Update FreeModelUrl.txt

FreeModelUrl.txt

@@ -1 +1,55 @@
 model=HfApiModel('https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'),
+
+Alternatively - how to use Ollama local setup
+
+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")
+