| model=HfApiModel('https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'), | |
| Alternatively - how to use Ollama local setup | |
| https://discord.com/channels/879548962464493619/1349138711643816006/1349138711643816006 | |
| I just want to share my solution if this could be useful. | |
| 1) I ran locally qwen2.5 model. how? | |
| 2) Download ollama from https://ollama.com/download | |
| 3) In terminal, once Ollama is installed run command: ollama pull qwen2.5:7b (some useful info: https://ollama.com/library/qwen2.5) | |
| 4) pip install smolagents, ollama | |
| 5) see script attached. The OllamaModel class was copied from other conversation in our community | |
| from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, HfApiModel, Tool, tool, VisitWebpageTool | |
| import ollama | |
| @tool | |
| def suggest_menu(occasion: str) -> str: | |
| """ | |
| Suggests a menu based on the occasion. | |
| Args: | |
| occasion: The type of occasion for the party. | |
| """ | |
| if occasion == "casual": | |
| return "Pizza, snacks, and drinks." | |
| elif occasion == "formal": | |
| return "3-course dinner with wine and dessert." | |
| elif occasion == "superhero": | |
| return "Buffet with high-energy and healthy food." | |
| else: | |
| return "Custom menu for the butler." | |
| class OllamaModel: | |
| def __init__(self, model_name): | |
| self.model_name = model_name | |
| def __call__(self, prompt, stop_sequences=["Task"]) -> str: | |
| # Convert the list of prompts to a single string | |
| prompt_text = "" | |
| for item in prompt: | |
| if item['role'] == 'system': | |
| for content in item['content']: | |
| if content['type'] == 'text': | |
| prompt_text += content['text'] | |
| elif item['role'] == 'user': | |
| for content in item['content']: | |
| if content['type'] == 'text': | |
| prompt_text += content['text'] | |
| # Use Ollama's generate or chat API to handle prompts | |
| response = ollama.chat(model=self.model_name, messages=[{"role": "user", "content": prompt_text}], stream=False) | |
| return response.message | |
| # Initialize the agent with OllamaModel | |
| ollama_model = OllamaModel(model_name="qwen2.5") | |