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ChatHuggingFace

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,请参阅 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

集成详情

类别本地可序列化JS 支持包下载量最新包版本
ChatHuggingFacelangchain-huggingface测试版PyPI - DownloadsPyPI - Version

模型特性

工具调用结构化输出JSON 模式图片输入音频输入视频输入逐令牌流式传输原生异步令牌使用量对数概率

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

import getpass
import os

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

安装

类别本地可序列化JS 支持包下载量最新包版本
ChatHuggingFacelangchain_huggingfacePyPI - DownloadsPyPI - Version

模型特性

工具调用结构化输出JSON 模式图片输入音频输入视频输入逐令牌流式传输原生异步令牌使用量对数概率

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

import getpass
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
"Enter your Hugging Face API key: "
)
%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate

[notice] A new release of pip is available: 24.0 -> 24.1.2
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

实例化

您可以通过两种不同的方式实例化一个 ChatHuggingFace 模型:通过 HuggingFaceEndpoint 或通过 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", # 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)
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使用量化实例化

要运行模型的量化版本,您可以按如下方式指定一个 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,
)

并将其作为其 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)

调用

from langchain_core.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,请参阅 API 参考:https://python.langchain.ac.cn/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

API 参考

有关所有 ChatHuggingFace 功能和配置的详细文档,请参阅 API 参考:https://python.langchain.ac.cn/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html