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，请前往 API 参考。有关 Hugging Face 支持的模型列表，请查看 此页面.

概述

集成详情

模型特性

设置

要访问 Hugging Face 模型，您需要创建一个 Hugging Face 账户，获取一个 API 密钥，并安装 langchain-huggingface 集成包。

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.

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

安装

模型特性

设置

要访问 langchain_huggingface 模型，您需要创建一个 Hugging Face 账户，获取一个 API 密钥，并安装 langchain-huggingface 集成包。

凭证

您需要拥有一个 Hugging Face 访问令牌 并将其保存为环境变量： HUGGINGFACEHUB_API_TOKEN.

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

实例化

您可以实例化一个 ChatHuggingFace 模型，有两种不同的方式，可以从 HuggingFaceEndpoint 或从 HuggingFacePipeline.

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",  # let Hugging Face choose the best provider for you
)

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 推理提供者 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",  # set your provider here
    # provider="nebius",
    # provider="together",
)

chat_model = ChatHuggingFace(llm=llm)

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)

config.json:   0%|          | 0.00/638 [00:00<?, ?B/s]

model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]

Downloading shards:   0%|          | 0/8 [00:00<?, ?it/s]

model-00001-of-00008.safetensors:   0%|          | 0.00/1.89G [00:00<?, ?B/s]

model-00002-of-00008.safetensors:   0%|          | 0.00/1.95G [00:00<?, ?B/s]

model-00003-of-00008.safetensors:   0%|          | 0.00/1.98G [00:00<?, ?B/s]

model-00004-of-00008.safetensors:   0%|          | 0.00/1.95G [00:00<?, ?B/s]

model-00005-of-00008.safetensors:   0%|          | 0.00/1.98G [00:00<?, ?B/s]

model-00006-of-00008.safetensors:   0%|          | 0.00/1.95G [00:00<?, ?B/s]

model-00007-of-00008.safetensors:   0%|          | 0.00/1.98G [00:00<?, ?B/s]

model-00008-of-00008.safetensors:   0%|          | 0.00/816M [00:00<?, ?B/s]

Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]

generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]

实例化时使用量化

要运行模型的量化版本，您可以指定 bitsandbytes 如下所示的量化配置：

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,
)

并将其传递给 HuggingFacePipeline 作为其 model_kwargs:

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)

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

API 参考

有关所有 ChatHuggingFace 功能和配置的详细文档，请前往 API 参考