MLX model support

docs/source/en/guided_tour.md

@@ -28,10 +28,11 @@ To initialize a minimal agent, you need at least these two arguments:
     - [`HfApiModel`] leverages a `huggingface_hub.InferenceClient` under the hood and supports all Inference Providers on the Hub.
     - [`LiteLLMModel`] similarly lets you call 100+ different models and providers through [LiteLLM](https://docs.litellm.ai/)!
     - [`AzureOpenAIServerModel`] allows you to use OpenAI models deployed in [Azure](https://azure.microsoft.com/en-us/products/ai-services/openai-service).
+    - [`MLXModel`] creates a [mlx-lm](https://pypi.org/project/mlx-lm/) pipeline to run inference on your local machine.
 
 - `tools`, a list of `Tools` that the agent can use to solve the task. It can be an empty list. You can also add the default toolbox on top of your `tools` list by defining the optional argument `add_base_tools=True`.
 
-Once you have these two arguments, `tools` and `model`,  you can create an agent and run it. You can use any LLM you'd like, either through [Inference Providers](https://huggingface.co/blog/inference-providers), [transformers](https://github.com/huggingface/transformers/), [ollama](https://ollama.com/), [LiteLLM](https://www.litellm.ai/), or [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service).
+Once you have these two arguments, `tools` and `model`,  you can create an agent and run it. You can use any LLM you'd like, either through [Inference Providers](https://huggingface.co/blog/inference-providers), [transformers](https://github.com/huggingface/transformers/), [ollama](https://ollama.com/), [LiteLLM](https://www.litellm.ai/), [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service), or [mlx-lm](https://pypi.org/project/mlx-lm/).
 
 <hfoptions id="Pick a LLM">
 <hfoption id="HF Inference API">
@@ -148,6 +149,19 @@ agent.run(
 )
 ```
 
+</hfoption>
+<hfoption id="mlx-lm">
+
+```python
+# !pip install smolagents[mlx-lm]
+from smolagents import CodeAgent, MLXModel
+
+mlx_model = MLXModel("mlx-community/Qwen2.5-Coder-32B-Instruct-4bit")
+agent = CodeAgent(model=mlx_model, tools=[], add_base_tools=True)
+
+agent.run("Could you give me the 118th number in the Fibonacci sequence?")
+```
+
 </hfoption>
 </hfoptions>
 

docs/source/en/reference/models.md

@@ -147,4 +147,23 @@ model = AzureOpenAIServerModel(
 )
 ```
 
-[[autodoc]] AzureOpenAIServerModel
+[[autodoc]] AzureOpenAIServerModel
+
+### MLXModel
+
+
+```python
+from smolagents import MLXModel
+
+model = MLXModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
+
+print(model([{"role": "user", "content": "Ok!"}], stop_sequences=["great"]))
+```
+```text
+>>> What a
+```
+
+> [!TIP]
+> You must have `mlx-lm` installed on your machine. Please run `pip install smolagents[mlx-lm]` if it's not the case.
+
+[[autodoc]] MLXModel

pyproject.toml

@@ -46,6 +46,9 @@ mcp = [
   "mcpadapt>=0.0.6",
   "mcp",
 ]
+mlx-lm = [
+  "mlx-lm"
+]
 openai = [
   "openai>=1.58.1"
 ]

src/smolagents/models.py

@@ -18,6 +18,7 @@
 import logging
 import os
 import random
+import uuid
 from copy import deepcopy
 from dataclasses import asdict, dataclass
 from enum import Enum
@@ -415,6 +416,128 @@ def __call__(
         return message
 
