MLX

This notebook shows how to get started using MLX LLM’s as chat models.

In particular, we will:

1. Utilize the MLXPipeline,
2. Utilize the ChatMLX class to enable any of these LLMs to interface with LangChain’s Chat Messages abstraction.
3. Demonstrate how to use an open-source LLM to power an ChatAgent pipeline

%pip install -qU  mlx-lm transformers huggingface_hub

1. Instantiate an LLM

There are three LLM options to choose from.

from langchain_community.llms.mlx_pipeline import MLXPipeline

llm = MLXPipeline.from_model_id(
    "mlx-community/quantized-gemma-2b-it",
    pipeline_kwargs={"max_tokens": 10, "temp": 0.1},
)

2. Instantiate the ChatMLX to apply chat templates

Instantiate the chat model and some messages to pass.

from langchain_community.chat_models.mlx import ChatMLX
from langchain_core.messages import HumanMessage

messages = [
    HumanMessage(
        content="What happens when an unstoppable force meets an immovable object?"
    ),
]

chat_model = ChatMLX(llm=llm)

Inspect how the chat messages are formatted for the LLM call.

chat_model._to_chat_prompt(messages)

Call the model.

res = chat_model.invoke(messages)
print(res.content)

3. Take it for a spin as an agent

Here we’ll test out gemma-2b-it as a zero-shot ReAct Agent. The example below is taken from here.

Note: To run this section, you’ll need to have a SerpAPI Token saved as an environment variable: SERPAPI_API_KEY

from langchain import hub
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import (
    ReActJsonSingleInputOutputParser,
)
from langchain.tools.render import render_text_description
from langchain_community.utilities import SerpAPIWrapper

Configure the agent with a react-json style prompt and access to a search engine and calculator.

# setup tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)

# setup ReAct style prompt
# Based on 'hwchase17/react' prompt modification, cause mlx does not support the `System` role
human_prompt = """
Answer the following questions as best you can. You have access to the following tools:

{tools}

The way you use the tools is by specifying a json blob.
Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).

The only values that should be in the "action" field are: {tool_names}

The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:

\`\`\`
{{
  "action": $TOOL_NAME,
  "action_input": $INPUT
}}
\`\`\`

ALWAYS use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action:
\`\`\`
$JSON_BLOB
\`\`\`
Observation: the result of the action
... (this Thought/Action/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin! Reminder to always use the exact characters `Final Answer` when responding.

{input}

{agent_scratchpad}

"""

prompt = human_prompt.partial(
    tools=render_text_description(tools),
    tool_names=", ".join([t.name for t in tools]),
)

# define the agent
chat_model_with_stop = chat_model.bind(stop=["\nObservation"])
agent = (
    {
        "input": lambda x: x["input"],
        "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
    }
    | prompt
    | chat_model_with_stop
    | ReActJsonSingleInputOutputParser()
)

# instantiate AgentExecutor
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

agent_executor.invoke(
    {
        "input": "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
    }
)