Improve inference choice examples (#311)

* Improve inference choice examples

* Fix style

---------

Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>

examples/agent_from_any_llm.py

@@ -0,0 +1,51 @@
+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
+    )
+
+elif chosen_inference == "litellm":
+    # For anthropic: change model_id below to 'anthropic/claude-3-5-sonnet-latest'
+    model = LiteLLMModel(model_id="gpt-4o")
+
+
+@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("ToolCallingAgent:", agent.run("What's the weather like in Paris?"))

examples/tool_calling_agent_from_any_llm.py

@@ -1,30 +0,0 @@
-from typing import Optional
-
-from smolagents import LiteLLMModel, tool
-from smolagents.agents import ToolCallingAgent
-
-
-# Choose which LLM engine to use!
-# model = HfApiModel(model_id="meta-llama/Llama-3.3-70B-Instruct")
-# model = TransformersModel(model_id="meta-llama/Llama-3.2-2B-Instruct")
-
-# For anthropic: change model_id below to 'anthropic/claude-3-5-sonnet-20240620'
-model = LiteLLMModel(model_id="gpt-4o")
-
-
-@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(agent.run("What's the weather like in Paris?"))

examples/tool_calling_agent_mcp.py

@@ -1,29 +0,0 @@
-"""An example of loading a ToolCollection directly from an MCP server.
-
-Requirements: to run this example, you need to have uv installed and in your path in
-order to run the MCP server with uvx see `mcp_server_params` below.
-
-Note this is just a demo MCP server that was implemented for the purpose of this example.
-It only provide a single tool to search amongst pubmed papers abstracts.
-
-Usage:
->>> uv run examples/tool_calling_agent_mcp.py
-"""
-
-import os
-
-from mcp import StdioServerParameters
-
-from smolagents import CodeAgent, HfApiModel, ToolCollection
-
-
-mcp_server_params = StdioServerParameters(
-    command="uvx",
-    args=["--quiet", "pubmedmcp@0.1.3"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-with ToolCollection.from_mcp(mcp_server_params) as tool_collection:
-    # print(tool_collection.tools[0](request={"term": "efficient treatment hangover"}))
-    agent = CodeAgent(tools=tool_collection.tools, model=HfApiModel(), max_steps=4)
-    agent.run("Find me one risk associated with drinking alcohol regularly on low doses for humans.")

examples/tool_calling_agent_ollama.py

@@ -1,29 +0,0 @@
-from typing import Optional
-
-from smolagents import LiteLLMModel, tool
-from smolagents.agents import ToolCallingAgent
-
-
-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
-)
-
-
-@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(agent.run("What's the weather like in Paris?"))

src/smolagents/models.py

@@ -480,7 +480,6 @@ class TransformersModel(Model):
             messages=messages,
             stop_sequences=stop_sequences,
             grammar=grammar,
-            tools_to_call_from=tools_to_call_from,
             **kwargs,
         )
 
@@ -497,9 +496,6 @@ class TransformersModel(Model):
         if max_new_tokens:
             completion_kwargs["max_new_tokens"] = max_new_tokens
 
-        if stop_sequences:
-            completion_kwargs["stopping_criteria"] = self.make_stopping_criteria(stop_sequences)
-
         if tools_to_call_from is not None:
             prompt_tensor = self.tokenizer.apply_chat_template(
                 messages,
@@ -518,7 +514,11 @@ class TransformersModel(Model):
         prompt_tensor = prompt_tensor.to(self.model.device)
         count_prompt_tokens = prompt_tensor["input_ids"].shape[1]
 
-        out = self.model.generate(**prompt_tensor, **completion_kwargs)
+        out = self.model.generate(
+            **prompt_tensor,
+            stopping_criteria=(self.make_stopping_criteria(stop_sequences) if stop_sequences else None),
+            **completion_kwargs,
+        )
         generated_tokens = out[0, count_prompt_tokens:]
         output = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
         self.last_input_token_count = count_prompt_tokens