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这将帮助您开始使用 langchain_huggingface 聊天模型。有关所有 ChatHuggingFace 功能和配置的详细文档，请访问 API 参考。有关 Hugging Face 支持的模型列表，请查看此页面。

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概述

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集成详情

类包可序列化JS 支持下载量版本
ChatHuggingFacelangchain-huggingfacebeta❌PyPI - DownloadsPyPI - Version

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模型特性

工具调用结构化输出图像输入音频输入视频输入令牌级流式传输原生异步令牌使用量对数概率
✅✅✅✅✅❌✅✅❌

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设置

要访问 Hugging Face 模型，您需要创建一个 Hugging Face 帐户，获取 API 密钥，并安装 langchain-huggingface 集成包。

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凭证

生成一个 Hugging Face 访问令牌 并将其存储为环境变量：HUGGINGFACEHUB_API_TOKEN。 
import getpass
import os

if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Enter your token: ")

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安装

类包可序列化JS 支持下载量版本
ChatHuggingFacelangchain-huggingface❌❌PyPI - DownloadsPyPI - Version

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模型特性

工具调用结构化输出图像输入音频输入视频输入令牌级流式传输原生异步令牌使用量对数概率
✅✅❌❌❌❌❌❌❌

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设置

要访问 langchain_huggingface 模型，您需要创建一个 Hugging Face 帐户，获取 API 密钥，并安装 langchain-huggingface 集成包。

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凭证

您需要将一个 Hugging Face 访问令牌 保存为环境变量：HUGGINGFACEHUB_API_TOKEN。 
import getpass
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
    "Enter your Hugging Face API key: "
)

pip install -qU  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate

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实例化

您可以通过两种不同的方式实例化 ChatHuggingFace 模型，要么从 HuggingFaceEndpoint，要么从 HuggingFacePipeline。

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HuggingFaceEndpoint

from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
    provider="auto",  # 让 Hugging Face 为您选择最佳提供商
)

chat_model = ChatHuggingFace(llm=llm)

The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: fineGrained).
Your token has been saved to /Users/isaachershenson/.cache/huggingface/token
Login successful
现在让我们利用 推理提供商 在特定的第三方提供商上运行模型 
llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    provider="hyperbolic",  # 在此处设置您的提供商
    # provider="nebius",
    # provider="together",
)

chat_model = ChatHuggingFace(llm=llm)

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HuggingFacePipeline

from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
    ),
)

chat_model = ChatHuggingFace(llm=llm)

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使用量化进行实例化

要运行模型的量化版本，您可以按如下方式指定 bitsandbytes 量化配置： 
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True,
)
并将其作为 model_kwargs 的一部分传递给 HuggingFacePipeline： 
llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
        return_full_text=False,
    ),
    model_kwargs={"quantization_config": quantization_config},
)

chat_model = ChatHuggingFace(llm=llm)

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调用

from langchain.messages import (
    HumanMessage,
    SystemMessage,
)

messages = [
    SystemMessage(content="You're a helpful assistant"),
    HumanMessage(
        content="What happens when an unstoppable force meets an immovable object?"
    ),
]

ai_msg = chat_model.invoke(messages)

print(ai_msg.content)

According to the popular phrase and hypothetical scenario, when an unstoppable force meets an immovable object, a paradoxical situation arises as both forces are seemingly contradictory. On one hand, an unstoppable force is an entity that cannot be stopped or prevented from moving forward, while on the other hand, an immovable object is something that cannot be moved or displaced from its position.

In this scenario, it is un

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API 参考

有关所有 ChatHuggingFace 功能和配置的详细文档，请访问 API 参考
将这些文档连接到 Claude、VSCode 等，通过 MCP 获取实时答案。
在 GitHub 上编辑此页面 或 提交问题。