test: use phi-3 model for ChatMLX tools

libs/community/tests/integration_tests/chat_models/test_mlx_tool_calls.py

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+"""Tests ChatMLX tool calling."""
+
+from typing import Dict
+
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+from langchain_core.tools import tool
+
+from langchain_community.chat_models.mlx import ChatMLX
+from langchain_community.llms.mlx_pipeline import MLXPipeline
+
+# Use a Phi-3 model for more reliable tool-calling behavior
+MODEL_ID = "mlx-community/phi-3-mini-128k-instruct"
+
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply two integers."""
+    return a * b
+
+
+@pytest.fixture(scope="module")
+def chat() -> ChatMLX:
+    """Return ChatMLX bound with the multiply tool or skip if unavailable."""
+    try:
+        llm = MLXPipeline.from_model_id(
+            model_id=MODEL_ID, pipeline_kwargs={"max_new_tokens": 150}
+        )
+    except Exception:
+        pytest.skip("Required MLX model isn't available.", allow_module_level=True)
+    chat_model = ChatMLX(llm=llm)
+    return chat_model.bind_tools(tools=[multiply], tool_choice=True)  # type: ignore[return-value]
+
+
+def _call_tool(tool_call: Dict) -> ToolMessage:
+    result = multiply.invoke(tool_call["args"])
+    return ToolMessage(content=str(result), tool_call_id=tool_call.get("id", ""))
+
+
+def test_mlx_tool_calls_soft(chat: ChatMLX) -> None:
+    messages = [HumanMessage(content="Use the multiply tool to compute 2 * 3.")]
+    ai_msg = chat.invoke(messages)
+    tool_msg = _call_tool(ai_msg.tool_calls[0])
+    final = chat.invoke(messages + [ai_msg, tool_msg])
+    assert "6" in final.content
+
+
+def test_mlx_tool_calls_hard(chat: ChatMLX) -> None:
+    messages = [HumanMessage(content="Use the multiply tool to compute 2 * 3.")]
+    ai_msg = chat.invoke(messages)
+    assert isinstance(ai_msg, AIMessage)
+    assert ai_msg.tool_calls
+    tool_call = ai_msg.tool_calls[0]
+    assert tool_call["name"] == "multiply"
+    assert tool_call["args"] == {"a": 2, "b": 3}