MLX

此筆記本示範如何開始使用 MLX LLM 作為聊天模型。

特別是，我們將會

使用 MLXPipeline，
使用 ChatMLX 類別來啟用任何這些 LLM，以與 LangChain 的聊天訊息抽象介面互動。
示範如何使用開放原始碼 LLM 來驅動 ChatAgent 管線

%pip install --upgrade --quiet  mlx-lm transformers huggingface_hub

1. 實例化 LLM

有三個 LLM 選項可供選擇。

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},
)

API 參考：MLXPipeline

2. 實例化 `ChatMLX` 以套用聊天範本

實例化聊天模型和一些要傳遞的訊息。

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)

API 參考：ChatMLX | HumanMessage

檢查聊天訊息如何為 LLM 呼叫格式化。

chat_model._to_chat_prompt(messages)

呼叫模型。

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

3. 將其作為 Agent 試用！

在這裡，我們將測試 gemma-2b-it 作為 zero-shot ReAct Agent。以下範例取自此處。

注意：若要執行此章節，您需要將 SerpAPI Token 儲存為環境變數：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

設定具有 react-json 樣式提示的 Agent，並存取搜尋引擎和計算機。

# 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?"
    }
)

聊天模型概念指南
聊天模型操作指南

1. 實例化 LLM​

2. 實例化 ChatMLX 以套用聊天範本​

3. 將其作為 Agent 試用！​

相關連結​

此頁面是否對您有幫助？

1. 實例化 LLM

2. 實例化 `ChatMLX` 以套用聊天範本

3. 將其作為 Agent 試用！

相關連結