 
+class MLXModel(Model):
+    """A class to interact with models loaded using MLX on Apple silicon.
+
+    > [!TIP]
+    > You must have `mlx-lm` installed on your machine. Please run `pip install smolagents[mlx-lm]` if it's not the case.
+
+    Parameters:
+        model_id (str):
+            The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
+        tool_name_key (str):
+            The key, which can usually be found in the model's chat template, for retrieving a tool name.
+        tool_arguments_key (str):
+            The key, which can usually be found in the model's chat template, for retrieving tool arguments.
+        trust_remote_code (bool):
+            Some models on the Hub require running remote code: for this model, you would have to set this flag to True.
+        kwargs (dict, *optional*):
+            Any additional keyword arguments that you want to use in model.generate(), for instance `max_tokens`.
+
+    Example:
+    ```python
+    >>> engine = MLXModel(
+    ...     model_id="mlx-community/Qwen2.5-Coder-32B-Instruct-4bit",
+    ...     max_tokens=10000,
+    ... )
+    >>> messages = [
+    ...     {
+    ...         "role": "user", 
+    ...         "content": [
+    ...             {"type": "text", "text": "Explain quantum mechanics in simple terms."}
+    ...         ]
+    ...     }
+    ... ]
+    >>> response = engine(messages, stop_sequences=["END"])
+    >>> print(response)
+    "Quantum mechanics is the branch of physics that studies..."
+    ```
+    """
+
+    def __init__(
+        self,
+        model_id: str,
+        tool_name_key: str = "name",
+        tool_arguments_key: str = "arguments",
+        trust_remote_code: bool = False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if not _is_package_available("mlx_lm"):
+            raise ModuleNotFoundError(
+                "Please install 'mlx-lm' extra to use 'MLXModel': `pip install 'smolagents[mlx-lm]'`"
+            )
+        import mlx_lm
+
+        self.model_id = model_id
+        self.model, self.tokenizer = mlx_lm.load(model_id, tokenizer_config={"trust_remote_code": trust_remote_code})
+        self.stream_generate = mlx_lm.stream_generate
+        self.tool_name_key = tool_name_key
+        self.tool_arguments_key = tool_arguments_key
+
+    def _to_message(self, text, tools_to_call_from):
+        if tools_to_call_from:
+            # tmp solution for extracting tool JSON without assuming a specific model output format
+            maybe_json = "{" + text.split("{", 1)[-1][::-1].split("}", 1)[-1][::-1] + "}"
+            parsed_text = json.loads(maybe_json)
+            tool_name = parsed_text.get(self.tool_name_key, None)
+            tool_arguments = parsed_text.get(self.tool_arguments_key, None)
+            if tool_name:
+                return ChatMessage(
+                    role="assistant",
+                    content="",
+                    tool_calls=[
+                        ChatMessageToolCall(
+                            id=uuid.uuid4(),
+                            type="function",
+                            function=ChatMessageToolCallDefinition(name=tool_name, arguments=tool_arguments),
+                        )
+                    ],
+                )
+        return ChatMessage(role="assistant", content=text)
+
+    def __call__(
+        self,
+        messages: List[Dict[str, str]],
+        stop_sequences: Optional[List[str]] = None,
+        grammar: Optional[str] = None,
+        tools_to_call_from: Optional[List[Tool]] = None,
+        **kwargs,
+    ) -> ChatMessage:
+        completion_kwargs = self._prepare_completion_kwargs(
+            flatten_messages_as_text=True,  # mlx-lm doesn't support vision models
+            messages=messages,
+            stop_sequences=stop_sequences,
+            grammar=grammar,
+            tools_to_call_from=tools_to_call_from,
+            **kwargs,
+        )
+        messages = completion_kwargs.pop("messages")
+        prepared_stop_sequences = completion_kwargs.pop("stop", [])
+        tools = completion_kwargs.pop("tools", None)
+        completion_kwargs.pop("tool_choice", None)
+
+        prompt_ids = self.tokenizer.apply_chat_template(
+            messages,
+            tools=tools,
+            add_generation_prompt=True,
+        )
+
+        self.last_input_token_count = len(prompt_ids)
+        self.last_output_token_count = 0
+        text = ""
+
+        for _ in self.stream_generate(self.model, self.tokenizer, prompt=prompt_ids, **completion_kwargs):
+            self.last_output_token_count += 1
+            text += _.text
+            for stop_sequence in prepared_stop_sequences:
+                if text.strip().endswith(stop_sequence):
+                    text = text[: -len(stop_sequence)]
+                    return self._to_message(text, tools_to_call_from)
+
+        return self._to_message(text, tools_to_call_from)
+
+
 class TransformersModel(Model):
     """A class that uses Hugging Face's Transformers library for language model interaction.
 
@@ -837,6 +960,7 @@ def __init__(
     "tool_role_conversions",
     "get_clean_message_list",
     "Model",
+    "MLXModel",
     "TransformersModel",
     "HfApiModel",
     "LiteLLMModel",

tests/test_models.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 import json
 import os
+import sys
 import unittest
 from pathlib import Path
 from typing import Optional
@@ -22,7 +23,7 @@
 import pytest
 from transformers.testing_utils import get_tests_dir
 
-from smolagents import ChatMessage, HfApiModel, TransformersModel, models, tool
+from smolagents import ChatMessage, HfApiModel, MLXModel, TransformersModel, models, tool
 from smolagents.models import MessageRole, get_clean_message_list, parse_json_if_needed
 
 
@@ -61,6 +62,13 @@ def test_get_hfapi_message_no_tool_external_provider(self):
         messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
         model(messages, stop_sequences=["great"])
 
+    @unittest.skipUnless(sys.platform.startswith("darwin"), "requires macOS")
+    def test_get_mlx_message_no_tool(self):
+        model = MLXModel(model_id="HuggingFaceTB/SmolLM2-135M-Instruct", max_tokens=10)
+        messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
+        output = model(messages, stop_sequences=["great"]).content
+        assert output.startswith("Hello")
+
     def test_transformers_message_no_tool(self):
         model = TransformersModel(
             model_id="HuggingFaceTB/SmolLM2-135M-Instruct",