Nebius-backed HuggingFace InferenceClient | |
| # hf_client = InferenceClient( | |
| # provider="nebius", | |
| # api_key=os.getenv("NEBIUS_API_KEY") | |
| # ) | |
| # Wrap it for smolagents agentic interface | |
| model = InferenceClientModel( | |
| model_id="Qwen/Qwen2.5-VL-72B-Instruct", | |
| provider="nebius", | |
| api_key=os.getenv("NEBIUS_API_KEY") | |
| ) | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": "Tell me an easy ice cream recipe."}, | |
| ] | |
| # completion = client.chat.completions.create( | |
| # model="Qwen/Qwen2.5-VL-72B-Instruct", | |
| # messages=messages, | |
| # max_tokens=500 | |
| # ) | |
| # print(completion.choices[0].message.content) | |
| # Example: No tools, just agentic reasoning (tool use can be added if desired) | |
| agent = ToolCallingAgent(model=model, tools=[]) | |
| response = agent.run(messages[-1]['content'], max_steps=10) | |
| print(response) |