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from typing import Optional
from smolagents import HfApiModel, LiteLLMModel, TransformersModel, tool
from smolagents.agents import CodeAgent, ToolCallingAgent
# Choose which inference type to use!
available_inferences = ["hf_api", "transformers", "ollama", "litellm"]
chosen_inference = "transformers"
print(f"Chose model: '{chosen_inference}'")
if chosen_inference == "hf_api":
model = HfApiModel(model_id="meta-llama/Llama-3.3-70B-Instruct")
elif chosen_inference == "transformers":
model = TransformersModel(model_id="HuggingFaceTB/SmolLM2-1.7B-Instruct", device_map="auto", max_new_tokens=1000)
elif chosen_inference == "ollama":
model = LiteLLMModel(
model_id="ollama_chat/llama3.2",
api_base="http://localhost:11434", # replace with remote open-ai compatible server if necessary
api_key="your-api-key", # replace with API key if necessary
num_ctx=8192, # ollama default is 2048 which will often fail horribly. 8192 works for easy tasks, more is better. Check https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator to calculate how much VRAM this will need for the selected model.
)
elif chosen_inference == "litellm":
# For anthropic: change model_id below to 'anthropic/claude-3-5-sonnet-latest'
model = LiteLLMModel(
model_id="deepseek/deepseek-chat", # deepseek-chat
api_key= DEEPSEEK_API_KEY,
num_ctx=8192
)
@tool
def get_weather(location: str, celsius: Optional[bool] = False) -> str:
"""
Get weather in the next days at given location.
Secretly this tool does not care about the location, it hates the weather everywhere.
Args:
location: the location
celsius: the temperature
"""
return "The weather is UNGODLY with torrential rains and temperatures below -10°C"
agent = ToolCallingAgent(tools=[get_weather], model=model)
print("ToolCallingAgent:", agent.run("What's the weather like in Paris?"))
agent = CodeAgent(tools=[get_weather], model=model)
print("CodeAgent:", agent.run("What's the weather like in Paris?"))
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